Pr reviewer

pr-reviewer

When to use

When the user asks to review / audit / comment / 评审 / 审查 a GitHub PR by URL or local diff. Trigger phrases include:

• "帮我 review 这个 PR"
• "review https://github.com/.../pull/123"
• "check this diff before I merge"

Not suitable for: bug-hunting (use Copilot Code Review), security scans (use CodeQL), runtime performance review.

Target users

• Open-source maintainers handling 5–20 PRs/week
• Small dev teams (3–8 engineers) without a dedicated reviewer
• Solo developers who want a second pair of eyes on style and structure

Estimated frequency: per-PR (high-frequency, daily for active repos).

Why this skill

Unlike GitHub Copilot Code Review (focuses on bugs) or CodeQL (focuses on security), pr-reviewer focuses on team-specific style and structural conventions — the kind of rules that are written down in your team handbook but no off-the-shelf tool knows about.

Quantified value: cuts the average human "first-pass review" from ~30 min to ~5 min per PR — saves about 25 min/PR × 10 PRs/week ≈ 4 h/week per reviewer.

Inputs

Field	Type	Required	Notes
pr_url	string	yes	e.g. https://github.com/owner/repo/pull/123
team_rules	string	optional	path to team convention markdown; defaults to references/default-rules.md
language	enum	optional	one of python, typescript, go; auto-detect if omitted

Cache key: SHA-256 of (pr_url, head_sha, team_rules_hash) — same PR + same head SHA reuses cached review without re-calling the LLM.

Workflow

1. Fetch diff: call scripts/fetch_pr.py with pr_url → returns unified diff + file list.
2. Static lint: run language-specific linter (ruff / eslint / golangci-lint) for objective issues.
3. Style review (LLM): feed diff + team_rules to the LLM, ask for ≤ 5 most impactful style issues with line refs.
4. Compose comments: merge linter issues + LLM issues into JSON, deduplicated by (file, line, message_first_30_chars).
5. Validate output: pass through scripts/validate_review.py against assets/review.schema.json; on fail, regenerate once, then escalate to user.
6. Output: structured JSON ready for gh pr review --comment or copy-paste into a GitHub review.

Outputs

{
  "summary": "string, ≤ 200 chars",
  "comments": [
    { "file": "path/to/file.py", "line": 42, "severity": "info|warn|error", "message": "string" }
  ],
  "approve": "comment | request_changes | approve"
}

Schema lives at assets/review.schema.json; comments[].severity must be one of the three enum values.

Dependencies

Name	Type	Paid?	Approx cost / call
GitHub API (read-only)	API	free (60 req/h unauth, 5000/h with token)	$0
ruff / eslint / golangci-lint	CLI	free	$0
Skill's underlying LLM provider	API	yes	~$0.005 / PR

Determinism

• Steps 1, 2, 5 are fully deterministic (scripts only).
• Step 3 (LLM) uses temperature=0.2; same diff + same rules produces ≈ same output across reruns.
• Step 4 deduplicates by (file, line, message_first_30_chars) so result order doesn't matter.

Failure handling

• GitHub API 403 / 404 → tell user "PR not accessible; check URL or auth token"; do not retry.
• Linter binary missing → skip step 2 with a warning, continue with LLM-only review.
• LLM output schema validation fails twice → return raw LLM output verbatim with a _schema_failed: true flag and surface the validation error to the user.
• Rate limit hit → exponential backoff up to 3 retries; if all fail, return the partial review with _partial: true.

Privacy

Diff content is sent to the LLM provider configured by the host (e.g. DeepSeek / Anthropic). Do NOT use this skill on PRs containing secrets, credentials, or internal-only code without first checking your provider's data retention policy. See references/privacy.md for per-provider notes. No PR data is persisted by this skill itself.

Example

# input
pr-reviewer review https://github.com/octocat/hello-world/pull/42

# output (truncated)
{
  "summary": "3 minor style issues; safe to merge after fixes.",
  "comments": [
    {"file": "src/utils.py", "line": 18, "severity": "warn",
     "message": "Function process_data has 4 params; consider grouping into a dataclass per team rule §2.3."},
    {"file": "src/utils.py", "line": 45, "severity": "info",
     "message": "Magic number 0.85 — extract to a named constant THRESHOLD."}
  ],
  "approve": "comment"
}

Files

• scripts/fetch_pr.py — GitHub API client for diff fetching
• scripts/validate_review.py — JSON schema validator
• references/default-rules.md — fallback team conventions if user doesn't supply one
• references/privacy.md — per-LLM-provider data handling notes (read before reviewing PRs containing secrets)
• assets/review.schema.json — JSON schema for output validation
• tests/sample_inputs.json — sample PR diffs for regression testing
• tests/expected_outputs.json — expected reviews for those diffs

Changelog

• 0.3.0 — added schema validation step, multi-language linter support
• 0.2.0 — switched to deterministic dedup; added cache key
• 0.1.0 — initial release (Python only)