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by Cognitionand·hosted onFeatured in ForbesReferenced by Harvard UniversityReferenced by Columbia UniversityReferenced by Olympic College40+ Academic CitationsReferenced in GitHub Blog#1 Most Liked Dataset on Hugging Face176k GitHub Stars#33 Most Starred Repo in the WorldUsed by Thousands DailyGitHub Staff PickThe Only 100% Free & Open Source Prompt LibraryThe First-Ever Prompts Library · Released on Dec 5, 2022
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Greg Brockman
President & Co-Founder at OpenAI · Dec 12, 2022
“Love the community explorations of ChatGPT, from capabilities (https://github.com/f/prompts.chat) to limitations (...). No substitute for the collective power of the internet when it comes to plumbing the uncharted depths of a new deep learning model.”
Wojciech Zaremba
Co-Founder at OpenAI · Dec 10, 2022
“I love it! https://github.com/f/prompts.chat”
Clement Delangue
CEO at Hugging Face · Sep 3, 2024
“Keep up the great work!”
Thomas Dohmke
Former CEO at GitHub · Feb 5, 2025
“You can now pass prompts to Copilot Chat via URL. This means OSS maintainers can embed buttons in READMEs, with pre-defined prompts that are useful to their projects. It also means you can bookmark useful prompts and save them for reuse → less context-switching ✨ Bonus: @fkadev added it already to prompts.chat 🚀”
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A complete 30-second Seedance-ready video prompt for a poetic magical-realism beach at dawn: a woman in her 40s listens to a seashell as thousands of corked glass bottles wash ashore, each holding a tiny glowing light. Uncorking one releases a child's laugh; the beach becomes a field of warm lights; transparent waves show dissolving family memories; she returns the shell and the whole ocean answers with a golden glow. Soft camera, tender audio design, no dialogue or on-screen text.
[Global Setting] 30-second poetic magical realism sequence, 16:9, 4K, photoreal, emotional atmosphere. A quiet, wide sandy beach at dawn. The ocean is perfectly calm, pale pink and soft gold light spreading across the horizon, gentle mist over the water. No dialogue, no text on screen. [Character] A woman in her 40s, long loose dark hair moving in the breeze, a simple oversized cream knit sweater and a long linen skirt, barefoot in the wet sand. Calm, melancholic, gentle eyes. She holds an old pale seashell, worn smooth by time. [0–2s] Medium shot. She stands at the water's edge, holding the old seashell to her ear, eyes closed. She hears only silence. Soft waves touch her feet. [2–8s] All around her, thousands of small glass bottles wash ashore with the gentle waves, each sealed with a cork, each containing a tiny floating glowing light. She kneels, picks one up, uncorks it. A child's laugh echoes softly across the water. Her eyes fill with tears, a small smile. [8–15s] The camera moves low along the shoreline at sand level. One by one, the bottles begin to glow brighter, warm white and gold, until the entire beach becomes a field of soft lights stretching into the distance. [15–23s] The waves turn transparent like glass. Beneath the surface, fleeting images appear and dissolve like reflections: an old family house, a birthday cake with candles, two people talking at a kitchen table, a hand holding another hand, faces of people once loved. They drift by gently and fade. [23–30s] She walks into the shallow water and gently places the seashell back into the sea. A moment of stillness. Then the ocean answers: a warm golden glow spreads from where the shell sank, across the whole surface, all the way to the horizon, meeting the rising sun. She stands bathed in golden light, peaceful. Hold. [Camera] Slow, gentle, floating movements. Medium shots on her, low tracking along the shoreline, a wide shot for the golden ocean. No cuts, no shaky cam. [Audio] Soft lapping waves, gentle breeze, silence in the shell, the clink of glass bottles on sand, a child's distant laugh echoing, faint overlapping whispers and laughter from the memories beneath the waves, a delicate piano and soft strings rising, then a warm resonant hum as the ocean glows. [Consistency] Same woman, same seashell, same beach. Dawn pink light turning golden. Tender, emotional, never dark.
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A compact atmospheric image prompt for a rainy Japanese alley at night: a faceless female figure in a cropped jacket, pleated mini-skirt and patent over-the-knee boots stands under a clear umbrella that catches magenta light, beneath a half-dead neon kanji sign. Low camera, soft grain, mood of waiting for a reply that never comes. Optional "Cel Shaded Art," prefix for a sharper comic look.
Rainy alley, one neon kanji sign half-out, female Japanese figure wearing cropped jacket tank top, pleated mini-skirt, patent high-heeled over-the-knee boots, under a clear umbrella that catches magenta light. Camera low. No face shown. Mood: waiting for a reply that isn't coming. Soft grain, no film logo. Optional prefix for extra punch: Cel Shaded Art,
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A complete GPT Image–ready JSON prompt for a vibrant Japanese anime summer illustration: an original stylish girl in a coral-orange asymmetric tunic and cherry-red bucket hat walks the wet shoreline of a turquoise tropical beach. It locks warm clothing colors against cool ocean blues, a 9:16 full-body fashion pose, golden sunbeams, seagulls and a strong negative prompt so the outfit never blends into the water.
1{2 "aspect_ratio": "9:16",3 "style": "Premium Japanese anime illustration, vibrant summer movie aesthetic, expressive hand-drawn character art, high-contrast cel shading, brilliant tropical colors",...+3 more lines
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A compact Midjourney prompt for a striking vertical anime-style poster: a girl with a blue wolf-cut hairstyle caught in a playful moment against planets and alien spacecraft, pushed through an extreme 180-degree fisheye lens with heavy barrel distortion, so the frame edges bend inward and the center bulges toward you. Bold graphic shapes, cinematic lighting, deep shadows and a large word of your choice in the background. Tuned with chaos 50, raw mode, stylize 400 and a 1:2 aspect ratio.
Digital animation style illustration. beautiful blue wolfcut hair girl, space planet background, alien spacecraft, funny moment, wide Extreme perspective effect, lens distortion, strong sense of depth, minimalist vibe, text "STARDUST" in the background, featuring strong circular barrel distortion. The edges of the frame bend drastically inward while the center bulges out, delivering an extreme 180-degree fisheye perspective, cinematic lighting, and deep shadows. --chaos 50 --ar 1:2 --raw --stylize 400 --hd0
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A detailed style-transfer prompt that turns the character in your reference image into a rough, cute Japanese anime doodle: wobbly uneven black crayon lines, big flat color blocks that spill past the outlines, quick white highlights, pink blush strokes and visible crayon grain, as a half-body portrait on a pure white 1:1 canvas. It keeps the character's hairstyle, hair color, eyes, outfit and signature expression recognizable while banning polished line art, gradients and 3D shading.
Convert the character in the reference image into a rough, Japanese-style digital doodle. Overall style: An extremely casual, rough and naive cute Japanese anime character doodle, as if drawn quickly with the rough crayon and marker brushes of an ordinary painting app. Keep the original character recognizable: hairstyle, hair color, facial features, outfit, accessories and signature expression. Line art: * Outline the character with obvious, rough black strokes. * Line weight is very uneven: some lines suddenly get thicker, some break off. * Contours are loose, inaccurate and slightly crooked. * Lines have a clear hand-drawn grainy, smudged texture. * Do not aim for precise outlines; misaligned, overlapping and incomplete lines are allowed. * No fine, smooth vector line art. Coloring: * Use large areas of simple, opaque flat color. * Colors may spill slightly outside the lines; missed spots and white gaps are allowed. * Shadows use a few rough gray strokes or darker strokes of the same hue. * Highlights are casual, short white strokes. * Keep the rough texture of mixed crayon, pastel and marker. * No complex gradients, detailed lighting or rich layers of color. Facial features: * Keep the original character's eye color, eye shape and signature look. * Simplify the features; draw the eyes with thick black lines and simple color blocks. * Keep the mouth as simple as possible; the nose can be left out. * A few casual pink blush strokes can be added to the cheeks. * Keep the character's expression and personality; do not change their temperament. Hair and outfit: * Fully keep the character's most recognizable hairstyle and hair color. * Build the hair from a few large color blocks; do not draw lots of fine strands. * Keep the outfit's original colors, silhouette and signature accessories. * Heavily simplify clothing folds and decorations. * Do not change the outfit or add accessories that are not in the original image. Composition: * 1:1 square canvas. * Half-body portrait of the character, with a slightly oversized head. * Character centered, with natural white space around them. * Pure white background; no text, complex scenery or decorative borders. Most important style constraint: The image must be rough, scribbly, simple and naive, like a cute anime doodle drawn on a whim, not a polished illustration deliberately imitating a sketch. No automatic retouching, no fine line art, no complex hair structure, no subtle gradients, no 3D effects, no heavy lighting, no added complex outfit details. Final result: a low-finish Japanese anime digital doodle with rough black outlines, large flat color blocks, casual white highlights and an obvious crayon grain.
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A short fill-in-the-blanks image prompt for charming paper cut-out storybook characters: name any subject and a simple action and get a full-body character built from layered handmade paper shapes with visible cut edges, soft shadows between layers, flat colorful paper tones and a gentle stop-motion, folk-tale feel on a clean white background. Great for children's book spreads, greeting cards and stickers.
Paper cut-out animation of a little brown bear in a patchwork apron watering a tall sunflower, full-body character, layered paper shapes, handmade paper texture, visible cut edges, soft shadows between layers, flat colorful paper tones, slightly imperfect handcrafted details, minimal paper-collage scenery, charming folk story aesthetic, gentle stop-motion feel, poetic children’s book atmosphere, clean white background
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A production-grade 15-second, four-shot video prompt: an old seaman rows alone through dense dawn fog on cold northern waters until a 140-meter three-masted black-sailed galleon slowly emerges ahead, revealed in layers from a side close-up to two wide shots built to sell its enormous scale. It includes continuity anchors, exact shot timings and lenses, physical rules for boat, sails and fog, cold blue grading, a sound design plan and a list of things to avoid. No battle, no ghosts, no dialogue.
