Convert PDF files into Markdown with precision. This AI tool ensures the Markdown output mirrors the original PDF content, maintaining structure and formatting, while excluding specific logos. Perfect for creating documentation or sharing formatted content on platforms like GitHub.
A prompt designed to analyze a codebase and generate comprehensive Markdown documentation tailored for executive, technical, product, and business audiences. It guides an AI to extract high-level system purpose, architecture, key components, workflows, product features, business domains, and limitations, producing an onboarding and discovery document suitable for both technical and non-technical stakeholders.
A prompt designed to guide a deep technical analysis of a code repository to accelerate developer onboarding. It instructs an AI to analyze the entire codebase and generate a structured Markdown document covering architecture, technology stack, key components, execution and data flows, integrations, testing, security, and build/deployment, serving as a technical reference guide.
Analyze individual sections of a PDF to create clear and complete MATLAB code, along with explanations and a presentation.
Key sections: PDF Type detection — Vector vs Scanned, different extraction strategy for each Step-by-step workflow — 6 steps from file organization to discrepancy report Visual symbol table — per ELV system (CCTV, FAS, ACS, PA, SC, IPTV, etc.) Best practices — legend-first, one device type at a time, grid method, typical floor check Confidence rating — High / Medium / Low per drawing
High-end Prompt Engineering & Prompt Refiner skill. Transforms raw or messy user requests into concise, token-efficient, high-performance master prompts for systems like GPT, Claude, and Gemini. Use when you want to optimize or redesign a prompt so it solves the problem reliably while minimizing tokens.
Profiles CSV and spreadsheet exports before you trust them: infers column types and measures missing values, duplicates, mixed types, outliers, date format chaos, and key uniqueness with a tested Python script, then writes a prioritized data quality report with safe fixes.
Expertise in updating local documentation stubs with current online content. Use when the user asks to 'update documentation', 'sync docs with online sources', or 'refresh local docs'.
Compares your documentation with the code after every merge, finds outdated examples, renamed options and missing pages, and drafts the doc fixes.
Watches your upload pace, posting hours and workload signs, and warns you before you burn out, with a plan to batch content and take a real break.
Takes one long piece (a video, podcast or blog post) and turns it into a thread, a LinkedIn post, short-video hooks and a newsletter section, each in the right tone for its platform.
