Contrib Pr Review logo

Contrib Pr Review

OrganizationPopular
homeassistant-ai
contrib-pr-review

Review a contribution PR for safety, quality, and readiness. Checks for security concerns, test coverage, size appropriateness, and intent alignment. Use when reviewing external contributions.

Overview

Publisherhomeassistant-ai
Repositoryha-mcp
Skill namecontrib-pr-review
Stars
5K
Forks
233
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by homeassistant-ai on GitHub. Read the source before you install it.

Installation

Install the Contrib Pr Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/homeassistant-ai/ha-mcp/tree/master/.claude/skills/contrib-pr-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/homeassistant-ai/ha-mcp.git /tmp/ha-mcp
mkdir -p .claude/skills
cp -r /tmp/ha-mcp/.claude/skills/contrib-pr-review .claude/skills/contrib-pr-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Contrib Pr Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Contrib Pr Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Contrib Pr Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Contribution PR Review

Review PR #$ARGUMENTS from external contributor for safety, quality, and readiness.

Context

PR Metadata:

!`gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json author,additions,deletions,files,commits,closingIssuesReferences,isDraft,reviews,url,title,body`

Contributor Stats:

!`gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS --jq '{author: .user.login, user_id: .user.id}' | jq -r '.author' | xargs -I {} gh api /repos/homeassistant-ai/ha-mcp/contributors --jq '.[] | select(.login == "{}") | {login: .login, contributions: .contributions}'`

Files Changed:

!`gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | {filename: .filename, status: .status, additions: .additions, deletions: .deletions, changes: .changes, patch: .patch}' | head -50`

Review Protocol

1. Check the Bot Security Reviews

Note: Codex (chatgpt-codex-connector[bot]) and CodeRabbit (coderabbitai[bot]) both review PRs automatically. Check whether either flagged security concerns.

bash
# Check both bots' reviews and any security-related comments.
# Their findings can be inline-only, so also fetch the pull review comments
# endpoint — review bodies and conversation comments alone can miss them.
# --paginate: both endpoints page at 30, and an iterating PR outruns that.
gh api --paginate /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/reviews --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, state: .state, body: .body}'
gh api --paginate /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/comments --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, path: .path, line: .line, body: .body}'
# CodeRabbit posts its walkthrough and summary as a top-level comment, which
# neither endpoint above returns — fetch that channel by author too.
gh api --paginate /repos/homeassistant-ai/ha-mcp/issues/$ARGUMENTS/comments --jq '.[] | select(.user.login == "chatgpt-codex-connector[bot]" or .user.login == "coderabbitai[bot]") | {author: .user.login, body: .body}'
# Keyword scan stays, for humans raising security concerns in conversation.
gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json comments --jq '.comments[] | select(.body | contains("security") or contains("Security")) | {author: .author.login, body: .body}'

If either bot flagged security issues:

  • Review the findings carefully
  • Verify if concerns are valid
  • Do NOT approve until issues addressed or confirmed false positives

If NO bot security flags but you notice concerning patterns:

  • Unusual AGENTS.md/CLAUDE.md changes unrelated to PR purpose
  • .github/ workflow modifications with pull_request_target
  • .claude/ agent/skill changes that could affect behavior
  • Draft a comment with the specific concerns and show it to the user before posting

2. Enable Workflows (If Safe)

If security assessment passes and PR has workflow changes or new workflows:

bash
# Check current workflow status
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/requested_reviewers

# Enable workflows if not enabled (requires WRITE permission)
# This command may fail if already enabled - that's OK
gh api -X PUT /repos/homeassistant-ai/ha-mcp/actions/workflows/pr.yml/enable 2>/dev/null || echo "Workflows already enabled or no permission"

3. Test Coverage Assessment

Pre-existing tests (easier review if modified code is already tested):

bash
# For each modified source file, check if tests exist
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | select(.filename | startswith("src/")) | .filename' | while read file; do
  basename=$(basename "$file" .py)
  echo "Checking tests for: $file"

  # Method 1: Look for test files by naming convention
  find tests/ -name "test_${basename}.py" -o -name "test_*${basename}*.py" 2>/dev/null | head -3