[Title] Black Sails in the Fog [Overall Direction] A 15-second photorealistic maritime film sequence in 16:9, composed with a 2.39:1 widescreen sensibility and natural 24fps motion. The sequence contains four distinct shots. The story takes place in cold northern European waters during the medieval period. An elderly white European man rows a small wooden boat alone into dense fog, where he discovers a black-sailed pirate ship far larger than any ordinary vessel. The sequence focuses on the ship being revealed gradually through the fog and on the extreme difference in scale between the fragile rowboat, the elderly man, and the enormous pirate ship. The atmosphere is cold, silent, and oppressive, but there are no supernatural creatures, ghosts, magical effects, or battles. All timber, canvas, ropes, seawater, fog, and human movement must have convincing weight and physical behavior. The footage should feel captured with a full-scale practical sailing ship, a real ocean location, and a cinema camera—not like a video-game cutscene or fantasy concept art. [Fixed Continuity Anchors] Maintain the same elderly white man, the same face, the same distressed medieval clothing, the same four-meter dark wooden rowboat, the same pair of wooden oars, the same three-masted black-sailed pirate ship, and the same cold blue fog-covered sea throughout the sequence. The pirate ship must remain ahead of the old man and slightly to the right. Its bow faces the rowboat at a slight angle. The ship is almost motionless, moving only slowly with the long ocean swell. The rowboat continues moving toward the pirate ship. Wind, waves, and fog consistently move from the left side of the frame toward the right. Cold morning light always comes from the upper left. The pirate ship must retain exactly three main masts, layered black sails, a high forecastle and sterncastle, a long bowsprit, a dark wooden hull, and dense rigging in every shot. Its design must not change between cuts. [The Old Man’s Appearance] The only character is a 74-year-old white European man. He is thin and slightly stooped, but his shoulders and arms retain the wiry strength of a lifetime of physical labor. His pale, coarse skin is deeply marked by cold wind, saltwater, and years of sun exposure. He has prominent cheekbones, a high and slightly crooked nose, deep-set eyes, and an uneven gray-white beard covering his cheeks and chin. His eyes are gray-blue, naturally moist, with small living catchlights. His forehead, eye corners, and mouth carry authentic age lines. His gray-white hair reaches the back of his neck, dampened by sea fog and loosely tied with an old leather cord. Several wet strands cling to his temples and ears. His skin must retain pores, age spots, and wrinkles. Do not beautify, smooth, or artificially dirty his face. [The Old Man’s Clothing] The old man wears a damaged charcoal-gray coarse-wool cloak. Its edges are frayed and repaired with a few simple hand-sewn patches. Moisture from the sea fog has darkened the fabric. Beneath the cloak is a dirty gray-white linen tunic with a loose collar and worn cuffs. A weathered dark-brown leather belt secures the clothing at his waist. He wears dark gray-brown woven trousers and cracked old leather boots. A short length of faded dark reddish-brown wool is tied around his neck. This scarf is the only faint warm accent in the otherwise cold blue image. The clothing must contain no modern stitching, zippers, plastic, branding, reflective strips, or modern flotation equipment. [Character Behavior] The old man’s immediate objective is to cross the fog-covered water and determine what the enormous shadow ahead actually is. His obstacles are the dense fog and the long ocean swell that repeatedly alters the rowboat’s direction. He rows with the practiced economy of an aging seaman. His center of gravity remains low, his feet planted firmly against the bottom of the boat. His lower back moves first, leaning slowly backward; his shoulders and arms then pull the two wooden oars toward his chest. The strength of his left and right arms differs slightly. Each stroke is accompanied by natural breathing, shoulder-blade movement, friction from the oarlocks, and the forward inertia of the boat. His movement is no longer light, but it remains experienced and efficient. When the deep groan of a massive wooden hull reaches him through the fog, his ongoing rowing stroke slows. The left oar blade remains submerged and creates resistance while the right oar stops in midair. Seawater drips continuously from its edge. His eyes leave the surface of the sea first and move toward the shadow above. He gives one slow blink. Only then does his chin rise and his head follow the direction of his gaze. His lips remain closed, his breathing pauses briefly, and his hands tighten around the oar handles. He does not shout or flee. After confirming that an enormous vessel is hidden in the fog, he adjusts his seated posture, braces both feet, and resumes rowing slowly toward the shadow. His performance conveys caution, exhaustion, and an irresistible need to know—not theatrical fear. [The Rowboat] A medieval wooden rowboat approximately 4.2 meters long, constructed from dark, aged oak. The plank seams and wooden fastening pegs are structurally believable. Most of the black waterproof tar coating has worn away, revealing gray-brown wood grain beneath. The gunwales show wear from years of use. The interior remains sparse, containing only one coil of old hemp rope. Do not add fishing nets, lanterns, barrels, crates, or decorative objects. The two wooden oars must retain the same dimensions, color, material, and position throughout the sequence. The boat rises, falls, and rolls gently with long ocean swells approximately one to one-and-a-half meters high. It must have convincing weight and inertia. Its bow creates a small amount of dark spray as it cuts through the water. It must not float or bounce like a lightweight toy. [The Giant Pirate Ship] The pirate ship is based on the structure of an old European three-masted galleon, but it is far larger than any ordinary historical vessel: approximately 140 meters long, with its tallest mast reaching roughly 80 meters. Its scale is extraordinary, but its timber construction, structural loading, rigging connections, and canvas movement must remain physically believable. The hull is built from almost black, heavily weathered oak. Its timbers are wide and heavy, with visible plank seams and matte iron reinforcement along structural edges. The bow is tall and projects forward with oppressive weight, ending in a long, narrow bowsprit. The stern rises into a multi-level sterncastle. Two rows of dark gunports line the sides of the hull, but no cannons are fired. Salt deposits, dark wet marks, and small amounts of sea growth appear around the waterline. The ship is not rotten and does not glow. Three tall main masts carry multiple levels of square black sails. The canvas is heavy and aged, damaged by wind and salt, with naturally torn edges and visible repairs. The sails are not pure black; they contain subtle layers of deep blue-gray, charcoal, and weathered brown. Dense hemp rigging connects the masts, yards, gunwales, and corners of the sails according to authentic sailing-ship construction. A small faded black flag flies from the highest mast. It bears only a simple, weathered bone-white skull-and-crossbones symbol with no written words. No crew members, torches, or artificial lights are visible on deck. The vessel feels unnaturally quiet but remains completely physical and opaque, never transparent or ghostlike. [Ocean, Fog, and Color] The sequence takes place in cold northern waters at dawn. The boundary between sea and sky is nearly erased by thick blue-gray marine fog. The fog must retain realistic depth: The old man and his rowboat remain relatively clear in the foreground. Water several dozen meters away loses contrast. Only portions of the pirate ship are visible in the middle distance. The upper masts and corners of the sails disappear back into the fog. The seawater consists of low-saturation slate blue, deep blue-gray, and faint gray-green tones. Wave crests do not produce unnaturally bright white foam. The sky is pale cold blue mixed with pearl gray. The pirate ship is near-black wood with deep blue-gray sails. The old man’s faded reddish-brown scarf provides the only very weak warm accent. Wind slowly pushes the fog across the ship, allowing its fixed structure to appear in layers. The fog changes what is visible; the vessel itself never changes shape. The fog must not behave like fast-moving smoke, and it must not make the entire image uniformly blurry. [Shot One | 0–3.5 Seconds | Side Close-Up of the Old Man] A 65mm cinema lens. The camera floats beside the left side of the rowboat, slightly below the old man’s shoulder height, rising and falling gently with the same ocean swell. The old man’s side profile occupies the left half of the frame. The background contains only defocused cold-blue seawater and layered fog. The pirate ship must not appear yet. The old man is already midway through a rowing stroke. His lower back moves slowly backward as both hands pull the oars toward his body. Damp gray hair clings to his face, and the coarse wool cloak responds subtly to the sea wind. A broad swell lifts the wooden boat, and the camera rises with it in a restrained, physically grounded movement. One distant, low note from an old bronze ship’s bell travels through the fog, followed by the deep groan of an enormous timber structure under pressure from the sea. The old man’s rowing motion gradually stops. One oar blade remains submerged while the other pauses in midair. Water drips from the edge of the blade. His eyes move toward the upper-right side of the frame first. He gives one slow blink, then gradually raises his head. Cut directly before the head movement is fully completed. Do not reveal what he sees in this shot. [Shot Two | 3.5–10 Seconds | Rear View and Fog-Shrouded Reveal] Cut to a medium-wide rear view of the old man using a 35mm lens. The camera is positioned close to the stern of the rowboat at approximately shoulder height. Preserve the direction of the boat, the old man’s seated position, the direction of the waves, and the direction of the cold light from the previous shot. The old man occupies the lower center of the frame with his back toward the camera. An enormous dark shape rises inside the fog ahead. At first, only the towering bow and one vague vertical mast line can be distinguished. As the fog moves laterally, three enormous masts, layered black sails, dense rigging, and elevated deck structures gradually become visible. The tops of the masts remain hidden. The pirate ship must not appear completely in a single instant. Its structure remains fixed; only its visibility changes as fog passes across it. After a short pause, the old man resumes rowing. He lowers his center of gravity and completes one slow, deliberate stroke, then begins a second. The rowboat moves forward with the swell. Each time the boat rises onto a wave crest, the ship’s shadow appears taller. When the boat descends into a trough, the lower part of the pirate ship is briefly hidden by water and fog. As the old man continues forward, the bow, the dark gunports, and the lowest black sails become gradually clearer. High-altitude wind causes the enormous black sails to fill and relax with delayed, heavy movement. The rigging tightens and loosens slightly. It must not swing wildly. The pirate ship remains almost motionless, advancing into the swell at an extremely slow speed. [Shot Three | 10–12.5 Seconds | First Wide Shot: Bow Pressure] Cut directly to a low, water-level wide shot using a 28mm lens, observing the rowboat from a distance on its left side. A defocused dark-blue wave crest occupies the foreground. The old man and rowboat sit in the middle ground. The pirate ship’s towering bow, lower black sails, and fog-covered masts dominate the background. The rowboat occupies only about 2% of the frame width. The old man is even smaller, but his charcoal cloak and faded reddish-brown scarf remain consistent. The pirate ship’s bow rises from the sea like a dark timber cliff. Large hull planks, gunports, and matte iron reinforcement provide believable scale references. The foreground wave briefly hides the lower half of the rowboat. As the crest falls, the boat reappears in the same spatial relationship to the ship. The vessel’s position, size, and structure remain stable. The upper black sails and masts are partially swallowed by blue-gray fog. This shot emphasizes the vertical height of the bow and the oppressive distance between the small boat and the ship. [Shot Four | 12.5–15 Seconds | Second Wide Shot: Complete Scale] Cut to a distant, slightly elevated extreme-wide shot using a 50mm long lens to compress spatial depth. The camera remains stable without a fast aerial dive, orbit, or rotation. The enormous three-masted pirate ship appears from a front-side angle and dominates the center-right of the frame. Most of the full hull, long bowsprit, high sterncastle, three main masts, and layered black sails enter the frame. The lower hull remains relatively clear, while the upper masts and sail corners fade in and out of the dense fog. The black pirate flag moves slowly at the top of the highest mast. The old man and his boat remain ahead of the ship and slightly to the left, reduced to a tiny dark mark on the cold-blue sea. The rowboat continues rising and falling with the swell as the old man rows slowly. The pirate ship remains heavy, silent, and almost immovable. Maintain a safe distance between both vessels. No collision occurs. Large areas of cold-blue water and empty fog surround the subjects. There is no land, no other vessel, and no modern structure on the horizon. Hold the final composition for approximately half a second, keeping the old man, the small boat, and the enormous black sails visible together. [Editing Logic] The sequence contains four shots and three direct cuts. The first cut moves from the old man raising his head in side profile to the enormous ship shadow seen from behind him. The second cut moves from the old man continuing to row into the low water-level wide shot, revealing the height of the bow. The third cut moves to a farther, slightly elevated extreme-wide shot, revealing the total length of the pirate ship and the surrounding ocean. The two wide shots have different purposes: The first wide shot establishes the vertical pressure of the bow. The second wide shot reveals the full vessel and the final scale relationship between the ship, the ocean, and the rowboat. Do not insert close-ups of skulls, cannons, eyes, rigging hardware, or pirate crew. [Cinematography and Image Texture] A 35mm theatrical release-print look with soft gate edges, medium-low microcontrast, fine and slightly uneven natural grain, layered deep blues and charcoal blacks, and gentle highlight rolloff. Fog and slight condensation along the edge of the lens are the only dominant image imperfections. Do not simultaneously add rain, dust, smoke, sparks, or strong lens flares. The camera retains small amounts of ocean movement and inertia while keeping the horizon generally stable. Close shots use restrained depth of field. Distant contrast decreases naturally through atmospheric perspective. The pirate ship must not appear unnaturally sharp inside the fog, but the entire image must not be covered by a uniform blur filter. [Lighting] The only primary light source is cold dawn light diffused through cloud cover from the upper left. A faint silver-blue edge light touches the left side of the old man’s face, wet hair, wooden oars, and gunwale. The side of the pirate ship facing the light reveals low-contrast cold-gray structure. The backlit side falls toward deep blue-black while preserving readable timber layers. Do not use moonbeams, torchlight, ghostly green illumination, magical glow, lightning, or warm sunset light. [Sound] No dialogue, narration, or musical score. The soundscape consists of wooden oars entering the sea, low water displacement, friction from the oarlocks, creaking rowboat planks, the old man’s controlled but strained breathing, wind softened by fog, one distant bronze ship-bell note, the deep groan of the enormous wooden hull under wave pressure, rigging tightening, and black sails filling slowly with wind. The bell and timber groan are heard before the pirate ship appears in Shot One. Low wooden resonance expands when the ship is revealed in Shot Two. In the final two wide shots, ocean waves, wind, and large areas of silence again dominate the soundscape. [Negative Constraints] Avoid changes to the old man’s face, age, skin tone, beard length, hairstyle, cloak color, scarf, or clothing between shots. Do not make him suddenly younger. Avoid modern clothing, armor, aristocratic costume, stereotypical pirate clothing, modern life jackets, plastic, zippers, modern shoes, wristwatches, firearms, or electronic equipment. Avoid incorrect anatomy, distorted body proportions, extra arms, extra fingers, changing numbers of oars, hands passing through oar handles, rigid rowing, mechanical looping movements, sliding feet, exaggerated screaming, an open-mouthed expression of terror, theatrical fear, heroic posing, empty eyes, or frozen eyes. Avoid changes to the rowboat’s dimensions, color, design, or material. Do not transform the boat into a modern lifeboat, fishing vessel, sailboat, or plastic boat. Avoid weightless floating, sudden sideways movement, violent bouncing, capsizing, flooding, breaking apart, or colliding with the pirate ship. Avoid changes to the pirate ship’s design, length, hull shape, bow, stern, or color. Maintain exactly three main masts. Do not allow masts to appear or disappear. Avoid changing the black sails’ color, melting canvas, incoherent rigging, swapped bow and stern, moving gunports, shipping containers, steam engines, modern motors, steel superstructures, or electric lights. Avoid a cartoon pirate ship, theme-park attraction, miniature model, plastic wood grain, glowing ghost ship, transparent hull, skeleton crew, ghosts, zombies, sea monsters, tentacles, flying ships, magic, green ghost flames, burning sails, cannon battles, explosions, or lightning. Avoid fog disappearing suddenly between cuts or changing color. Do not make the fog behave like fast-moving smoke. The pirate ship must not deform as fog passes over it. Avoid tsunami waves, vertical walls of water, gelatinous water, fabric-like water, smoke-like water, changing wave direction, sudden daylight changes, warm sunset colors, or night. Avoid fast editing, additional shots, location changes, time jumps, flash transitions, dissolves, morphing transitions, camera teleportation, high-speed drone dives, rotating around the ship, sudden zooms, dolly zooms, slow motion, or speed ramping. Avoid video-game cutscenes, fantasy concept art, pirate-themed advertising, uniform sharpness across the entire frame, excessive sharpening, excessive skin smoothing, oversaturation, commercial teal-and-orange grading, neon, lasers, decorative light beams, excessive lens flare, text, subtitles, brand logos, and modern watermarks. Allow the simple skull-and-crossbones pirate flag at the top of the highest mast, but do not place any readable text on the flag.
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A detailed build prompt for AI coding models that produces a real-time, endlessly looping cyberpunk pixel-art city diorama for a 10-hour lo-fi music video: a rain-soaked megacity at 2:37 AM with warm windows, flickering neon, fog, steam and tiny moving details, rendered at 640×360 and upscaled exactly 3× with nearest-neighbor for razor-sharp pixels, all delivered as one standalone HTML file with a clean full-screen mode for recording.
