ai-agent-skills

Clarify requirements before implementing. Do not use automatically, only when invoked explicitly.

Use when checking the overall health of a skills library. Run doctor, validate, check for stale skills, and verify generated docs are in sync.

Backend API design, database architecture, microservices patterns, and test-driven development. Use for designing APIs, database schemas, or backend system architecture.

Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.

Use when exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.

Use when regenerating README.md and WORK_AREAS.md in a managed library workspace. Always dry-run first to preview changes.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Writing effective code documentation - API docs, README files, inline comments, and technical guides. Use for documenting codebases, APIs, or writing developer guides.

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

Use when building a managed team skills library for a real stack. Map work to shelves, browse before curating, write meaningful `whyHere` notes, and create a starter pack once the first pass is solid.

Database schema design, optimization, and migration patterns for PostgreSQL, MySQL, and NoSQL databases. Use for designing schemas, writing migrations, or optimizing queries.

Use when installing skills from a shared ai-agent-skills library repo. Inspect with `--list` first, prefer `--collection`, and preview with `--dry-run` before installing.

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

Use when moving skills between library workspaces or upgrading from a personal library to a team library. Export from one workspace, import into another.

Use when evaluating whether a skill belongs in a library. Preview content, check frontmatter, validate structure, and decide whether to keep, curate, or remove.

Use when a managed library is ready to publish to GitHub and hand to teammates as an install command. Run the GitHub publishing steps, then return the exact shareable install command.

Use when syncing or updating previously installed skills to their latest version. Always dry-run updates before applying, and check for breaking changes.

Verify claims, quotations, citations and references against live primary sources, and produce a reproducible audit record instead of an assertion. Use when asked to: check whether a quotation, verse, statistic, date, attribution or citation is accurate; verify references before publishing a report, thesis, article, slide deck or legal filing; confirm a text against an authoritative database; cross-check a claim across independent sources; build a citation register or verification appendix; find out whether two datasets agree; detect a misattributed or fabricated quotation; or audit a document's existing citations. Source-agnostic by design, with working providers for REST/JSON APIs, scraped HTML, bulk corpora, bibliographic catalogues (OpenLibrary), DOI resolution (Crossref) and reference works (Wikipedia) -- including scriptural tiers (Qur'an.com, sunnah.com and an independent hadith corpus) with full diacritic handling for Arabic, Hebrew, Greek and any script with optional marks. Cov