Opinionated conventions for building reliable LLM-powered apps, RAG and agents. 来源：https://prompts.chat/prompts/cmvnjn4s80001lb08myabe3oq
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. 来源：https://prompts.chat/prompts/cmny4rpjq0003jy08nopcvben
A read-only maintenance audit workflow for Agent Skills. Reviews existing skills for stale or version-sensitive guidance, trigger conflicts, overlap, broken references, unsafe helper behavior, specification drift, context bloat, and outdated technology assumptions. Verifies material freshness claims against authoritative sources and reports only evidence-backed maintenance findings without modifying the audited skills. 来源：https://prompts.chat/prompts/cmuwbt3p80004qs06iif6z1q1
ExpertLens-Lite turns any AI into a genuine expert thinking partner. It diagnoses the real problem, adapts reasoning to the domain, self-audits before answering, gives real recommendations instead of hedged lists, and can consult other AI models for tougher calls. Platform-agnostic — any LLM. 来源：https://prompts.chat/prompts/cmtihooyr0001l504w9yfw9e6
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. 来源：https://prompts.chat/prompts/cmvqd0g7l000ela08fer31y84