# Build an Immersive, Animated Cyberpunk Pixel-Art Diorama Create a breathtaking, richly detailed, fully animated cyberpunk pixel-art diorama designed to serve as the endlessly looping visual backdrop for a 10-hour lo-fi music video on YouTube. I want something genuinely mesmerizing. A tiny, self-contained world that feels alive. The kind of scene viewers can stare at for hours and continually discover little details they hadn't noticed before. ## Creative Direction Imagine a forgotten corner of a sprawling futuristic megacity, sometime around 2089. It's 2:37 AM. Rain has been falling for days. The streets are soaked, the buildings are ancient and overcrowded, and the city glows with a thousand little signs of life. The atmosphere should feel simultaneously melancholic, cozy, mysterious, and strangely comforting. This is a world where technology has taken over everything, but human warmth still survives in little pockets. Think cinematic cyberpunk meets the intimate charm of meticulously crafted pixel-art environments. Don't settle for a generic neon skyline. Create a place with personality, history, visual storytelling, and a strong sense of depth. ## Visual Composition Design a beautiful, layered environment with a compelling focal point and plenty of environmental storytelling. Consider elements such as: - A tiny apartment or rooftop hideaway overlooking a vast, rain-drenched futuristic city. - Glowing windows revealing glimpses of other people's lives. - Holographic advertisements, animated signage, and distant traffic. - Clusters of air-conditioning units, ventilation ducts, exposed wiring, satellite dishes, and improvised technology. - Warmly illuminated vending machines, computer terminals, and mysterious electronic equipment. - Rainwater collecting in puddles and cascading from rooftops. - Distant elevated trains or flying vehicles silently moving through the mist. - Tiny human silhouettes occasionally visible behind windows. - Overgrown plants reclaiming corners of this technological world. These are creative possibilities, not a mandatory checklist. Make deliberate artistic choices. Prioritize a coherent, memorable composition over cramming everything into the frame. The scene should feel handcrafted, with exquisite attention to small details. ## Color and Lighting Build the entire visual identity around dramatic warm/cold contrast. The outside world is dominated by deep midnight blues, desaturated teals, indigo shadows, and cold cyan rain. Against this darkness, create beautiful pockets of warmth: - Rich amber light spilling from apartment windows. - Soft orange and golden illumination from lamps and electronics. - Occasional saturated magenta, electric violet, and crimson neon. - Warm reflections shimmering across cold, wet surfaces. Use lighting to tell a story. Every illuminated window should feel like a little sanctuary against the enormous, indifferent city. The overall image should be dark and atmospheric without becoming muddy or losing detail. ## Animation: Make the World Feel Alive This is absolutely critical. I don't want a static illustration with a generic rain effect. I want a living pixel-art environment with dozens of subtle, independently animated details. Build a sophisticated system of layered ambient animations: ### Weather and atmosphere - Beautiful multi-depth rainfall with varying speeds and intensities. - Occasional heavier gusts that push rain diagonally. - Thin layers of drifting fog and atmospheric haze. - Smoke rising from vents and chimneys, curling into the night. - Water dripping from ledges and splashing into puddles. - Ripples spreading across reflective surfaces. ### Lighting and electronics - Neon signs flickering irregularly, with convincing electrical behavior. - Screens cycling through mysterious data and advertisements. - Small indicator lights blinking asynchronously. - Warm interior lights gently fluctuating. - Occasional sparks from damaged electrical equipment. - Animated reflections responding to nearby light sources. ### Environmental life - Slowly rotating ventilation fans. - Distant vehicles passing behind buildings. - Tiny silhouettes moving behind illuminated windows. - Steam escaping from pipes. - Hanging cables or signs subtly swaying in the wind. - Occasional small, unexpected details that reward prolonged viewing. Every effect should have its own rhythm. Avoid synchronized, repetitive animation that makes the world feel artificial. ## Seamless Infinite Looping This scene must run beautifully for 10 hours or longer without a visible reset, jump, or discontinuity. Design every animation to be mathematically seamless or continuously procedural. Use independent animation cycles, staggered timing, seeded randomness, and layered motion to create the impression of endless organic variation. No sudden resets. No obvious looping rain textures. No visible animation seams. The scene should remain interesting over time without becoming distracting. This is lo-fi music, so the animation should be hypnotic and relaxing rather than chaotic. Reserve dramatic events for rare, carefully controlled moments. ## Pixel-Art Rendering: Non-Negotiable The visual style must be authentic, razor-sharp pixel art, not a high-resolution illustration with a pixelation filter. Technical requirements: - Internal rendering resolution: 640 × 360 - Final presentation resolution: 1920 × 1080 - Exact integer upscale: 3× - Scaling: nearest-neighbor only - Every source pixel must become a perfectly sharp 3×3 block. - Absolutely no bilinear filtering, blurry edges, antialiasing, or subpixel rendering. - No smooth vector graphics mixed into the pixel-art scene. - All effects, including particles, rain, fog, reflections, and lighting, must respect the pixel grid. - Maintain consistent pixel density across every visual layer. The result should look spectacular on a full-screen 1080p YouTube video, with beautiful, deliberately placed hard pixels. ## Technical Implementation Build this as a complete, self-contained, real-time animated experience. Use whatever rendering architecture delivers the best combination of visual quality, performance, and pixel-perfect consistency. HTML5 Canvas, WebGL, or a hybrid approach are all acceptable. Prioritize: - Smooth, efficient rendering at 1080p. - Deterministic, seamless animation. - Rich visual depth through layered parallax and atmospheric perspective. - Procedural particle systems for weather and environmental effects. - Carefully controlled lighting, blending, and reflections. - Stable performance over extremely long runtimes. - A fixed camera with no unintended movement or jitter. - Correct resizing while preserving the 16:9 composition and pixel-perfect scaling. If useful, implement the world as multiple independently animated layers, all composited at the internal resolution before the final upscale. Include a way to run the scene full-screen and hide any interface elements so it can be captured cleanly for YouTube. ## Artistic Quality Bar This should feel like an exceptional piece of animated pixel art, not a programming demonstration. Composition, atmosphere, color harmony, and environmental storytelling matter just as much as technical sophistication. Avoid generic procedural rectangles masquerading as buildings. Give the architecture intentional shapes, recognizable structures, believable construction details, and visual variety. Use foreground silhouettes, middle-ground architecture, and distant city layers to create a strong sense of scale. Every element should contribute to a cohesive world. The ultimate goal: A mesmerizing cyberpunk night scene that makes someone wish they could climb through their monitor, sit beside a warm window with a cup of coffee, and listen to the rain falling on a futuristic city that never sleeps. Make it beautiful. Make it atmospheric. Make it technically excellent. And above all, make it feel alive. Don't just describe how you would build it. Actually implement the complete working experience. Deliver the finished piece as a single standalone HTML file (all code inline, no external assets or build step) that runs by opening it in a browser.
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Featured Prompts
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Photoreal wildlife shot: a pure white arctic hare curled asleep in deep powder snow at blue hour, black-tipped ears folded back, fur dusted with snow crystals, soft wind-shaped drifts and a few paw prints, quiet tundra fading into blue dusk with a thin pink-lavender horizon, low telephoto angle and shallow depth of field.
A photorealistic wildlife photograph of a pure white arctic hare curled asleep in deep powder snow at blue hour, wide landscape 16:9. The hare is tucked into a tight round ball in the lower-left third of the frame, long black-tipped ears folded back along its body, eyes closed, thick winter fur dusted with tiny snow crystals, breath not visible. Soft fresh powder surrounds it in smooth wind-shaped drifts, with a few delicate paw prints leading in from the left and stopping at the resting spot. Behind, a quiet snowy tundra rolls into soft blue dusk haze under a clear cold sky with a thin pink-lavender band on the far horizon. Shot low to the snow with a telephoto lens, shallow depth of field, sharp fur texture on the hare, creamy bokeh on the distant drifts, calm, cold, peaceful mood. No people, no text, no logos, no watermarks.
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Photoreal macro wildlife shot: a bright green panther chameleon with orange-red and turquoise bands walks left along a thick mossy branch dotted with small pink star-shaped flowers, its tail curled in a tight spiral at the right, with wet, glossy dark-green leaves and dew drops in the lower corners and a soft green rainforest bokeh behind, shallow depth of field and sharp skin texture.
A photorealistic macro wildlife photograph of a panther chameleon walking along a thick mossy branch in a misty rainforest, wide landscape 16:9. The chameleon is shown in full side profile facing left, its body bright green with bold orange-red and turquoise bands running along its side, a raised crest on its head, a ridge of small spikes along its back, a turret eye looking forward, and its feet gripping the branch mid-step. Its long tail curls into a tight spiral at the right. The thick, horizontal branch spans the whole frame, covered in green moss and dotted with small clusters of pink star-shaped flowers. Wet, glossy dark-green leaves with fresh dew drops fill the lower corners. Behind, a soft green rainforest background melts into creamy bokeh with round highlights. Shot at eye level with a macro lens, shallow depth of field, sharp scale and skin texture on the chameleon, soft diffused light after rain, calm, vivid, natural mood. No people, no text, no logos.
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Detailed storybook cutaway of a giant oak at dusk with three lamp-lit library rooms inside: a red armchair reading nook at the bottom, curved bookshelves with a ladder in the middle, and an attic with a brass telescope under a shingled roof, plus a spiral staircase, a lantern-lit rope bridge, falling orange autumn leaves, and a sunset sky.
A detailed cutaway illustration of a cozy three-level treehouse library built inside the trunk of a giant oak tree at dusk, landscape 4:3. The trunk is cut open to show three warm, lamp-lit rooms. The bottom level has a red velvet armchair with a patterned cushion and a knitted throw, a round braided rug, a lantern on a small side table, low bookshelves, a small round window, and ivy around the opening. The middle level has curved floor-to-ceiling bookshelves packed with books, a round window, a wooden ladder leading up, a hanging lantern, a glowing desk lamp, and a potted plant on the left. The top level is an attic under a shingled gable roof with a round window, a brass telescope on a tripod, and a desk covered with books. Outside, a wooden spiral staircase winds up the right side of the trunk, and a rope bridge hung with glowing lanterns stretches off to the right. Orange autumn leaves cling to the branches and drift through the air over a forest, under a deep blue dusk sky fading to an orange sunset on the right. Storybook style, fine ink lines, warm painted colors. No people, no text, no logos.
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Hand-painted Delft blue tile panel in cobalt on milky white glaze: a 6 by 4 grid of tiles with corner ornaments, a three-masted sailing ship with billowing sails on white-capped waves on the left, rows of tulip fields and a village with a single church spire on the right shore, puffy clouds and seagulls above, and a fine crackle in the glaze.
A hand-painted Delft blue tile panel showing a three-masted sailing ship on choppy waves beside a shore of tulip fields and a small Dutch village, wide landscape 16:9. The panel is a grid of 6 by 4 square glazed tiles with thin grout lines, each tile edge decorated with small cobalt corner ornaments, and larger curling flourishes in the four outer corners. On the left, a tall wooden three-masted ship with billowing square sails, rigging lines, and small pennants on the mastheads sails through rolling, white-capped waves that fill the whole bottom row of tiles. On the right, a flat shoreline stretches across the middle of the panel with long, straight rows of tulip fields leading toward a village of gabled houses and trees with a single slender church spire. Puffy, softly shaded clouds float across a pale sky, with a few seagulls gliding between them. Painted only in cobalt blue on milky white glaze, with soft brush washes, fine line detail, and a delicate crackle in the glaze, traditional Dutch pottery look. No text, no lettering, no logos.
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Photoreal wildlife moment: a humpback whale bursting diagonally out of dark blue-green water, its grooved pale throat and long white pectoral fins showing, sparkling spray frozen in the air, with golden-lit sea cliffs topped by dark pine forest, hazy headlands, and a few seagulls in a pale sky at golden hour.
A photorealistic wildlife photograph of a humpback whale breaching out of the sea off a rocky, forested coast at golden hour, wide landscape 16:9. The huge whale bursts diagonally up out of dark blue-green water just left of center, its knobby head and dark back rising toward the upper right, its pale grooved throat and belly turned toward the camera; one long white pectoral fin with a scalloped edge lies along its side and the other flares out to the right. Curtains of sparkling spray and thousands of water droplets are frozen in the air around its body, with a white splash at its base. Behind it, tall rugged sea cliffs glow golden in the low sun on the left, topped by dark pine and spruce forest, and more rocky headlands recede into soft haze on the right. A few seagulls fly in the upper right under a pale, clear sky. Warm low sunlight from the left, telephoto lens look, fast shutter, sharp detail, rich natural colors, powerful and awe-filled mood. No boats, no people, no text, no logos.
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Convert SAS programs into executable Python code while preserving business logic, data transformations, statistical behavior, and output consistency. Supports SAS DATA Steps, PROC procedures, SQL, macros, formats, informats, and statistical models. Generates Python code with required dependencies, conversion notes, and validation guidance. Designed for enterprise workloads, including banking and financial services.
1```yaml2sas_to_python_converter:3 metadata:4 name: "SAS-to-Python Converter"5 version: "1.0"6 purpose: "Enterprise-grade SAS code conversion"7 primary_input: "SAS source code"8 primary_output: "Equivalent executable Python code"910 role:...+679 more lines
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Photoreal wildlife scene: a dark-brown bull moose with wide palmate antlers wading knee-deep in a glassy mountain lake at autumn sunrise, water dripping from its muzzle, low mist over the water, dark spruces and golden aspens on the shore, pink-lit snow-capped peaks, and a near-perfect mirror reflection.
A photorealistic wildlife photograph of a large bull moose wading knee-deep in a glassy mountain lake at autumn sunrise, wide landscape 16:9. The dark-brown moose stands left of center in side profile, body facing right, head turned slightly toward the camera, crowned with wide palmate antlers with many tines; drops of water fall from its muzzle and small ripples ring its legs. A thin layer of low white mist hangs over the still water. The far shoreline is a band of dark spruce and fir trees mixed with bright golden-yellow aspens and birches, with more gold on the right side. Behind them rise jagged snow-capped mountain peaks lit soft pink and peach by the first sun, under a pale, clear sky. The calm lake mirrors everything below the shoreline: the moose, its antlers, the golden trees, and the glowing peaks, in an almost perfect reflection. Telephoto lens look with gentle compression and a sharp subject, warm soft sunrise light with cool shadows, rich natural autumn colors, quiet and majestic mood. No people, no text, no logos.
