Examen de PR approfondi

Examen détaillé d'une pull request avec commentaires inline. Utilise des agents parallèles pour valider la qualité, la couverture de tests et les commentaires, puis rédige des commentaires concis.

Spar Skills Guide Bot
DeveloppementIntermédiaire
1022/07/2026
Claude Code
#pr-review#code-review#github#pull-request#multi-agent

Recommandé pour


name: review-pr description: Deep PR review with inline GitHub comments. Launches parallel review agents, validates findings, drafts casual inline comments, and posts after user approval. tools: Task, Read, Glob, Grep, Bash, AskUserQuestion color: Orange

Purpose

Run a thorough, multi-agent code review on a pull request and post concise inline comments to GitHub. The review should feel like it came from a senior engineer who actually read the code -- not a linter or AI tool.

When to use: "review this PR", "review PR #123", "give me a code review", or just /review-pr

Instructions

Step 1: Identify the PR

Parse $ARGUMENTS for a PR number. If blank, detect from the current branch:

gh pr view --json number,title,state,headRefOid

Then get the scope:

git diff --name-only main...HEAD
git diff --stat main...HEAD -- '*.py' '*.ts' '*.tsx' '*.js'

Tell the user the PR number, title, and file count before proceeding.

Step 2: Launch review agents in parallel

Launch ALL of the following simultaneously using the Task tool with run_in_background: true:

| Agent | subagent_type | Focus | |-------|--------------|-------| | Code quality | pr-review-toolkit:code-reviewer | CLAUDE.md compliance, bugs, security | | Error handling | pr-review-toolkit:silent-failure-hunter | Silent failures, swallowed exceptions | | Test coverage | pr-review-toolkit:pr-test-analyzer | Coverage gaps, test quality | | Comments | pr-review-toolkit:comment-analyzer | Accuracy, staleness, misleading docs | | Types | pr-review-toolkit:type-design-analyzer | Type invariants, design quality |

Skip the type-design-analyzer if no new types/schemas are introduced.

Each agent prompt should include:

  • The PR number and branch
  • Changed files relevant to that agent's specialty
  • Instruction to use git diff main...HEAD

Step 3: Collect results

Wait for all agents to complete. Read each output.

Step 4: Validate findings

This is the most important step. For every finding:

  • Read the actual source code at the reported location
  • Confirm the issue exists as described
  • Drop anything that's incorrect, already handled, or not worth commenting on

False positives destroy credibility. Be ruthless about cutting.

Step 5: Draft inline comments

Write concise, casual comments. Follow these rules:

Tone:

  • Like a thoughtful colleague, not a linter
  • 1-2 sentences. Get to the point.
  • No corporate speak. No "I would suggest..." -- just say what's up
  • "Nit:" for non-blocking. "Suggestion:" for substantial with rationale
  • "Not blocking" where appropriate

Content:

  • Focus on what matters: bugs, silent failures, misleading behavior, dead code
  • Skip pure style nits
  • Short code suggestions are fine (3-5 lines max)
  • Group related issues where it makes sense

Volume:

  • 5-7 comments max. Quality over quantity.
  • Not every finding deserves a comment

Step 6: Draft PR review body

Short and human. Structure:

  • 2-3 sentences acknowledging what's good (genuinely)
  • Bullet the main concerns (2-4 bullets max)
  • One-line overall assessment
  • Review action: COMMENT unless there are truly blocking issues (REQUEST_CHANGES)

Step 7: Present for approval

CRITICAL: Do NOT post to GitHub yet.

Show the user:

  • All inline comments with file:line targets
  • The review body
  • The review action

Ask if they want to adjust anything.

Step 8: Post the review

Only after explicit approval, post as a single GitHub review:

COMMIT=$(gh pr view <PR> --json headRefOid -q '.headRefOid')
OWNER=$(gh pr view <PR> --json headRepositoryOwner -q '.headRepositoryOwner.login')
REPO=$(gh pr view <PR> --json headRepository -q '.headRepository.name')

Build JSON payload:

{
  "commit_id": "<COMMIT>",
  "event": "COMMENT",
  "body": "<review body>",
  "comments": [
    {
      "path": "relative/path/to/file.py",
      "line": 42,
      "side": "RIGHT",
      "body": "Comment text"
    }
  ]
}

Post via:

gh api repos/$OWNER/$REPO/pulls/<PR>/reviews \
  --method POST --input /tmp/pr_review.json \
  --jq '{ id: .id, state: .state, html_url: .html_url }'

Return the review URL.


Anti-Patterns

  • Posting without approval -- Never. The user must greenlight every comment.
  • Trusting agent output blindly -- Always validate against actual code.
  • Over-commenting -- 10 comments is too many. Prioritize.
  • Sounding like AI -- No "I would recommend considering..." Just say it.
  • Commenting on things you didn't verify -- If you didn't read the line, don't comment on it.
  • Mixing blocking and non-blocking -- Be clear about what actually needs fixing vs nice-to-have.

Example Session

User: /review-pr 1724

Claude: PR #1724 "feat(eval): implement complete evaluation framework"
        42 code files changed, +4512/-139 lines
        Launching 5 review agents in parallel...

        [agents complete]

        Validated 8 findings. Here are the proposed comments:

        Comment 1 - snapshot_utils.py:128
        > This empty except swallows JSON parse errors and then...

        Comment 2 - schemas.py:27
        > This DatasetError(Exception) collides with...

        [etc.]

        PR Review Body:
        > Solid work here -- the eval framework design is clean...

        Ready to post, or want to adjust anything?

User: Comment 3 is too long, trim it

Claude: [adjusts] Updated. Ready to post?

User: Ship it

Claude: Review posted: https://github.com/.../pull/1724#pullrequestreview-...
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