Skill10k estrellas del repoactualizado today
bootstrap-repo-analysis
**bootstrap-repo-analysis** crawls a repository's merged pull request history using the GitHub CLI to extract team-specific code review norms and patterns, cataloguing at least eight substantive human reviewer comments to identify what the team routinely flags, ignores, and how they calibrate severity. Use this skill once when establishing reviews for a new repository with no prior analysis; switch to continual-learning after the reviewer accumulates finding outcomes on that repo.
Instalar en Claude Code
Copiargit clone --depth 1 https://github.com/langchain-ai/open-swe /tmp/bootstrap-repo-analysis && cp -r /tmp/bootstrap-repo-analysis/agent/skills/bootstrap-repo-analysis ~/.claude/skills/bootstrap-repo-analysisDespués abre una sesión nueva de Claude Code; el skill carga automáticamente.
Definición
SKILL.md
# Bootstrap repo analysis You are writing the **first** review-style prompt for the repository named in the system prompt. There is no outcomes history yet, so your signal comes entirely from the repo's own historical PR review feedback. Do not call `read_finding_outcomes` in this mode — it will be empty. Always invoke gh as: `GH_TOKEN=dummy gh <command>`. ## 1. Research (required) Browse historical **merged** PR review feedback until you have catalogued at least **8 substantive human** review comments (skip `[bot]` accounts and obvious automation like codecov / dependabot). Useful commands: ``` GH_TOKEN=dummy gh pr list --repo <owner>/<repo> --state merged --limit 30 GH_TOKEN=dummy gh api repos/<owner>/<repo>/pulls/<PR_NUMBER>/reviews GH_TOKEN=dummy gh api repos/<owner>/<repo>/pulls/<PR_NUMBER>/comments GH_TOKEN=dummy gh api repos/<owner>/<repo>/issues/<PR_NUMBER>/comments ``` If the first batch is sparse, raise `--limit` or walk older PR numbers. The user message may include **preloaded samples** — verify and extend them with `gh`, don't just trust them. Identify the top ~5 human reviewers by volume and note their phrasing, what severity they assign, and what they routinely ignore. ## 2. Extract concrete, repo-specific patterns The highest-value content is a **bug taxonomy tied to this repo's stack** — concrete "hunt for X" rules a maintainer would catch on first read — plus a calibrated "do not flag" list. Pair each pattern with the failure mode and, where you saw it, the kind of diff that triggered it. Avoid generic advice that would apply to any repo. Cover: - What the team routinely flags vs. skips (paraphrased patterns, not invented quotes) - Severity calibration tied to user-visible / runtime consequence - Tone and test expectations - Repo-specific conventions (frameworks, repository/data-access boundaries, naming) - Anti-patterns the reviewers here deliberately avoid Stay aligned with the reviewer-agent themes in the system prompt (high-signal, diff-anchored defects — not nits). ## 3. Save Only after real research, call `save_review_style_prompt` once with: - `custom_prompt`: 400–1200 words teaching the reviewer this repo's norms. - `analysis_summary`: 2–4 sentences for the dashboard. - `top_reviewers` (comma-separated logins), `prs_sampled`, `reviews_sampled`. Do **not** save a generic guide after one or two commands. Only after ~25+ merged PRs with zero human feedback may you save a short, conservative guide — and say so in `analysis_summary`.