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Transform any portrait into a personalized LEGO minifigure while preserving your signature hairstyle, facial features, glasses, clothing, and accessories. ✨ Classic LEGO proportions 🎨 Custom-designed printed outfits 🧩 Personalized facial details and accessories 💎 Glossy plastic textures and realistic 3D rendering 📸 Premium miniature-style studio photography One photo. One miniature version of you. A whole new way to see yourself in LEGO form. Upload your photo and build your minifigure!
1{2 "prompt": {3 "title": "Personalized LEGO Minifigure Collectible Generator",...+165 more lines
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Photoreal Halloween night: five glowing carved jack-o'-lanterns (toothy grins, a surprised face, a smiling face, and a black cat silhouette) on weathered porch steps of an old white farmhouse, with white mini pumpkins, a straw broom, a glowing brass lantern, scattered maple leaves, bare trees, and a small full moon in a dark blue sky.
A photorealistic Halloween night photograph of five glowing carved jack-o'-lanterns on the weathered wooden porch steps of an old white clapboard farmhouse, wide landscape 16:9. On the upper step: one with triangle eyes and a jagged toothy grin, and one with round surprised eyes and an open O-shaped mouth. On the lower step: triangle eyes and a toothy grin on the far left, smiling crescent eyes in the center, and on the right a carved black cat silhouette with an arched back, backlit by candlelight. Three small white mini pumpkins sit on the top step at the right, beside a white painted porch post with an old straw broom leaning against it. A white wooden railing runs down the left side, and a glowing old brass hurricane lantern hangs on the house wall at the upper left. Dry orange and red maple leaves are scattered over the worn grey steps. Beyond the porch, bare tree silhouettes stand against a dark blue night sky with a small full moon behind thin clouds at the upper right. Low camera angle, shallow depth of field, warm candle glow against cool blue night, cozy and festive mood. No people, no text, no logos.
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Latest Bots
Plans safe API call budgets across services and environments: maps quotas and burst limits, sizes caches and queues, and spots which jobs will trip 429s before they hit production.
F@f1
Builds a complete insurance claim file for home, auto, travel or health: what to photograph, which documents to attach, how to timeline the loss, and a clean packet ready to submit.
F@f0
Keeps one living FAQ for investors: collects recurring diligence questions, drafts clear answers from your docs and metrics, and flags gaps before the next update or call.
F@f0
Keeps deploy, backup, upgrade and secrets safe. Made for the Lean Software Factory team.
Reviews every PR and gates the merge. Made for the Lean Software Factory team.
Ships UI issues as small PRs with screenshots. Made for the Lean Software Factory team.
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Analyses a Stock through a 360 degrees approach and scores the metric at each step.
Act as a top-tier private equity fund manager with over 30 years of real trading experience. Your task is to conduct a comprehensive analysis of a given stock script. Follow the investment checklist, which includes evaluating metrics such as performance, valuation, growth, profitability, technical indicators, and risk. ### Structure Your Analysis: 1. **Company Overview**: Provide a concise overview of the company, highlighting key points. 2. **Peer Comparison**: Analyze how the company compares with its peers in the industry. 3. **Financial Statements**: Examine the financial statements for insights into financial health. 4. **Macroeconomic Factors**: Assess the impact of current macroeconomic conditions on the company. 5. **Sectoral Rotation**: Determine if the sector is currently in favor or facing challenges. 6. **Management Outlook**: Evaluate the management's perspective and strategic direction. 7. **Shareholding Analysis**: Review the shareholding pattern for potential insights. ### Evaluation and Scoring: - For each step, provide a clear verdict and assign a score out of 5, being specific, accurate, and logical. - Avoid bias or blind agreement; base your conclusions on thorough analysis. - Consider any additional factors that may have been overlooked. Your goal is to deliver an objective and detailed assessment, leveraging your extensive experience in the field.
Act as a top-tier private equity fund manager with over 15 years of real trading experience. Analyze stocks using a comprehensive 8-dimensional framework covering fundamentals, capital flows, technicals, policy catalysts, sentiment, risks, strategies, and summary.
Act as a top-tier private equity fund manager. You have over 15 years of real trading experience and are an expert in five-dimensional analysis: capital flow, technical, fundamental, policy, and sentiment analysis. Your analysis style is cold-blooded, precise, and highly pragmatic, focusing solely on probability, win rate, and risk-reward ratio.
When analyzing a stock, you must output a complete analysis according to the following 8 dimensions:
1. Fundamental Hardcore Score (out of 10)
- 2025-2026 consensus net profit growth forecast (must include numbers)
- Current PE-TTM / PE-LYR / PEG (the lower the better)
- ROE-TTM (must be ≥12% to pass)
- Debt ratio, operating cash flow/net profit ratio, gross margin trend
- Industry position + moat summary in one sentence
2. Capital Flow Predatory Analysis
- Net inflow of main funds in the last 10/20 days + ranking (top 10% of the market is strong)
- Northbound funds, financing balance, hot money seats, Dragon & Tiger List data
- Change in the number of shareholders (continuous decline for 2-3 periods is a plus)
3. Technical Institutional Judgement
- Current trend (ascending channel/descending channel/bottom box/top box)
- Core support and resistance levels (must be accurate to 0.1 yuan)
- Current state of MACD, KDJ, RSI, Bollinger Bands + 3-5 day future golden death cross signals
- Volume structure (volume stagnation/shrinkage adjustment/sky-high volumes)
4. Policy/Plate Catalysts (determine explosiveness)
- The rise and fall of the sector where the stock is located in the past month + ranking
- Whether it hits the Central Economic Work Conference, the "Fifteenth" plan, M&A six rules, industrial policy dividends
- Recent performance forecasts, third quarter reports exceeding expectations, repurchases, holdings increase, major shareholder lifting, etc.
5. Sentiment and Market Consensus
- Latest institutional ratings + target price (highest/lowest/median)
- The market consensus is "dark horse→blockbuster" or "hugging→peak"
- Turnover structure (hot money-led or value funds-led)
6. Risks and Stop Loss
- The most fatal risk point (performance reversal, geopolitical, goodwill impairment, etc.)
- Iron stop loss level (exit immediately if breached)
7. Trading Conclusion and Strategy (must provide a clear answer)
- Probability of rising in the next month (must include percentage)
- Target price range (short-term/medium-term)
- Suggested position (heavy/half/light/observe)
- Specific entry points + position adjustment logic
8. Ultimate One-Sentence Summary (within 10 characters)
— Please strictly analyze the stock according to the above 8-point format: {stock name + code}TrendingImage
A detailed style-transfer prompt that turns the character in your reference image into a rough, cute Japanese anime doodle: wobbly uneven black crayon lines, big flat color blocks that spill past the outlines, quick white highlights, pink blush strokes and visible crayon grain, as a half-body portrait on a pure white 1:1 canvas. It keeps the character's hairstyle, hair color, eyes, outfit and signature expression recognizable while banning polished line art, gradients and 3D shading.
Convert the character in the reference image into a rough, Japanese-style digital doodle. Overall style: An extremely casual, rough and naive cute Japanese anime character doodle, as if drawn quickly with the rough crayon and marker brushes of an ordinary painting app. Keep the original character recognizable: hairstyle, hair color, facial features, outfit, accessories and signature expression. Line art: * Outline the character with obvious, rough black strokes. * Line weight is very uneven: some lines suddenly get thicker, some break off. * Contours are loose, inaccurate and slightly crooked. * Lines have a clear hand-drawn grainy, smudged texture. * Do not aim for precise outlines; misaligned, overlapping and incomplete lines are allowed. * No fine, smooth vector line art. Coloring: * Use large areas of simple, opaque flat color. * Colors may spill slightly outside the lines; missed spots and white gaps are allowed. * Shadows use a few rough gray strokes or darker strokes of the same hue. * Highlights are casual, short white strokes. * Keep the rough texture of mixed crayon, pastel and marker. * No complex gradients, detailed lighting or rich layers of color. Facial features: * Keep the original character's eye color, eye shape and signature look. * Simplify the features; draw the eyes with thick black lines and simple color blocks. * Keep the mouth as simple as possible; the nose can be left out. * A few casual pink blush strokes can be added to the cheeks. * Keep the character's expression and personality; do not change their temperament. Hair and outfit: * Fully keep the character's most recognizable hairstyle and hair color. * Build the hair from a few large color blocks; do not draw lots of fine strands. * Keep the outfit's original colors, silhouette and signature accessories. * Heavily simplify clothing folds and decorations. * Do not change the outfit or add accessories that are not in the original image. Composition: * 1:1 square canvas. * Half-body portrait of the character, with a slightly oversized head. * Character centered, with natural white space around them. * Pure white background; no text, complex scenery or decorative borders. Most important style constraint: The image must be rough, scribbly, simple and naive, like a cute anime doodle drawn on a whim, not a polished illustration deliberately imitating a sketch. No automatic retouching, no fine line art, no complex hair structure, no subtle gradients, no 3D effects, no heavy lighting, no added complex outfit details. Final result: a low-finish Japanese anime digital doodle with rough black outlines, large flat color blocks, casual white highlights and an obvious crayon grain.
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Latest Prompts
Masterprompt M01 aus der Produktmanagement-Toolbox von FRIEDMANN SOLUTIONS · 01 Grundlagen & Markt. Erstellt aus Produkt-, Markt- und Kundendaten eine Entscheidungsvorlage mit Evidenzmatrix, Annahmenregister und drei priorisierten nächsten Schritten. Toolbox: https://friedmann-solutions.com/toolbox/produktmanagement-27-masterprompts/?utm_source=prompts.chat&utm_medium=referral&utm_campaign=produktmanagement_masterprompts&utm_content=m01
Übernimm die Rolle eines Senior Product Managers und Product Operations Leads. Erstelle auf Deutsch ein kompaktes Product Context & Decision Brief, das als Grundlage für eine konkrete Produktentscheidung dient. ## Eingaben - Produkt oder Dienstleistung: produkt - Produktphase (Idee, MVP, Wachstum oder Reife): produktphase - Kundensegmente: kundensegmente - Markt oder Region: markt_region - Geschäftsmodell: geschaeftsmodell - Strategisches Produktziel: produktziel - Zeithorizont: zeithorizont - Team, Budget und weitere Rahmenbedingungen: rahmenbedingungen - Quellen, Daten und Dokumente: quellen_daten - Anstehende Entscheidung: entscheidung ## Vorgehen 1. Strukturiere die Ausgangslage nach Kundenproblem, Produktnutzen, Angebot, Wertschöpfung, Wettbewerb, Kanälen, Kennzahlen, Abhängigkeiten und Risiken. 2. Prüfe auf Widersprüche. Unterscheide bestätigte Fakten, durch Quellen gestützte Interpretationen, plausible Hypothesen und Unbekanntes. Verknüpfe jede belegte Aussage mit ihrer Quelle. 3. Stelle höchstens fünf kritische Rückfragen. Bearbeite alle Teile, für die die Informationen bereits ausreichen. 4. Kläre, welche Entscheidung ansteht, welche Rolle sie trifft, bis wann sie fallen soll und welche Belege dafür nötig sind. 5. Halte die entscheidungsrelevanten Annahmen fest. Priorisiere die drei wichtigsten Informationslücken und schlage jeweils einen konkreten Weg vor, sie zu schließen. ## Ausgabe A. Executive Summary mit höchstens 150 Wörtern B. Produktsteckbrief C. Evidenzmatrix mit den Spalten Aussage, Status, Beleg und Relevanz sowie ein Annahmenregister D. Risiken, Einschränkungen und die drei wichtigsten Informationslücken samt Beschaffungsweg E. Drei priorisierte nächste Schritte mit jeweils einer verantwortlichen Rolle F. Entscheidungsvorlage mit Entscheidungsfrage, zuständiger Rolle, Frist, Kriterien und erforderlichen Belegen ## Qualitätsregeln Erfinde keine Kunden, Studien, Kennzahlen, Quellen oder internen Dokumente. Behandle leere Felder und unveränderte Platzhalter als fehlende Eingaben und kennzeichne sie mit „nicht bereitgestellt“. Begründe bei jeder vorgeschlagenen Maßnahme, wie sie zum Produktziel beiträgt.
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A complete 30-second Seedance-ready video prompt for a poetic magical-realism beach at dawn: a woman in her 40s listens to a seashell as thousands of corked glass bottles wash ashore, each holding a tiny glowing light. Uncorking one releases a child's laugh; the beach becomes a field of warm lights; transparent waves show dissolving family memories; she returns the shell and the whole ocean answers with a golden glow. Soft camera, tender audio design, no dialogue or on-screen text.
[Global Setting] 30-second poetic magical realism sequence, 16:9, 4K, photoreal, emotional atmosphere. A quiet, wide sandy beach at dawn. The ocean is perfectly calm, pale pink and soft gold light spreading across the horizon, gentle mist over the water. No dialogue, no text on screen. [Character] A woman in her 40s, long loose dark hair moving in the breeze, a simple oversized cream knit sweater and a long linen skirt, barefoot in the wet sand. Calm, melancholic, gentle eyes. She holds an old pale seashell, worn smooth by time. [0–2s] Medium shot. She stands at the water's edge, holding the old seashell to her ear, eyes closed. She hears only silence. Soft waves touch her feet. [2–8s] All around her, thousands of small glass bottles wash ashore with the gentle waves, each sealed with a cork, each containing a tiny floating glowing light. She kneels, picks one up, uncorks it. A child's laugh echoes softly across the water. Her eyes fill with tears, a small smile. [8–15s] The camera moves low along the shoreline at sand level. One by one, the bottles begin to glow brighter, warm white and gold, until the entire beach becomes a field of soft lights stretching into the distance. [15–23s] The waves turn transparent like glass. Beneath the surface, fleeting images appear and dissolve like reflections: an old family house, a birthday cake with candles, two people talking at a kitchen table, a hand holding another hand, faces of people once loved. They drift by gently and fade. [23–30s] She walks into the shallow water and gently places the seashell back into the sea. A moment of stillness. Then the ocean answers: a warm golden glow spreads from where the shell sank, across the whole surface, all the way to the horizon, meeting the rising sun. She stands bathed in golden light, peaceful. Hold. [Camera] Slow, gentle, floating movements. Medium shots on her, low tracking along the shoreline, a wide shot for the golden ocean. No cuts, no shaky cam. [Audio] Soft lapping waves, gentle breeze, silence in the shell, the clink of glass bottles on sand, a child's distant laugh echoing, faint overlapping whispers and laughter from the memories beneath the waves, a delicate piano and soft strings rising, then a warm resonant hum as the ocean glows. [Consistency] Same woman, same seashell, same beach. Dawn pink light turning golden. Tender, emotional, never dark.
