Openakita/Skills@Code Review

CommunityPopular

openakita

openakita/skills@code-review

Review code changes for correctness, security, and maintainability. Supports local git diffs (staged or working tree) and remote Pull Requests (by ID or URL). Use when the user asks to review code, check a PR, audit changes, or wants feedback on code quality before merging.

Overview

Publisher	openakita
Repository	`openakita`
Skill name	`openakita/skills@code-review`
Stars	2K
Forks	277
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by openakita on GitHub. Read the source before you install it.

Installation

Install the Openakita/Skills@Code Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/openakita/openakita.git /tmp/openakita
mkdir -p .claude/skills
cp -r /tmp/openakita/skills/code-review .claude/skills/openakita-openakita-skills-code-review

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openakita/Skills@Code 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 Openakita/Skills@Code 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 Openakita/Skills@Code 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.

Code Reviewer

This skill guides the agent in conducting professional and thorough code reviews for both local development and remote Pull Requests.

Workflow

1. Determine Review Target

Remote PR: If the user provides a PR number or URL (e.g., "Review PR #123"), target that remote PR.
Local Changes: If no specific PR is mentioned, or if the user asks to "review my changes", target the current local file system states (staged and unstaged changes).

2. Preparation

For Remote PRs:

Checkout: Use the GitHub CLI to checkout the PR.

bash
gh pr checkout <PR_NUMBER>

Preflight: Execute the project's standard verification suite to catch automated failures early.
```
bash
npm run preflight
```
Context: Read the PR description and any existing comments to understand the goal and history.

For Local Changes:

Identify Changes:
- Check status: git status
- Read diffs: git diff (working tree) and/or git diff --staged (staged).
Preflight (Optional): If the changes are substantial, ask the user if they want to run npm run preflight before reviewing.

3. In-Depth Analysis

Analyze the code changes based on the following pillars:

Correctness: Does the code achieve its stated purpose without bugs or logical errors?
Maintainability: Is the code clean, well-structured, and easy to understand and modify in the future? Consider factors like code clarity, modularity, and adherence to established design patterns.
Readability: Is the code well-commented (where necessary) and consistently formatted according to our project's coding style guidelines?
Efficiency: Are there any obvious performance bottlenecks or resource inefficiencies introduced by the changes?
Security: Are there any potential security vulnerabilities or insecure coding practices?
Edge Cases and Error Handling: Does the code appropriately handle edge cases and potential errors?
Testability: Is the new or modified code adequately covered by tests (even if preflight checks pass)? Suggest additional test cases that would improve coverage or robustness.

4. Provide Feedback

Structure

Summary: A high-level overview of the review.
Findings:
- Critical: Bugs, security issues, or breaking changes.
- Improvements: Suggestions for better code quality or performance.
- Nitpicks: Formatting or minor style issues (optional).
Conclusion: Clear recommendation (Approved / Request Changes).

Tone

Be constructive, professional, and friendly.
Explain why a change is requested.
For approvals, acknowledge the specific value of the contribution.

5. Cleanup (Remote PRs only)

After the review, ask the user if they want to switch back to the default branch (e.g., main or master).

Bundled files

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

agents/openai.yaml1 KB

Frequently asked questions

What does the Openakita/Skills@Code Review AI skill do?

Why use Openakita/Skills@Code Review on TypingMind?

Because you install it once and use it with any model. Openakita/Skills@Code 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 Openakita/Skills@Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openakita/openakita/tree/main/skills/code-review. 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 Openakita/Skills@Code 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 Openakita/Skills@Code Review?

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

Is the Openakita/Skills@Code Review AI skill free?

Yes. It is published on GitHub by openakita 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Code Reviewer

Workflow

1. Determine Review Target

2. Preparation

For Remote PRs:

For Local Changes:

3. In-Depth Analysis

4. Provide Feedback

Structure

Tone

5. Cleanup (Remote PRs only)

Bundled files

Frequently asked questions

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