Agent Skills › langchain-ai/open-swe › bootstrap-repo-analysis

bootstrap-repo-analysis

GitHub

用于为新仓库首次生成代码审查提示词。通过爬取历史合并PR的评论，提取团队审查规范与特定技术栈的缺陷模式，构建初始审查策略。

agent/skills/bootstrap-repo-analysis/SKILL.md langchain-ai/open-swe

Trigger Scenarios

新仓库冷启动审查配置无历史审查结果的仓库初始化

Install

npx skills add langchain-ai/open-swe --skill bootstrap-repo-analysis -g -y

More Options

Non-standard path

npx skills add https://github.com/langchain-ai/open-swe/tree/main/agent/skills/bootstrap-repo-analysis -g -y

Use without installing

npx skills use langchain-ai/open-swe@bootstrap-repo-analysis

指定 Agent (Claude Code)

npx skills add langchain-ai/open-swe --skill bootstrap-repo-analysis -a claude-code -g -y

安装 repo 全部 skill

npx skills add langchain-ai/open-swe --all -g -y

预览 repo 内 skill

npx skills add langchain-ai/open-swe --list

SKILL.md

Frontmatter

{
    "name": "bootstrap-repo-analysis",
    "description": "First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes."
}

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.