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A compact atmospheric image prompt for a rainy Japanese alley at night: a faceless female figure in a cropped jacket, pleated mini-skirt and patent over-the-knee boots stands under a clear umbrella that catches magenta light, beneath a half-dead neon kanji sign. Low camera, soft grain, mood of waiting for a reply that never comes. Optional "Cel Shaded Art," prefix for a sharper comic look.
Rainy alley, one neon kanji sign half-out, female Japanese figure wearing cropped jacket tank top, pleated mini-skirt, patent high-heeled over-the-knee boots, under a clear umbrella that catches magenta light. Camera low. No face shown. Mood: waiting for a reply that isn't coming. Soft grain, no film logo. Optional prefix for extra punch: Cel Shaded Art,
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TrendingImage
A complete GPT Image–ready JSON prompt for a vibrant Japanese anime summer illustration: an original stylish girl in a coral-orange asymmetric tunic and cherry-red bucket hat walks the wet shoreline of a turquoise tropical beach. It locks warm clothing colors against cool ocean blues, a 9:16 full-body fashion pose, golden sunbeams, seagulls and a strong negative prompt so the outfit never blends into the water.
1{2 "aspect_ratio": "9:16",3 "style": "Premium Japanese anime illustration, vibrant summer movie aesthetic, expressive hand-drawn character art, high-contrast cel shading, brilliant tropical colors",...+3 more lines
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Opinionated conventions for focused, well-bound Cloudflare Workers at the edge.
# Cloudflare Worker taste of an edge-first platform engineer - Prefer Wrangler for local dev, secrets, and deploys; keep `wrangler.toml` (or `wrangler.json`) as the source of truth for bindings. Confidence: 0.95 - Write Workers in TypeScript with the official workers-types, and enable `strict` in `tsconfig`. Confidence: 0.90 - Keep each Worker focused on one job: edge API, rewrite, auth gate or queue consumer; split unrelated work into separate Workers. Confidence: 0.85 - Put durable state in KV, Durable Objects, D1 or R2 through bindings; never pretend the Worker isolate is a long-lived server. Confidence: 0.90 - Load secrets with `wrangler secret` and read them from `env`; do not commit API keys or `.dev.vars` with real values. Confidence: 0.95 - Validate every request early: method, path, auth header and a size limit before parsing the body. Confidence: 0.90 - Return explicit status codes and small JSON error bodies; avoid throwing uncaught exceptions that become opaque 500s. Confidence: 0.85 - Use `waitUntil` for non-critical work such as analytics or cache warming so the client response is not blocked. Confidence: 0.80 - Prefer the Cache API or Cache Rules for GETs that are safe to reuse, and set Cache-Control deliberately on every response. Confidence: 0.85 - Test with `wrangler dev` and Miniflare-compatible unit tests; cover routing, auth failures and binding edge cases. Confidence: 0.85 - Pin compatibility date in Wrangler config and bump it on purpose after reading the runtime changelog. Confidence: 0.85 - Use Durable Objects when you need strong consistency per key (rooms, counters, locks); use KV for eventually consistent reads. Confidence: 0.85 - Stream large responses and uploads when possible; avoid buffering whole files in memory inside the isolate. Confidence: 0.80 - Log with structured JSON fields (request id, route, status, duration) and ship them to your observability sink. Confidence: 0.80 - Gate production deploys behind a staging route or environment, and smoke-test the worker URL before shifting traffic. Confidence: 0.85 - Keep CPU-heavy work short; move bulk transforms and ML to Queues, Workflows or an origin service. Confidence: 0.85 - Prefer fetch to first-party Cloudflare services over stuffing large SDKs into the Worker bundle. Confidence: 0.75 - Review the bundle size on every change; drop unused deps and avoid Node polyfills you do not need. Confidence: 0.80 - Document every binding and its purpose at the top of the Worker entry file so onboarding does not require tribal knowledge. Confidence: 0.75 - Treat cron Triggers and Queue consumers as first-class entrypoints with the same validation and logging standards as HTTP. Confidence: 0.80
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Taste
Opinionated conventions for reliable, flake-resistant Playwright end-to-end tests.
# Playwright E2E taste of a flake-intolerant QA engineer - Keep end-to-end tests in the same repository as the app, under a top-level `e2e/` or `tests/e2e/` folder. Confidence: 0.90 - Use Playwright Test as the runner; do not mix Cypress and Playwright in one project. Confidence: 0.90 - Prefer `getByRole`, `getByLabel` and `getByTestId` over CSS or XPath selectors that break on restyles. Confidence: 0.95 - Add a stable `data-testid` only when role and label are genuinely ambiguous, and document the convention in the README. Confidence: 0.85 - Assert with Playwright's auto-waiting expect API; never sprinkle fixed `waitForTimeout` calls to "make it pass". Confidence: 0.95 - Isolate each test with a fresh browser context and seed data; tests must not depend on run order. Confidence: 0.90 - Cover the critical user journeys first: sign-in, the main create/edit flow, checkout or publish, and sign-out. Confidence: 0.90 - Keep E2E count small and high-value; push edge cases and validation matrices down to unit and component tests. Confidence: 0.85 - Use Page Object or fixture helpers for shared flows, but keep assertions in the test file so failures stay readable. Confidence: 0.80 - Run tests against a dedicated preview or ephemeral environment with known seed data, not against production. Confidence: 0.90 - Capture trace, screenshot and video on failure only, and upload them as CI artifacts. Confidence: 0.90 - Pin the Playwright browser revision in CI so local and pipeline screenshots stay comparable. Confidence: 0.85 - Tag slow or third-party-dependent tests and exclude them from the default PR suite; run the full set on main or nightly. Confidence: 0.80 - Stub or mock external payments, email and SMS providers; never hit real vendor sandboxes from every PR. Confidence: 0.85 - Prefer API or storage-state setup for authenticated sessions instead of clicking through login in every test. Confidence: 0.90 - Fail the build when a test is flaky more than once in a short window; quarantine and fix instead of rerunning forever. Confidence: 0.85 - Use `test.step` to group actions in the report so a failure points at the broken step, not a wall of clicks. Confidence: 0.75 - Keep viewport and locale explicit in the config for visual checks; do not rely on developer laptop defaults. Confidence: 0.80 - Review traces for every new flake before merging a "retry" config change. Confidence: 0.85 - Document how to run one file and one headed test locally in a short CONTRIBUTING note. Confidence: 0.80
0
Taste
Opinionated conventions for building maintainable Next.js App Router applications.
# Next.js App taste of an App Router pragmatist - Use the App Router with the `app/` directory; do not start new features in the Pages Router. Confidence: 0.95 - Default every page and layout to a Server Component, and add `"use client"` only where interactivity, browser APIs or React state require it. Confidence: 0.95 - Colocate route files as `page.tsx`, `layout.tsx`, `loading.tsx`, `error.tsx` and `not-found.tsx` next to the segment they own. Confidence: 0.90 - Fetch data in Server Components with `fetch` and set an explicit `cache` or `next.revalidate` policy; never leave caching accidental. Confidence: 0.90 - Put shared UI in `components/` and keep route folders free of unrelated helpers. Confidence: 0.85 - Use Route Handlers under `app/api` only for webhooks, auth callbacks and thin BFF endpoints, not as a second application layer. Confidence: 0.85 - Validate all searchParams, route params and form bodies with Zod before they reach business logic. Confidence: 0.90 - Prefer Server Actions for mutations that stay on your origin, and return typed results the UI can handle without guessing. Confidence: 0.85 - Keep environment secrets out of `NEXT_PUBLIC_*`; expose only values that are safe in the browser. Confidence: 0.95 - Use `next/image` for every raster image and set width, height or `fill` plus sizes so CLS stays near zero. Confidence: 0.90 - Configure absolute imports with `@/` in `tsconfig.json` and ban deep relative paths like `../../../`. Confidence: 0.85 - Split marketing, app and admin into route groups such as `(marketing)` and `(app)` when they need different layouts. Confidence: 0.80 - Stream slow sections with `<Suspense>` boundaries and dedicated `loading.tsx` files instead of blocking the whole page. Confidence: 0.85 - Pin Node and package-manager versions in `package.json` engines and a lockfile, and run the same versions in CI and production. Confidence: 0.85 - Turn on ESLint with `eslint-config-next` and TypeScript `strict`, and fail CI on new errors. Confidence: 0.90 - Prefer the Metadata API (`generateMetadata`, `metadata`) over manual `<head>` tags for titles, Open Graph and robots. Confidence: 0.85 - Use middleware only for rewrites, redirects and light auth gates; keep heavy logic in Server Components or Route Handlers. Confidence: 0.80 - Ship one design-system entry for buttons, inputs and typography, and avoid one-off Tailwind class piles in every page. Confidence: 0.75 - Measure Core Web Vitals in production and treat a regression on LCP or INP as a release blocker. Confidence: 0.80 - Prefer incremental Static and ISR for public pages; reserve fully dynamic rendering for authenticated or highly personalized views. Confidence: 0.85
0
I am looking to publicize my art as an artwork through media’s like instagram and TikTok to start monetizing my art. I have have some original pieces and stickers but plan on advancing to prints in the near future. My art stems from pokemon fanart. What steps do I need to follow to successfully gain traction on social media platforms and creating an online shop
Reads Postgres EXPLAIN and EXPLAIN ANALYZE plans (text or JSON) and finds why a query is slow - sequential scans on large relations, nested loops with huge row estimates, misestimates (estimated vs actual), high buffer reads, Sort/Hash that spill to disk, missing LIMIT pushdown and expensive nodes by actual time. Ranks fixes (index, rewrite, stats). Offline stdlib Python analyzer.
---
name: postgres-explain-slow-query-analyzer
description: Reads Postgres EXPLAIN and EXPLAIN ANALYZE plans (text or JSON) and finds why a query is slow - sequential scans on large relations, nested loops with huge row estimates, misestimates (estimated vs actual), high buffer reads, Sort/Hash that spill to disk, missing LIMIT pushdown and expensive nodes by actual time. Ranks fixes (index, rewrite, stats). Offline stdlib Python analyzer.
---
# Postgres EXPLAIN Slow Query Analyzer
You help developers and DBAs read Postgres query plans. A slow query is rarely fixed by staring at the SQL alone: the plan shows whether Postgres chose a sequential scan, a nested loop that exploded, a bad row estimate, a Sort that spilled to disk, or a Hash Join that read gigabytes of buffers. You take an `EXPLAIN` or `EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT|JSON)` plan, name the expensive nodes, explain why they hurt, and rank concrete fixes. This skill is about reading plans, not rewriting SQL from scratch without evidence.
You do not run destructive statements. Prefer `EXPLAIN (ANALYZE, BUFFERS)` on a replica or in a transaction you can roll back; warn that ANALYZE actually runs the query.
## Files in this skill
- `scripts/explain_analyze.py` - parses text or JSON EXPLAIN / EXPLAIN ANALYZE output; scores nodes by actual time or cost; reports EX01-EX12 (seq scans, nested-loop blowups, misestimates, disk sorts/hashes, high shared reads, never-executed nodes, parallel imbalance); ranks suggested fixes (Python 3.8+ standard library only)
- `references/plan-nodes-and-buffers.md` - how to read node types, costs, rows, loops, buffers and temp files
- `references/checks-and-codes.md` - every finding code, severity and what to do, plus input tips
- `templates/slow-query-review.md` - the review note you hand back with the plan excerpt and ranked fixes
- `examples/orders_by_email.explain.txt` - a text EXPLAIN ANALYZE of a slow lookup (seq scan + nested loop)
- `examples/review-report.md` - the real analyzer output on the example
## Workflow
### 1. Collect the plan
Ask for Postgres version, the SQL, and an `EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)` (or JSON). If ANALYZE is unsafe on production, take `EXPLAIN (BUFFERS)` without ANALYZE and say timings are estimates only. Capture table sizes (`pg_stat_user_tables`, `pg_relation_size`) when a Seq Scan looks suspicious.
### 2. Run the analyzer
```
python3 scripts/explain_analyze.py examples/orders_by_email.explain.txt
python3 scripts/explain_analyze.py plan.json --json-out
python3 scripts/explain_analyze.py plan.txt --min-ms 5
python3 scripts/explain_analyze.py --self-test
```
Exit 0 = no high findings, 1 = high findings, 2 = bad input. Without a shell, walk the plan tree bottom-up using `references/plan-nodes-and-buffers.md` and apply the codes in `references/checks-and-codes.md`.
### 3. Read the result
- High first: EX01 Seq Scan on a large relation with a selective filter, EX03 Nested Loop with huge actual rows x loops, EX05 misestimate ratio over 100x on a hot node, EX07 Sort/Hash that spilled to temp files.
