Opinionated conventions for building retrieval-augmented generation pipelines that stay accurate and testable.
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
Explains every line of a .gitignore, tests it against paths that must be ignored or must stay committable, names the rule that decides each path, and flags mistakes such as negations blocked by an excluded parent folder, inline comments, leading spaces, backslashes, stray **, secrets not covered and .env.example ignored. Returns a fixed file. Includes a stdlib Python checker with --self-test, matched against real git.
Catches quality drops when you change an AI feature's prompt or model: runs your test cases, compares old and new answers side by side, and tracks cost and speed.
