mcpField
P
pr-review

pr-review

github.com/significant-gravitasTesting & quality187.8k67 installsUpdated 2d ago
Repository

About pr-review

A review skill for contributors to Significant-Gravitas/AutoGPT. It guides PR discovery, description and diff checks, and posting categorized inline feedback.

Key features

  1. 01Find PRs with gh
  2. 02Check Why/What/How descriptions
  3. 03Review code and security
  4. 04Check tests and project conventions
  5. 05Post tiered inline comments

Use cases

  1. 01Review a PR in AutoGPT
  2. 02Identify missing PR context
  3. 03Check changes for security and correctness

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/significant-gravitas/autogpt --skill pr-review

After installing, type/pr-reviewin Claude Code to confirm it loaded.

Details

From the project README

PR Review

Find the PR

gh pr list --head $(git branch --show-current) --repo Significant-Gravitas/AutoGPT
gh pr view {N}

Read the PR description

Before reading code, understand the why, what, and how from the PR description:

gh pr view {N} --json body --jq '.body'

Every PR should have a Why / What / How structure. If any of these are missing, note it as feedback.

Read the diff

gh pr diff {N}

Fetch existing review comments

Before posting anything, fetch existing inline comments to avoid duplicates:

gh api repos/Significant-Gravitas/AutoGPT/pulls/{N}/comments --paginate
gh api repos/Significant-Gravitas/AutoGPT/pulls/{N}/reviews

What to check

Description quality: Does the PR description cover Why (motivation/problem), What (summary of changes), and How (approach/implementation details)? If any are missing, request them — you can't judge the approach without understanding the problem and intent.

Correctness: logic errors, off-by-one, missing edge cases, race conditions (TOCTOU in file access, credit charging), error handling gaps, async correctness (missing await, unclosed resources).

Security: input validation at boundaries, no injection (command, XSS, SQL), secrets not logged, file paths sanitized (os.path.basename() in error messages).

Code quality: apply rules from backend/frontend CLAUDE.md files and root AGENTS.md, including its "User-facing copy" section (text a person reads says workflow, never agent or graph).

Architecture: DRY, single responsibility, modular functions. Security() vs Depends() for FastAPI auth. data: for SSE events, : comment for heartbeats. transaction=True for Redis pipelines.

Testing: edge cases covered, colocated *_test.py (backend) / __tests__/ (frontend), mocks target where symbol is used not defined, AsyncMock for async.

Output format

Every comment must be prefixed with 🤖 and a criticality badge:

TierBadgeMeaning
Blocker🔴 **Blocker**Must fix before merge
Should Fix🟠 **Should Fix**Important improvement
Nice to Have🟡 **Nice to Have**Minor suggestion
Nit🔵 **Nit**Style / wording

Example: 🤖 🔴 **Blocker**: Missing error handling for X — suggest wrapping in try/except.

Post inline comments

For each finding, post an inline comment on the PR (do not just write a local report):

# Get the latest commit SHA for the PR
COMMIT_SHA=$(gh api repos/Significant-Gravitas/AutoGPT/pulls/{N} --jq '.head.sha')

# Post an inline comment on a specific file/line
gh api repos/Significant-Gravitas/AutoGPT/pulls/{N}/comments \
  -f body="🤖 🔴 **Blocker**: <description>" \
  -f commit_id="$COMMIT_SHA" \
  -f path="<file path>" \
  -F line=<line number>

Comments

Markdown supported