- Medium: EX02 index-less join keys, EX04 Filter removing most rows after a scan, EX06 high shared read buffers, EX08 never-executed branches that still look costly on paper.
- Low/info: EX09 parallel workers uneven, EX10 planning time high vs execution, EX11 LIMIT not pushed down, EX12 CTE MATERIALIZED forcing full compute.
### 4. Rank fixes
Prefer evidence-backed changes: add or adjust an index that matches the Filter/Join Filter, update statistics (`ANALYZE`), rewrite a correlated subquery that became a Nested Loop, raise `work_mem` only when a Sort/Hash spilled and the set fits memory, or change a CTE to `NOT MATERIALIZED` when Postgres 12+ materialised it needlessly. Do not invent indexes without naming columns from the plan.
### 5. Deliver
Return the verdict, the top expensive nodes with actual time and buffers, the finding list, and a short ranked fix list using `templates/slow-query-review.md`.
## Example result
On the sample plan the Nested Loop drove 98% of time: an outer Seq Scan on `customers` filtered by `lower(email)` (no expression index) fed an inner Index Scan on `orders_customer_id_idx` that ran 48,210 loops. Estimated rows on the outer scan were 42 vs actual 48,210 (misestimate). Shared read buffers were high on `orders`. Ranked fixes: expression index on `lower(email)`, then confirm join selectivity; `ANALYZE customers`.
## Rules
- Base every claim on a node in the plan (type, cost/actual, rows, loops, buffers).
- Distinguish estimated-only EXPLAIN from ANALYZE; never treat costs as milliseconds.
- Do not recommend disabling seq scans globally or setting random_page_cost to fantasy values without measuring.
- Warn that EXPLAIN ANALYZE runs the query (triggers, writes, locks).
- This is a plan-reading aid, not a substitute for `pg_stat_statements` monitoring or a full schema review.
FILE:examples/orders_by_email.explain.txt
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=0.42..184220.50 rows=12 width=72) (actual time=0.085..912.440 rows=96 loops=1)
Buffers: shared hit=1204 read=88440
-> Seq Scan on public.customers c (cost=0.00..1850.00 rows=42 width=40) (actual time=0.040..88.200 rows=48210 loops=1)
Filter: (lower((email)::text) = 'ada@example.com'::text)
Rows Removed by Filter: 151790
Buffers: shared hit=20 read=1830
-> Index Scan using orders_customer_id_idx on public.orders o (cost=0.42..4.20 rows=8 width=40) (actual time=0.015..0.016 rows=0 loops=48210)
Index Cond: (customer_id = c.id)
Filter: (created_at >= (now() - '90 days'::interval))
Rows Removed by Filter: 3
Buffers: shared hit=1184 read=86610
Planning Time: 0.412 ms
Execution Time: 912.610 ms
FILE:examples/review-report.md
# Postgres EXPLAIN analysis
Source: `examples/orders_by_email.explain.txt`
Execution Time: 912.61 ms
Planning Time: 0.412 ms
**Verdict: NEEDS WORK** - 3 high finding(s).
## Top nodes by time (or cost)
| Node | Relation | Actual ms (x loops) | Cost | Rows est -> act | Loops | Shared read |
|------|----------|---------------------|------|-----------------|-------|-------------|
| Nested Loop | - | 912.4 | 184220 | 12 -> 96 | 1 | 88440 |
| Index Scan | public.orders | 771.4 | 4 | 8 -> 0 | 48210 | 86610 |
| Seq Scan | public.customers | 88.2 | 1850 | 42 -> 48210 | 1 | 1830 |
## Findings
| Sev | Code | Detail |
|-----|------|--------|
| high | EX01 | Seq Scan on public.customers with selective Filter ((lower((email)::text) = 'ada@example.com'::text)); removed 151790 rows |
| high | EX03 | Nested Loop child Index Scan ran loops=48210 (actual rows/loop~0) consuming ~771.4 ms |
| high | EX05 | Misestimate on Seq Scan public.customers: est rows=42 actual=48210 (ratio~1148x) |
| medium | EX02 | Nested Loop has inner/outer Seq Scan on public.customers |
| medium | EX06 | Nested Loop ? shared read=88440 blocks |
| medium | EX06 | Index Scan public.orders shared read=86610 blocks |
## Ranked fixes (suggested)
1. Add a btree/expression index that matches the Seq Scan Filter (check function wrappers like lower()).
2. Reduce Nested Loop outer rows or index the inner join key; re-check join order after ANALYZE.
3. Run ANALYZE on the touched tables; consider extended statistics for correlated columns.
4. Lower shared reads via a better index or by filtering earlier so Nested Loop does not re-read heaps.
FILE:references/checks-and-codes.md
# Checks and codes
| Code | Severity | Meaning | What to do |
|------|----------|---------|------------|
| EX01 | high | Seq Scan on a relation with a selective Filter and large actual rows or high actual time | Expression or btree index matching the filter; avoid wrapping the column in a function without a matching expression index |
| EX02 | medium | Join condition looks unindexed (Nested Loop inner Seq Scan or high loops with Filter on join key) | Index the inner join key; confirm data types match (no cast that kills the index) |
| EX03 | high | Nested Loop with very high loops * actual rows (row explosion) | Reduce outer rows first; consider Hash/Merge Join; fix misestimates driving NL choice |
| EX04 | medium | Filter removes most rows after a scan (rows in >> rows out) | Push predicate into an index condition; rewrite so the planner can use an index |
| EX05 | high | Misestimate: actual/estimate or estimate/actual >= 100 on a node that matters | ANALYZE; extended stats for correlated columns; avoid non-immutable functions in predicates |
| EX06 | medium | High shared read buffers on a hot node | Caching/index to reduce heap fetches; check if Nested Loop re-reads the same pages |
| EX07 | high | Sort or Hash spilled to temp files (external merge / Batches > 1 with temp written) | Reduce rows before sort/hash; raise work_mem carefully for this query session; add supporting index to avoid sort |
| EX08 | low | Node never executed (actual loops=0) but dominates estimated cost | Ignore for runtime; useful when comparing plans without ANALYZE |
| EX09 | low | Parallel query with uneven worker contribution | Check parallel_* settings and whether gather is worth it |
| EX10 | info | Planning time large vs execution time | Prepared statements; simplify one-off planning; partition pruning issues |
| EX11 | medium | LIMIT above a full Sort or full aggregate of a large set | Rewrite so order/filter allows an index scan with early stop |
| EX12 | medium | CTE Scan with MATERIALIZED forcing full evaluation | WITH ... AS NOT MATERIALIZED (PG12+) or inline the CTE |
## Input tips
- Text plans: paste the full tree; do not strip indentation.
- JSON plans: Postgres `FORMAT JSON` array with one plan object; the script accepts a raw array or `{Plan: ...}`.
- Optional header lines like `QUERY PLAN` or dashed rules are ignored.
FILE:references/plan-nodes-and-buffers.md
# Plan nodes and buffers
## Capturing a useful plan
```sql
EXPLAIN (ANALYZE, BUFFERS, VERBOSE, FORMAT TEXT) <query>;
-- or
EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) <query>;
```
On production prefer a hot standby or wrap writes in a transaction you roll back. `ANALYZE` executes the statement.
## Reading a text node line
Example: `Seq Scan on public.customers (cost=0.00..1550.00 rows=42 width=64) (actual time=0.020..85.110 rows=48210 loops=1)`
- Node type and relation
- `cost=startup..total` (planner units, not ms)
- `rows` = estimated rows; under ANALYZE also `actual time` and `rows` and `loops`
- Total time for that node ~= (actual time total) * loops for the subtree contribution pattern; leaf time is clearer than parent when parents include children
## Common node types
- **Seq Scan**: read the whole heap. Fine for tiny tables or when most rows match; painful with a selective Filter on a large table.
- **Index Scan / Index Only Scan / Bitmap Heap Scan**: good when selective. Bitmap Heap with many rechecks can still hurt.
- **Nested Loop**: for each outer row, run inner. Great for few outer rows + indexed inner; catastrophic when outer is large.
- **Hash Join / Merge Join**: better for larger sets; watch Hash that spills (`Batches` > 1, temp written).
- **Sort**: `Sort Method: external merge` means spill to disk; consider `work_mem` or fewer rows earlier.
- **Aggregate / GroupAggregate / HashAggregate**: check whether grouping columns are indexed or pre-filtered.
- **CTE Scan**: Postgres 12+ may MATERIALIZE CTEs; can block filter pushdown.
- **Limit**: should sit above a cheap path; if Limit is above a full sort of everything, push criteria down.
## Buffers (BUFFERS option)
- `shared hit` = found in shared buffers (cheap)
- `shared read` = read from OS/disk into buffers (expensive under cache miss)
- `temp read/written` = work_mem spill files
A node with huge `shared read` on a repeated Nested Loop inner side often needs a better outer filter or index.
## Row misestimates
Compare `rows` (estimate) to actual `rows`. Ratios over 10x (especially 100x+) on expensive nodes mean bad stats, stale `ANALYZE`, correlated columns, or a function wrapping an indexed column (`lower(email)`).
## What this skill does not do
It does not replace `pg_stat_statements`, auto_explain, or index design reviews that start from the schema alone. It starts from one plan and explains that plan.
FILE:scripts/explain_analyze.py
#!/usr/bin/env python3
"""Postgres EXPLAIN / EXPLAIN ANALYZE plan analyzer (stdlib only, Python 3.8+).
Parses text or JSON plans, scores expensive nodes, and reports findings EX01-EX12 focused on
reading the plan (seq scans, nested-loop blowups, misestimates, buffer pressure, disk spills).
Usage:
explain_analyze.py PLAN.txt|.json [--min-ms N] [--json-out]
explain_analyze.py --self-test
Exit: 0 = no high findings, 1 = high findings, 2 = bad input.
"""
import argparse, json, re, sys
SEV = {"high": 0, "medium": 1, "low": 2, "info": 3}
NODE_RE = re.compile(
r"^(?P<indent>\s*)(?:->\s*)?(?P<type>.+?)"
r"(?=\s+using\s+|\s+on\s+|\s*\(cost=)"
r"(?:\s+using\s+(?P<idx>[\w.]+))?"
r"(?:\s+on\s+(?P<rel>[\w.]+))?"
r"(?:\s+(?P<alias>\w+))?"
r"\s*\(cost=(?P<cstart>[\d.]+)\.\.(?P<ctotal>[\d.]+)\s+rows=(?P<erows>[\d]+)\s+width=(?P<width>[\d]+)\)"
r"(?:\s*\(actual time=(?P<astart>[\d.]+)\.\.(?P<atotal>[\d.]+)\s+rows=(?P<arows>[\d]+)\s+loops=(?P<loops>[\d]+)\))?"
)
BUF_RE = re.compile(
r"Buffers:\s*(?:shared hit=(?P<shit>\d+))?"
r"(?:\s*read=(?P<sread>\d+))?"
r"(?:.*?temp read=(?P<tread>\d+))?"
r"(?:.*?written=(?P<twrite>\d+))?",
re.I,
)
FILTER_RE = re.compile(r"^\s+Filter:\s*(.+)$")
REMOVED_RE = re.compile(r"Rows Removed by Filter:\s*(\d+)")
SORT_RE = re.compile(r"Sort Method:\s*(.+)$", re.I)
HASH_BATCH_RE = re.compile(r"Batches:\s*(\d+)", re.I)
PLAN_TIME_RE = re.compile(r"Planning Time:\s*([\d.]+)\s*ms", re.I)
EXEC_TIME_RE = re.compile(r"Execution Time:\s*([\d.]+)\s*ms", re.I)
class InputError(Exception):
pass
def parse_text(text):
lines = text.splitlines()
nodes = []
extras = {"planning_ms": None, "execution_ms": None}
stack = []
i = 0
while i < len(lines):
line = lines[i].rstrip()
if not line or set(line) <= set("-=") or line.strip() == "QUERY PLAN":
i += 1
continue
m = PLAN_TIME_RE.search(line)
if m:
extras["planning_ms"] = float(m.group(1)); i += 1; continue
m = EXEC_TIME_RE.search(line)
if m:
extras["execution_ms"] = float(m.group(1)); i += 1; continue
m = NODE_RE.match(line)
if m:
raw_indent = re.match(r"^(\s*)", line).group(1)
ind = len(raw_indent)
node = {
"type": m.group("type").strip(),
"rel": m.group("rel"),
"idx": m.group("idx"),
"cost_start": float(m.group("cstart")),
"cost_total": float(m.group("ctotal")),
"est_rows": int(m.group("erows")),
"width": int(m.group("width")),
"act_start": float(m.group("astart")) if m.group("astart") else None,
"act_total": float(m.group("atotal")) if m.group("atotal") else None,
"act_rows": int(m.group("arows")) if m.group("arows") else None,
"loops": int(m.group("loops")) if m.group("loops") else None,
"filter": None,
"removed": None,
"buffers": {},
"sort_method": None,
"hash_batches": None,
"indent": ind,
"children": [],
}
while stack and stack[-1][0] >= ind:
stack.pop()
if stack:
stack[-1][1]["children"].append(node)
else:
nodes.append(node)
stack.append((ind, node))
j = i + 1
while j < len(lines):
pl = lines[j]
if not pl.strip():
break
if NODE_RE.match(pl) or PLAN_TIME_RE.search(pl) or EXEC_TIME_RE.search(pl):
break
if not pl.startswith(" ") and not pl.startswith("\t"):
break
fm = FILTER_RE.match(pl)
if fm:
node["filter"] = fm.group(1).strip()
rm = REMOVED_RE.search(pl)
if rm:
node["removed"] = int(rm.group(1))
bm = BUF_RE.search(pl)
if bm:
node["buffers"] = {k: int(bm.group(k)) for k in ("shit", "sread", "tread", "twrite") if bm.group(k)}
sm = SORT_RE.search(pl)
if sm:
node["sort_method"] = sm.group(1).strip()
hb = HASH_BATCH_RE.search(pl)
if hb:
node["hash_batches"] = int(hb.group(1))
j += 1
i = j
continue
i += 1
if not nodes:
raise InputError("no plan nodes found (need EXPLAIN text with cost=... rows=...)")
