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Post Code Review To Pr

Organization
testdouble
post-code-review-to-pr

Run a full pull request review and post review comments directly to the current branch's GitHub PR. Requires the gh CLI to be installed and a PR to already exist for the current branch. Use when you want review feedback posted to GitHub as PR comments. For local code review without posting to GitHub, use code-review instead. Does not write or update PR descriptions — use update-pr-description for that.

Overview

Publishertestdouble
Repositoryhan
Skill namepost-code-review-to-pr
Stars
320
Forks
26
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by testdouble on GitHub. Read the source before you install it.

Installation

Install the Post Code Review To Pr 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/testdouble/han/tree/main/han-github/skills/post-code-review-to-pr
  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/testdouble/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/han-github/skills/post-code-review-to-pr .claude/skills/post-code-review-to-pr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Post Code Review To Pr 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 Post Code Review To Pr 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 Post Code Review To Pr 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.

When running a PR code review, follow the process outlined here.

Pre-requisites

  • gh CLI: !which gh 2>/dev/null || echo "not installed"
  • jq: !which jq 2>/dev/null || echo "not installed"

If gh is not found, inform the user it must be installed and configured; if jq is not found, inform the user it must be installed. In either case, immediately stop.

Project Context

  • current branch: !git branch --show-current 2>/dev/null || echo unknown
  • default branch: !git symbolic-ref --short refs/remotes/origin/HEAD 2>/dev/null || echo unknown
  • changed files: !gh pr diff --name-only 2>/dev/null || echo "no pr"
  • personal config directory: !bash "${CLAUDE_PLUGIN_ROOT}/scripts/han-config-dir.sh" 2>/dev/null || echo "$HOME/.claude"
  • project .han/config.md: !cat .han/config.md 2>/dev/null || echo ""

As your first action, use the Read tool on .han/config.md inside the personal config directory path above. A read that returns no file is no personal configuration: continue silently. When that file or the project .han/config.md probe supplies content, apply it per config-rule.md, which governs precedence between the two files, relative-path resolution, and what to do with a file that reads but cannot be used.

Step 1: Validate PR State

If changed files is empty or reads no pr, or gh pr view --json number,url fails, inform the user no reviewable PR exists for the current branch and stop.

Step 2: Run Code Review

Invoke the /code-review skill to perform the full code review. Pass along any user-provided focus areas or context from the original arguments.

/code-review writes its report to a file and names the path in its closing message; it does not print the review into the conversation. Capture that path — Step 3 reads the report from it. After /code-review completes, proceed immediately to Step 3 — do not stop here.

Step 3: Offer to Post Review to GitHub

Ask the user whether they'd like to post the review to the PR on GitHub using AskUserQuestion with options "Yes, post the review to GitHub" and "No, just the local review". If the user declines, proceed to Step 5.

If the user accepts:

  1. Gather PR metadata by running ${CLAUDE_SKILL_DIR}/scripts/pr-metadata.sh, which outputs JSON with owner_repo, pr_number, head_sha, pr_author_login, and current_user_login.
  2. Read the report file at the path captured in Step 2, then build the review body from it: Review Summary table, Review Recommendation, and all findings organized by severity, plus any optional sections that are present. Treat every section other than the Review Summary table and the Review Recommendation as optional — the code-review skill renders a section only when it has content, so a section (the Review Coverage section, the What's Good section, an absent severity section on a clean review, the Security Vulnerabilities section, the Remediation note) may simply not be there. Include each section when present and omit it without error or an empty heading when absent. Review Coverage always crosses when present. It appears only when no specialist read the change, and a reviewer on the pull request is the reader who most needs to know that.
  3. Continue to Step 4 — do not post yet.

Step 4: Pre-Post Clarity Check

Because the review body will be publicly visible on the PR, run a clarity pass on the draft before posting.

Match the body's length to what the review found. Every finding earns its place by naming a specific problem at a specific location. Skip filler sections, a restated summary of the diff, and boilerplate the reader can see for themselves on the PR. Three sections are exempt from that length-matching and are never cut or shortened: the Review Summary table, the Review Recommendation, and the Review Coverage section. Review Coverage names no problem at any location by construction; it says which coverage the review did not have, and deleting it as filler would hide that from the widest audience the review reaches. Stay inside what the review covered: this step edits wording and severity, and never adds a finding /code-review did not raise.

  1. Write the draft review body to a temporary file (e.g., /tmp/post-code-review-to-pr-draft.md) using the Write tool.
  2. Launch a single han-core:junior-developer agent in artifact-review mode with the prompt: "You are reviewing the text of a code review that is about to be posted publicly on a GitHub pull request. The review is at {draft_path}. Do not re-review the code — review the review. Flag findings whose wording is unclear, severity is mis-assigned (CRIT used where WARN would be accurate, or vice versa), language is accusatory or blaming rather than evidence-based, or file_path:line_number references are missing or invalid. Leave the Review Summary table, the Review Recommendation, and any Review Coverage section as they are; they are not findings and are not subject to the length-matching bar. Return a short list of specific edits with before/after text; return an empty list if the review reads well as-is."
  3. Apply every actionable edit the agent returns. If the agent raises a severity-assignment issue, adjust the finding's task ID and the Review Summary table to match.
  4. Generate a unique temp file path by running ${CLAUDE_SKILL_DIR}/scripts/create-review-tempfile.sh. Write the final, edited review body to that path using the Write tool (not Bash).
  5. Post based on authorship: If pr_author_login matches current_user_login (self-authored PR), post as a PR comment (GitHub rejects formal reviews from PR authors) by running ${CLAUDE_SKILL_DIR}/scripts/post-pr-comment.sh {pr_number} {temp_file_path}. If they differ, determine event type (REQUEST_CHANGES if any CRIT or WARN findings exist, COMMENT if only SUGG) and post as a formal review by running ${CLAUDE_SKILL_DIR}/scripts/post-pr-review.sh {owner/repo} {pr_number} {head_sha} {event_type} {temp_file_path}.
  6. On success, report the PR URL. On failure, report the error.

Step 5: Offer to Create Fix Plan (Only If Issues Found)

If any Critical or Warning issues were identified, ask the user using AskUserQuestion — "Would you like me to create a plan to fix the identified issues?" with options "Yes, create a fix plan" and "No, just the review". If yes, enter plan mode and create a detailed implementation plan listing each Critical and Warning item by task ID, with specific code changes, file paths, and line numbers, ordered by priority (Critical first). If no Critical or Warning issues were found, the review is complete — suggestions alone do not warrant a fix plan.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Post Code Review To Pr AI skill do?

Run a full pull request review and post review comments directly to the current branch's GitHub PR. Requires the gh CLI to be installed and a PR to already exist for the current branch. Use when you want review feedback posted to GitHub as PR comments. For local code review without posting to GitHub, use code-review instead. Does not write or update PR descriptions — use update-pr-description for that.

Why use Post Code Review To Pr on TypingMind?

Because you install it once and use it with any model. Post Code Review To Pr 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 Post Code Review To Pr in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/testdouble/han/tree/main/han-github/skills/post-code-review-to-pr. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Post Code Review To Pr?

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 Post Code Review To Pr?

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

Is the Post Code Review To Pr AI skill free?

Yes. It is published on GitHub by testdouble 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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