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Review Pr

  • 1 installs
  • 2 repo stars
  • Updated April 17, 2026
  • ai-builder-team/ai-builder-plugin-marketplace

review-pr is a Claude Code skill that runs a multi-agent code review on a pull request and posts validated, concise inline comments to GitHub after user approval.

About

review-pr is a Claude Code skill that runs a thorough, multi-agent code review on a pull request and posts concise inline comments to GitHub. It launches parallel review agents covering code quality, silent failures, test coverage, comment accuracy and type design, then validates each finding against the actual source. A developer uses it to get a senior-engineer-style review with casual inline comments. It requires explicit user approval before any comment is posted to GitHub.

  • Runs a multi-agent code review on a PR and posts concise inline GitHub comments
  • Launches parallel review agents for quality, silent failures, tests, comments and types
  • Validates every finding against source and requires user approval before posting

Review Pr by the numbers

  • 1 all-time installs (skills.sh)
  • Ranked #984 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Data as of Jul 7, 2026 (Skillselion catalog sync)
At a glance

review-pr capabilities & compatibility

Free; requires the gh CLI and GitHub access, no separate API keys.

Capabilities
code review · pr review · inline comments · silent failure detection · test coverage analysis
Works with
github
Use cases
code review
Pricing
Free
From the docs

What review-pr says it does

Deep PR review with inline GitHub comments. Launches parallel review agents, validates findings, drafts casual inline comments, and posts after user approval.
SKILL.md
**This is the most important step.** For every finding:
SKILL.md
npx skills add https://github.com/ai-builder-team/ai-builder-plugin-marketplace --skill review-pr

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Listed on Skillselion
Installs1
repo stars2
Last updatedApril 17, 2026
Repositoryai-builder-team/ai-builder-plugin-marketplace

What it does

Run a multi-agent code review on a pull request and post validated inline GitHub comments after approval.

Who is it for?

Developers who want a validated senior-engineer-style PR review with inline GitHub comments.

Skip if: Auto-posting reviews without human sign-off; it never posts before explicit approval.

When should I use this skill?

you want a deep, multi-agent review of a pull request with inline comments.

What you get

A validated set of concise inline comments and a short review body are posted to the PR after user approval.

  • Validated inline PR comments with file:line targets
  • A short PR review body with a COMMENT or REQUEST_CHANGES action

By the numbers

  • Launches 5 parallel review agents
  • Caps output at 5-7 inline comments

Files

SKILL.mdMarkdownGitHub ↗

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:

Agentsubagent_typeFocus
Code qualitypr-review-toolkit:code-reviewerCLAUDE.md compliance, bugs, security
Error handlingpr-review-toolkit:silent-failure-hunterSilent failures, swallowed exceptions
Test coveragepr-review-toolkit:pr-test-analyzerCoverage gaps, test quality
Commentspr-review-toolkit:comment-analyzerAccuracy, staleness, misleading docs
Typespr-review-toolkit:type-design-analyzerType 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-...

Related skills

FAQ

Does it post automatically?

No. It presents all inline comments and the review body for approval and only posts after explicit greenlight.

How does it avoid false positives?

It reads the actual source at each reported location and drops any finding that is incorrect, already handled, or not worth commenting on.

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