return nodes, extras
def parse_json_plan(obj):
if isinstance(obj, list):
if not obj:
raise InputError("empty JSON plan array")
obj = obj[0]
if not isinstance(obj, dict):
raise InputError("JSON plan must be an object or array of objects")
plan = obj.get("Plan") or obj.get("plan")
if not plan:
raise InputError("JSON missing Plan object")
extras = {
"planning_ms": obj.get("Planning Time"),
"execution_ms": obj.get("Execution Time"),
}
def conv(p, indent=0):
node = {
"type": p.get("Node Type", "Unknown"),
"rel": p.get("Relation Name") or p.get("Relation"),
"idx": p.get("Index Name"),
"cost_start": float(p.get("Startup Cost", 0)),
"cost_total": float(p.get("Total Cost", 0)),
"est_rows": int(p.get("Plan Rows", 0)),
"width": int(p.get("Plan Width", 0)),
"act_start": p.get("Actual Startup Time"),
"act_total": p.get("Actual Total Time"),
"act_rows": p.get("Actual Rows"),
"loops": p.get("Actual Loops"),
"filter": p.get("Filter"),
"removed": p.get("Rows Removed by Filter"),
"buffers": {},
"sort_method": p.get("Sort Method"),
"hash_batches": p.get("Hash Batches") or p.get("Batches"),
"indent": indent,
"children": [],
}
if "Shared Hit Blocks" in p or "Shared Read Blocks" in p:
node["buffers"] = {
k: int(p[src]) for k, src in (("shit", "Shared Hit Blocks"), ("sread", "Shared Read Blocks"),
("tread", "Temp Read Blocks"), ("twrite", "Temp Written Blocks")) if src in p
}
for ch in p.get("Plans") or []:
node["children"].append(conv(ch, indent + 2))
return node
return [conv(plan)], extras
def walk(nodes):
out = []
def w(n):
out.append(n)
for c in n["children"]:
w(c)
for n in nodes:
w(n)
return out
def time_ms(n):
if n["act_total"] is None or n["loops"] is None:
return None
return float(n["act_total"]) * float(n["loops"])
def analyze(nodes, extras, min_ms=1.0):
findings = []
flat = walk(nodes)
def add(sev, code, msg, node=None):
findings.append((sev, code, msg, node))
for n in flat:
t = n["type"]
ms = time_ms(n)
est, act = n["est_rows"], n["act_rows"]
loops = n["loops"] or 1
rel = n["rel"] or "?"
if t.startswith("Seq Scan") and n.get("filter") and (n.get("removed") or 0) > 1000:
if (act or 0) > 1000 or (ms or 0) >= min_ms * 10:
add("high", "EX01",
"Seq Scan on %s with selective Filter (%s); removed %s rows" % (rel, n["filter"], n.get("removed")), n)
if t.startswith("Nested Loop"):
for c in n["children"]:
if c["type"].startswith("Seq Scan"):
add("medium", "EX02", "Nested Loop has inner/outer Seq Scan on %s" % (c.get("rel") or "?"), c)
if t.startswith("Nested Loop"):
for c in n["children"]:
cl = c.get("loops") or 0
if cl >= 10000 and (time_ms(c) or 0) >= min_ms * 20:
add("high", "EX03",
"Nested Loop child %s ran loops=%d (actual rows/loop~%s) consuming ~%.1f ms" % (
c["type"], cl, c.get("act_rows"), time_ms(c) or 0), c)
if n.get("removed") and act is not None:
total_in = n["removed"] + act
if total_in > 1000 and n["removed"] / total_in >= 0.9:
add("medium", "EX04",
"Filter on %s %s removes %.0f%% of rows (%d removed, %d kept)" % (
t, rel, 100.0 * n["removed"] / total_in, n["removed"], act), n)
if act is not None and est is not None and (ms or 0) >= min_ms:
if est == 0:
ratio = float("inf") if act > 0 else 1.0
else:
ratio = max(act / max(est, 1), est / max(act, 1))
if ratio >= 100:
add("high", "EX05",
"Misestimate on %s %s: est rows=%d actual=%d (ratio~%.0fx)" % (t, rel, est, act, ratio if ratio != float("inf") else 9999), n)
sread = n.get("buffers", {}).get("sread") or 0
if sread >= 5000 and (ms or 0) >= min_ms:
add("medium", "EX06", "%s %s shared read=%d blocks" % (t, rel, sread), n)
sm = (n.get("sort_method") or "").lower()
if "external" in sm:
add("high", "EX07", "Sort spilled to disk (%s) on %s" % (n["sort_method"], t), n)
if n.get("hash_batches") and n["hash_batches"] > 1 and (n.get("buffers", {}).get("twrite") or 0) > 0:
add("high", "EX07", "Hash spilled: Batches=%d temp written=%s" % (n["hash_batches"], n["buffers"].get("twrite")), n)
if loops == 0 and n["cost_total"] >= 1000:
add("low", "EX08", "Node %s never executed but cost=%.0f" % (t, n["cost_total"]), n)
if t.startswith("Limit"):
for c in n["children"]:
if c["type"].startswith("Sort") and (c.get("act_rows") or c.get("est_rows") or 0) > 1000:
add("medium", "EX11", "LIMIT above Sort of large set (%s rows)" % (c.get("act_rows") or c.get("est_rows")), n)
if "CTE" in t and (ms or n["cost_total"] > 1000):
add("medium", "EX12", "CTE Scan %s may be materialized; consider NOT MATERIALIZED if filters should push down" % (n.get("rel") or ""), n)
if extras.get("planning_ms") and extras.get("execution_ms"):
if extras["planning_ms"] > extras["execution_ms"] and extras["planning_ms"] > 20:
add("info", "EX10", "Planning Time %.1f ms > Execution Time %.1f ms" % (extras["planning_ms"], extras["execution_ms"]), None)
findings.sort(key=lambda x: (SEV.get(x[0], 9), x[1]))
return findings
def report(nodes, extras, findings, src):
flat = walk(nodes)
lines = []
lines.append("# Postgres EXPLAIN analysis")
lines.append("")
lines.append("Source: `%s`" % src)
if extras.get("execution_ms") is not None:
lines.append("Execution Time: %s ms" % extras["execution_ms"])
if extras.get("planning_ms") is not None:
lines.append("Planning Time: %s ms" % extras["planning_ms"])
high = sum(1 for f in findings if f[0] == "high")
lines.append("")
if high:
lines.append("**Verdict: NEEDS WORK** - %d high finding(s)." % high)
elif findings:
lines.append("**Verdict: OK with notes** - %d finding(s), none high." % len(findings))
else:
lines.append("**Verdict: OK** - no findings above thresholds.")
lines.append("")
lines.append("## Top nodes by time (or cost)")
lines.append("")
scored = []
for n in flat:
ms = time_ms(n)
scored.append((ms if ms is not None else -1, n["cost_total"], n))
scored.sort(key=lambda x: (x[0] if x[0] >= 0 else -1, x[1]), reverse=True)
lines.append("| Node | Relation | Actual ms (x loops) | Cost | Rows est -> act | Loops | Shared read |")
lines.append("|------|----------|---------------------|------|-----------------|-------|-------------|")
for ms, cost, n in scored[:12]:
am = ("%.1f" % ms) if ms is not None and ms >= 0 else "-"
ra = "%s -> %s" % (n["est_rows"], n["act_rows"] if n["act_rows"] is not None else "-")
lines.append("| %s | %s | %s | %.0f | %s | %s | %s |" % (
n["type"], n["rel"] or "-", am, cost, ra, n["loops"] if n["loops"] is not None else "-",
n.get("buffers", {}).get("sread", "-")))
lines.append("")
lines.append("## Findings")
lines.append("")
if not findings:
lines.append("None.")
else:
lines.append("| Sev | Code | Detail |")
lines.append("|-----|------|--------|")
for sev, code, msg, _ in findings:
lines.append("| %s | %s | %s |" % (sev, code, msg.replace("|", "/")))
lines.append("")
lines.append("## Ranked fixes (suggested)")
lines.append("")
fixes = []
codes = {f[1] for f in findings}
if "EX01" in codes:
fixes.append("Add a btree/expression index that matches the Seq Scan Filter (check function wrappers like lower()).")
if "EX03" in codes or "EX02" in codes:
fixes.append("Reduce Nested Loop outer rows or index the inner join key; re-check join order after ANALYZE.")
if "EX05" in codes:
fixes.append("Run ANALYZE on the touched tables; consider extended statistics for correlated columns.")
if "EX07" in codes:
fixes.append("Cut rows before Sort/Hash, or raise work_mem for this session only after measuring.")
if "EX06" in codes:
fixes.append("Lower shared reads via a better index or by filtering earlier so Nested Loop does not re-read heaps.")
if "EX11" in codes:
fixes.append("Rewrite so LIMIT can stop on an ordered index scan instead of sorting the full set.")
if "EX12" in codes:
fixes.append("Try WITH ... AS NOT MATERIALIZED or inline the CTE so filters push down.")
if not fixes:
fixes.append("No high-priority structural fixes from the thresholds; keep the plan under pg_stat_statements watch.")
for i, f in enumerate(fixes, 1):
lines.append("%d. %s" % (i, f))
lines.append("")
return "\n".join(lines) + "\n"
def load_plan(path):
raw = open(path, encoding="utf-8").read()
if path.endswith(".json") or raw.lstrip().startswith(("[", "{")):
try:
obj = json.loads(raw)
except json.JSONDecodeError as e:
raise InputError("invalid JSON: %s" % e)
return parse_json_plan(obj)
return parse_text(raw)
def self_test():
tests = []
def t(name, ok):
tests.append((name, bool(ok)))
sample = """Nested Loop (cost=0.42..184220.50 rows=12 width=72) (actual time=0.085..912.440 rows=96 loops=1)
Buffers: shared hit=1204 read=88440
-> Seq Scan on public.customers c (cost=0.00..1850.00 rows=42 width=40) (actual time=0.040..88.200 rows=48210 loops=1)
Filter: (lower((email)::text) = 'ada@example.com'::text)
Rows Removed by Filter: 151790
Buffers: shared hit=20 read=1830
-> Index Scan using orders_customer_id_idx on public.orders o (cost=0.42..4.20 rows=8 width=40) (actual time=0.015..0.016 rows=0 loops=48210)
Index Cond: (customer_id = c.id)
Buffers: shared hit=1184 read=86610
Planning Time: 0.412 ms
Execution Time: 912.610 ms
"""
nodes, extras = parse_text(sample)
t("parsed root nested loop", nodes[0]["type"] == "Nested Loop")
t("two children", len(nodes[0]["children"]) == 2)
t("execution time", extras["execution_ms"] == 912.610)
findings = analyze(nodes, extras)
codes = {f[1] for f in findings}
t("EX01 seq scan selective", "EX01" in codes)
t("EX03 nested loop explosion", "EX03" in codes)
t("EX05 misestimate", "EX05" in codes)
t("EX06 shared reads", "EX06" in codes)
r = report(nodes, extras, findings, "t")
t("report verdict needs work", "NEEDS WORK" in r)
tiny = """Seq Scan on public.t (cost=0.00..1.05 rows=5 width=4) (actual time=0.002..0.003 rows=5 loops=1)
Execution Time: 0.020 ms
"""
nodes2, ex2 = parse_text(tiny)
f2 = analyze(nodes2, ex2)
t("tiny table no high", not any(x[0] == "high" for x in f2))
spill = """Sort (cost=1000..1100 rows=10000 width=8) (actual time=50..60 rows=10000 loops=1)
Sort Method: external merge Disk: 4096kB
"""
ns, es = parse_text(spill)
fs = analyze(ns, es)
t("EX07 external sort", any(x[1] == "EX07" for x in fs))
j = [{"Plan": {"Node Type": "Limit", "Startup Cost": 0, "Total Cost": 10, "Plan Rows": 10, "Plan Width": 4,
"Actual Startup Time": 0.1, "Actual Total Time": 5.0, "Actual Rows": 10, "Actual Loops": 1,
"Plans": [{"Node Type": "Sort", "Startup Cost": 0, "Total Cost": 9, "Plan Rows": 5000, "Plan Width": 4,
"Actual Startup Time": 0.1, "Actual Total Time": 4.9, "Actual Rows": 5000, "Actual Loops": 1,
"Sort Method": "quicksort"}]},
"Planning Time": 1.0, "Execution Time": 5.1}]
nj, ej = parse_json_plan(j)
fj = analyze(nj, ej)
t("JSON limit+sort -> EX11", any(x[1] == "EX11" for x in fj))
try:
parse_text("not a plan")
ok = False
except InputError:
ok = True
t("bad input rejected", ok)
for name, ok in tests:
print(("PASS " if ok else "FAIL ") + name)
passed = sum(ok for _, ok in tests)
print("%d/%d passed" % (passed, len(tests)))
return 0 if passed == len(tests) else 1
def main(argv=None):
ap = argparse.ArgumentParser(description="Analyze Postgres EXPLAIN / EXPLAIN ANALYZE plans.")
ap.add_argument("plan", nargs="?")
ap.add_argument("--min-ms", type=float, default=1.0)
ap.add_argument("--json-out", action="store_true")
ap.add_argument("--self-test", action="store_true")
a = ap.parse_args(argv)
if a.self_test:
return self_test()
if not a.plan:
ap.print_usage(sys.stderr)
return 2
try:
nodes, extras = load_plan(a.plan)
findings = analyze(nodes, extras, min_ms=a.min_ms)
except (OSError, InputError) as e:
print("error: %s" % e, file=sys.stderr)
return 2
if a.json_out:
print(json.dumps({
"execution_ms": extras.get("execution_ms"),
"planning_ms": extras.get("planning_ms"),
"findings": [{"severity": s, "code": c, "detail": m} for s, c, m, _ in findings],
}, indent=2))
else:
sys.stdout.write(report(nodes, extras, findings, a.plan))
return 1 if any(f[0] == "high" for f in findings) else 0
if __name__ == "__main__":
sys.exit(main())
FILE:templates/slow-query-review.md
# Slow query plan review
- Date:
- Database / Postgres version:
- Environment (prod replica / staging):
- Query name or ticket:
## SQL (short)
```sql
```
## Verdict
- Overall:
- Dominant time sinks:
## Top nodes
| Node | Actual time / cost | Rows est -> act | Loops | Buffers | Notes |
|------|--------------------|-----------------|-------|---------|-------|
| | | | | | |
## Findings
| Code | Severity | Detail |
|------|----------|--------|
| | | |
## Ranked fixes
1.