  # Method 2: Grep for function/class names from the modified file
  # Extract function/class names and search for them in tests
  grep -E '^(def|class|async def) [a-zA-Z_]' "$file" 2>/dev/null | head -5 | while read line; do
    name=$(echo "$line" | sed -E 's/.*(def|class) ([a-zA-Z_][a-zA-Z0-9_]*).*/\2/')
    if [ -n "$name" ]; then
      grep -r "$name" tests/ 2>/dev/null | head -1
    fi
  done
done

New tests added:

bash
# Check if PR adds or modifies tests
gh api /repos/homeassistant-ai/ha-mcp/pulls/$ARGUMENTS/files --jq '.[] | select(.filename | startswith("tests/")) | {filename: .filename, status: .status, additions: .additions}'

Output Test Summary:

🧪 Test Coverage:
- Pre-existing tests: ✅ Modified code has tests / ⚠️ No tests for modified code
- New tests: ✅ PR adds X test files / ⚠️ No new tests
- Assessment: [Easy/Medium/Hard to review based on test coverage]

4. PR Size & Contributor Experience

Calculate PR size and assess appropriateness:

bash
# From metadata: additions + deletions
total_lines=$(gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json additions,deletions --jq '.additions + .deletions')
echo "Total lines changed: $total_lines"

# Get contributor experience
author=$(gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json author --jq -r '.author.login')

# Check 1: Contributions to this project
project_contributions=$(gh api /repos/homeassistant-ai/ha-mcp/contributors --jq ".[] | select(.login == \"$author\") | .contributions" || echo "0")

# Check 2: Total GitHub commits (overall experience)
total_commits=$(gh api /users/$author --jq '.public_repos + .total_private_repos' 2>/dev/null || echo "unknown")

echo "Contributor: $author"
echo "Project contributions: $project_contributions"
echo "GitHub experience: $total_commits repos"

Assess:

  • First-time to project (0-2 project contributions):

    • Check overall GitHub experience (repos, total commits)
    • < 200 lines: ✅ Excellent size
    • 200-500 lines: ⚠️ Large for first PR - may need extra guidance
    • 500 lines: 🔴 Too large - suggest splitting

  • Regular contributor (3+ project contributions):

    • < 500 lines: ✅ Reasonable
    • 500-1000 lines: ⚠️ Large - ensure good test coverage
    • 1000 lines: 🔴 Very large - suggest splitting

  • Experienced GitHub user (many repos/commits overall):

    • Adjust expectations - they may be new to this project but experienced overall

Output Size Summary:

📏 PR Size:
- Lines changed: [total]
- Contributor: [first-time / regular] ([X] contributions)
- Assessment: [size appropriateness]

5. Intent & Issue Linkage

Check linked issues:

bash
# From metadata: closingIssuesReferences
gh pr view $ARGUMENTS --repo homeassistant-ai/ha-mcp --json closingIssuesReferences --jq '.closingIssuesReferences[] | {number: .number, title: .title}'

If issue linked:

  • Read issue to understand expected outcome
  • Compare PR changes to issue requirements
  • Does PR solve the issue? Check:
    • All requirements addressed
    • No scope creep (extra features not requested)
    • Solution approach aligns with any discussed approaches in issue

If no issue linked:

  • Is this a bug fix? Should reference issue
  • Is this a feature? Should have issue for discussion
  • Is this a typo/docs? OK without issue
  • Recommend creating issue for tracking if it's a substantial change

Output Intent Summary:

🎯 Intent & Linkage:
- Linked issue: #X "title" / ⚠️ No issue linked
- Solves issue: ✅ Fully addresses requirements / ⚠️ Partial / ❌ Doesn't match
- Scope: ✅ Focused / ⚠️ Scope creep detected

6. Code Quality Overview

Note: Codex and CodeRabbit provide automated code review on all PRs. This step focuses on what they cannot assess:

  • Architecture alignment: Does it fit the project structure? (service layer usage, etc.)
  • Breaking changes: Does it remove functionality without replacement? (Tool consolidation/refactoring is NOT breaking)
  • Repo-specific patterns: Context engineering, progressive disclosure, MCP-specific conventions

