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.
git 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-analysisSKILL.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. `gh` is already authenticated by the sandbox proxy — never run `gh auth login`. ## 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 pr list --repo <owner>/<repo> --state merged --limit 30 gh api repos/<owner>/<repo>/pulls/<PR_NUMBER>/reviews gh api repos/<owner>/<repo>/pulls/<PR_NUMBER>/comments 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`.
Nightly refinement of an existing per-repo review-style prompt using this reviewer's own finding outcomes. Read confirmed (resolved-by-commit / thumbs-up) and dismissed (thumbs-down) findings, promote the bug patterns the team actually fixes, demote the false-positive patterns, reconcile against the current prompt, and save the refined version. Use this once outcomes exist; use bootstrap-repo-analysis for a cold-start repo.
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
Author the HTML for a plan artifact, dashboard iframe, or Slack attachment — structure, design plan, available runtime, theming, and craft. Read this before writing HTML for save_plan, output_iframe, or slack_attach_html.