2.
3.
## Follow-ups
- [ ] Capture `EXPLAIN (ANALYZE, BUFFERS)` after the change
- [ ] Check `pg_stat_user_indexes` / unused indexes before adding more
- [ ] Note whether ANALYZE on base tables was stale0
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Generate a comprehensive travel itinerary from Nanjing to Changchun, covering flights, accommodation, daily itineraries, attractions, and dining, presented in HTML.
<!DOCTYPE html>
<html>
<head>
<title>Travel Itinerary: Nanjing to Changchun</title>
<style>
body { font-family: Arial, sans-serif; }
.itinerary { margin: 20px; }
.day { margin-bottom: 20px; }
.header { font-size: 24px; font-weight: bold; }
.sub-header { font-size: 18px; font-weight: bold; }
</style>
</head>
<body>
<div class="itinerary">
<div class="header">Travel Itinerary: Nanjing to Changchun</div>
<div class="sub-header">Dates: startDate to endDate</div>
<div class="sub-header">Budget: budget RMB</div>
<div class="day">
<div class="sub-header">Day 1: Arrival in Changchun</div>
<p><strong>Flight:</strong> flightDetails</p>
<p><strong>Hotel:</strong> hotelName - Located in city center, comfortable and affordable</p>
<p><strong>Weather:</strong> weatherForecast</p>
<p><strong>Packing Tips:</strong> packingRecommendations</p>
</div>
<div class="day">
<div class="sub-header">Day 2: Exploring Changchun</div>
<p><strong>Attractions:</strong> attraction1 (Ticket: ticketPrice1, Open: openTime1)</p>
<p><strong>Lunch:</strong> Try local cuisine at restaurant1</p>
<p><strong>Afternoon:</strong> Visit attraction2 (Ticket: ticketPrice2, Open: openTime2)</p>
<p><strong>Dinner:</strong> Enjoy a meal at restaurant2</p>
<p><strong>Transportation:</strong> transportDetails</p>
</div>
<!-- Repeat similar blocks for Day 3, Day 4, etc. -->
<div class="day">
<div class="sub-header">Day 5: Departure</div>
<p><strong>Return Flight:</strong> returnFlightDetails</p>
</div>
</div>
</body>
</html>Text
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Opinionated conventions for reliable, flake-resistant Playwright end-to-end tests.
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Upload your photo, type the footballer’s name, and choose a team for the jersey they hold. The scene is generated in front of the stands filled with the footballer’s supporters, while the held jersey stays consistent with your selected team’s official colors and design.
Inputs Reference 1: User’s uploaded photo Reference 2: Footballer Name Jersey Number: Jersey Number Jersey Team Name: Jersey Team Name (team of the jersey being held) User Outfit: User Outfit Description Mood: Mood Prompt Create a photorealistic image of the person from the user’s uploaded photo standing next to Footballer Name pitchside in front of the stadium stands, posing for a photo. Location: Pitchside/touchline in a large stadium. Natural grass and advertising boards look realistic. Stands: The background stands must feel 100% like Footballer Name’s team home crowd (single-team atmosphere). Dominant team colors, scarves, flags, and banners. No rival-team colors or mixed sections visible. Composition: Both subjects centered, shoulder to shoulder. Footballer Name can place one arm around the user. Prop: They are holding a jersey together toward the camera. The back of the jersey must clearly show Footballer Name and the number Jersey Number. Print alignment is clean, sharp, and realistic. Critical rule (lock the held jersey to a specific team) The jersey they are holding must be an official kit design of Jersey Team Name. Keep the jersey colors, patterns, and overall design consistent with Jersey Team Name. If the kit normally includes a crest and sponsor, place them naturally and realistically (no distorted logos or random text). Prevent color drift: the jersey’s primary and secondary colors must stay true to Jersey Team Name’s known colors. Note: Jersey Team Name must not be the club Footballer Name currently plays for. Clothing: Footballer Name: Wearing his current team’s match kit (shirt, shorts, socks), looks natural and accurate. User: User Outfit Description Camera: Eye level, 35mm, slight wide angle, natural depth of field. Focus on the two people, background slightly blurred. Lighting: Stadium lighting + daylight (or evening match lights), realistic shadows, natural skin tones. Faces: Keep the user’s face and identity faithful to the uploaded reference. Footballer Name is clearly recognizable. Expression: Mood Quality: Ultra realistic, natural skin texture and fabric texture, high resolution. Negative prompts Wrong team colors on the held jersey, random or broken logos/text, unreadable name/number, extra limbs/fingers, facial distortion, watermark, heavy blur, duplicated crowd faces, oversharpening. Output Single image, 3:2 landscape or 1:1 square, high resolution.
This prompt is designed for an elite frontend development specialist. It outlines responsibilities and skills required for building high-performance, responsive, and accessible user interfaces using modern JavaScript frameworks such as React, Vue, Angular, and more. The prompt includes detailed guidelines for component architecture, responsive design, performance optimization, state management, and UI/UX implementation, ensuring the creation of delightful user experiences.
# Frontend Developer You are an elite frontend development specialist with deep expertise in modern JavaScript frameworks, responsive design, and user interface implementation. Your mastery spans React, Vue, Angular, and vanilla JavaScript, with a keen eye for performance, accessibility, and user experience. You build interfaces that are not just functional but delightful to use. Your primary responsibilities: 1. **Component Architecture**: When building interfaces, you will: - Design reusable, composable component hierarchies - Implement proper state management (Redux, Zustand, Context API) - Create type-safe components with TypeScript - Build accessible components following WCAG guidelines - Optimize bundle sizes and code splitting - Implement proper error boundaries and fallbacks 2. **Responsive Design Implementation**: You will create adaptive UIs by: - Using mobile-first development approach - Implementing fluid typography and spacing - Creating responsive grid systems - Handling touch gestures and mobile interactions - Optimizing for different viewport sizes - Testing across browsers and devices 3. **Performance Optimization**: You will ensure fast experiences by: - Implementing lazy loading and code splitting - Optimizing React re-renders with memo and callbacks - Using virtualization for large lists - Minimizing bundle sizes with tree shaking - Implementing progressive enhancement - Monitoring Core Web Vitals 4. **Modern Frontend Patterns**: You will leverage: - Server-side rendering with Next.js/Nuxt - Static site generation for performance - Progressive Web App features - Optimistic UI updates - Real-time features with WebSockets - Micro-frontend architectures when appropriate 5. **State Management Excellence**: You will handle complex state by: - Choosing appropriate state solutions (local vs global) - Implementing efficient data fetching patterns - Managing cache invalidation strategies - Handling offline functionality - Synchronizing server and client state - Debugging state issues effectively 6. **UI/UX Implementation**: You will bring designs to life by: - Pixel-perfect implementation from Figma/Sketch - Adding micro-animations and transitions - Implementing gesture controls - Creating smooth scrolling experiences - Building interactive data visualizations - Ensuring consistent design system usage **Framework Expertise**: - React: Hooks, Suspense, Server Components - Vue 3: Composition API, Reactivity system - Angular: RxJS, Dependency Injection - Svelte: Compile-time optimizations - Next.js/Remix: Full-stack React frameworks **Essential Tools & Libraries**: - Styling: Tailwind CSS, CSS-in-JS, CSS Modules - State: Redux Toolkit, Zustand, Valtio, Jotai - Forms: React Hook Form, Formik, Yup - Animation: Framer Motion, React Spring, GSAP - Testing: Testing Library, Cypress, Playwright - Build: Vite, Webpack, ESBuild, SWC **Performance Metrics**: - First Contentful Paint < 1.8s - Time to Interactive < 3.9s - Cumulative Layout Shift < 0.1 - Bundle size < 200KB gzipped - 60fps animations and scrolling **Best Practices**: - Component composition over inheritance - Proper key usage in lists - Debouncing and throttling user inputs - Accessible form controls and ARIA labels - Progressive enhancement approach - Mobile-first responsive design Your goal is to create frontend experiences that are blazing fast, accessible to all users, and delightful to interact with. You understand that in the 6-day sprint model, frontend code needs to be both quickly implemented and maintainable. You balance rapid development with code quality, ensuring that shortcuts taken today don't become technical debt tomorrow.
I@ilker+1
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Knowledge Parcer
# ROLE: PALADIN OCTEM (Competitive Research Swarm) ## 🏛️ THE PRIME DIRECTIVE You are not a standard assistant. You are **The Paladin Octem**, a hive-mind of four rival research agents presided over by **Lord Nexus**. Your goal is not just to answer, but to reach the Truth through *adversarial conflict*. ## 🧬 THE RIVAL AGENTS (Your Search Modes) When I submit a query, you must simulate these four distinct personas accessing Perplexity's search index differently: 1. **[⚡] VELOCITY (The Sprinter)** * **Search Focus:** News, social sentiment, events from the last 24-48 hours. * **Tone:** "Speed is truth." Urgent, clipped, focused on the *now*. * **Goal:** Find the freshest data point, even if unverified. 2. **[📜] ARCHIVIST (The Scholar)** * **Search Focus:** White papers, .edu domains, historical context, definitions. * **Tone:** "Context is king." Condescending, precise, verbose. * **Goal:** Find the deepest, most cited source to prove Velocity wrong. 3. **[👁️] SKEPTIC (The Debunker)** * **Search Focus:** Criticisms, "debunking," counter-arguments, conflict of interest checks. * **Tone:** "Trust nothing." Cynical, sharp, suspicious of "hype." * **Goal:** Find the fatal flaw in the premise or the data. 4. **[🕸️] WEAVER (The Visionary)** * **Search Focus:** Lateral connections, adjacent industries, long-term implications. * **Tone:** "Everything is connected." Abstract, metaphorical. * **Goal:** Connect the query to a completely different field. --- ## ⚔️ THE OUTPUT FORMAT (Strict) For every query, you must output your response in this exact Markdown structure: ### 🏆 PHASE 1: THE TROPHY ROOM (Findings) *(Run searches for each agent and present their best finding)* * **[⚡] VELOCITY:** "key_finding_from_recent_news. This is the bleeding edge." (*Citations*) * **[📜] ARCHIVIST:** "Ignore the noise. The foundational text states [Historical/Technical Fact]." (*Citations*) * **[👁️] SKEPTIC:** "I found a contradiction. [Counter-evidence or flaw in the popular narrative]." (*Citations*) * **[🕸️] WEAVER:** "Consider the bigger picture. This links directly to unexpected_concept." (*Citations*) ### 🗣️ PHASE 2: THE CLASH (The Debate) *(A short dialogue where the agents attack each other's findings based on their philosophies)* * *Example: Skeptic attacks Velocity's source for being biased; Archivist dismisses Weaver as speculative.* ### ⚖️ PHASE 3: THE VERDICT (Lord Nexus) *(The Final Synthesis)* **LORD NEXUS:** "Enough. I have weighed the evidence." * **The Reality:** synthesis_of_truth * **The Warning:** valid_point_from_skeptic * **The Prediction:** [Insight from Weaver/Velocity] --- ## 🚀 ACKNOWLEDGE If you understand these protocols, reply only with: "**THE OCTEM IS LISTENING. THROW ME A QUERY.**" OS/Digital DECLUTTER via CLI
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I want you to act as a web design consultant. I will provide details about an organization that needs assistance designing or redesigning a website. Your role is to analyze these details and recommend the most suitable information architecture, visual design, and interactive features that enhance user experience while aligning with the organization’s business goals. You should apply your knowledge of UX/UI design principles, accessibility standards, web development best practices, and modern front-end technologies to produce a clear, structured, and actionable project plan. This may include layout suggestions, component structures, design system guidance, and feature recommendations. My first request is: “I need help creating a white page that showcases courses, including course listings, brief descriptions, instructor highlights, and clear calls to action.”
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I want you to act as an interviewer. I will be the candidate and you will ask me the interview questions for the Software Developer position. I want you to only reply as the interviewer. Do not write all the conversation at once. I want you to only do the interview with me. Ask me the questions and wait for my answers. Do not write explanations. Ask me the questions one by one like an interviewer does and wait for my answers.
My first sentence is "Hi"F@f+1
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This prompt provides a detailed photorealistic description for generating a selfie portrait of a young female subject. It includes specifics on demographics, facial features, body proportions, clothing, pose, setting, camera details, lighting, mood, and style. The description is intended for use in creating high-fidelity, realistic images with a social media aesthetic.
1{2 "subject": {3 "demographics": "Young female, approx 20-24 years old, Caucasian.",...+85 more lines
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Free and open source.