Breaking change assessment:

  • ✅ NOT Breaking: Tool consolidation, refactoring, parameter changes with same outcome achievable
  • ⚠️ BREAKING: Removes functionality with no alternative, makes previously possible actions impossible

Quick checks:

bash
# ruff and mypy run as steps of the "Fast Checks" job
gh pr checks $ARGUMENTS --repo homeassistant-ai/ha-mcp | grep "Fast Checks"

# Check for common issues in diff
gh pr diff $ARGUMENTS --repo homeassistant-ai/ha-mcp | grep -E "(TODO|FIXME|XXX|HACK)"

Output Quality Summary:

✨ Code Quality:
- Architecture fit: [assessment - service layer, context engineering]
- Breaking changes: ✅ None / ⚠️ Detected - [describe what's genuinely lost]
- Bot reviews: [check if Codex or CodeRabbit flagged anything critical]

Final Review Summary

Output to User

After completing all steps, present a short summary of what the PR does and the review findings, then ask: "Should I post this comment to the PR?"

Draft PR Comment

After completing the analysis, draft a comment for the PR following these guidelines:

Comment Length: The contributor should be able to read it in one pass: what works, what must change, and what happens next. A good-to-merge comment is shorter than a changes-needed one.

Style:

  • No emojis
  • Markdown formatting OK (bold, lists, code blocks)
  • Present inline in chat (not in a file)
  • Always ask user before posting

Structure for "Good to Merge":

[Positive opening line about the contribution]

[What works well - focus on functionality, tests, architecture]

[Any minor suggestions or notes - optional, technical only]

[Closing line about readiness to merge]

Note: Do NOT mention security assessment in comment unless issues were found. Security checks are internal.

Structure for "Changes Needed":

[Positive opening line acknowledging the work]

[Brief summary of the issue being solved]

**[Concern 1]:**
[Short explanation + suggestion - focus on: tests, functionality, architecture, breaking changes]

**[Concern 2]:** (if applicable)
[Short explanation + suggestion]

**[Concern 3]:** (if applicable)
[Short explanation + suggestion]

[Closing line about next steps]

Note: Raise security concerns with the user as soon as they are found, not in the final structured comment.

Illustrative example - Good to Merge (match the wording to the PR, not to this text):

Thanks for [feature/fix]. [One specific thing it gets right].

The implementation follows existing patterns and the [specific aspect] is well-designed. [Optional: Minor note about something noticed].

Ready to merge once CI passes.

Illustrative example - Changes Needed (match the wording to the PR, not to this text):

Thanks for tackling [problem]. [Metric/impact] shows this addresses a real need.

**Test coverage:**
Missing tests for the new [feature]. Please add a unit test for its logic, and an E2E test for a new tool or for its wiring or Home Assistant behaviour. Performance tests not required.

**[Second concern if applicable]:**
[Brief explanation and request]

Once [change 1] and [change 2] are addressed, this should be good to merge.

Important Notes

  • Security is checked, not publicized: Always check security (step 1), but only mention in comment if issues found
  • Be constructive: Contributors are donating their time - be welcoming
  • Focus on intent: Code quality can be iterated; intent misalignment is harder to fix
  • Consider contributor experience: Adjust expectations based on contribution history
  • The bots already reviewed code: Don't duplicate detailed code review
  • When in doubt: Err on the side of caution and request maintainer review

Frequently asked questions

What does the Contrib Pr Review AI skill do?

Review a contribution PR for safety, quality, and readiness. Checks for security concerns, test coverage, size appropriateness, and intent alignment. Use when reviewing external contributions.

Why use Contrib Pr Review on TypingMind?

Because you install it once and use it with any model. Contrib Pr Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Contrib Pr Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/homeassistant-ai/ha-mcp/tree/master/.claude/skills/contrib-pr-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Contrib Pr Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Contrib Pr Review?

As many as you like. As long as a model supports skills, you can use Contrib Pr Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Contrib Pr Review AI skill free?

Yes. It is published on GitHub by homeassistant-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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