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Code Review Expert

CommunityPopular
dtsola
code-review-expert

Expert code review of current git changes with a senior engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements.

Overview

Publisherdtsola
Repositoryxiaoyaosearch
Skill namecode-review-expert
Stars
1K
Forks
54
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Code Review Expert 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/dtsola/xiaoyaosearch/tree/main/.claude/skills/code-review-expert
  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/dtsola/xiaoyaosearch.git /tmp/xiaoyaosearch
mkdir -p .claude/skills
cp -r /tmp/xiaoyaosearch/.claude/skills/code-review-expert .claude/skills/code-review-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Review Expert 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 Code Review Expert 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 Code Review Expert 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 Review Expert

Overview

Perform a structured review of the current git changes with focus on SOLID, architecture, removal candidates, and security risks. Default to review-only output unless the user asks to implement changes.

Severity Levels

LevelNameDescriptionAction
P0CriticalSecurity vulnerability, data loss risk, correctness bugMust block merge
P1HighLogic error, significant SOLID violation, performance regressionShould fix before merge
P2MediumCode smell, maintainability concern, minor SOLID violationFix in this PR or create follow-up
P3LowStyle, naming, minor suggestionOptional improvement

Workflow

1) Preflight context

  • Use git status -sb, git diff --stat, and git diff to scope changes.
  • If needed, use rg or grep to find related modules, usages, and contracts.
  • Identify entry points, ownership boundaries, and critical paths (auth, payments, data writes, network).

Edge cases:

  • No changes: If git diff is empty, inform user and ask if they want to review staged changes or a specific commit range.
  • Large diff (>500 lines): Summarize by file first, then review in batches by module/feature area.
  • Mixed concerns: Group findings by logical feature, not just file order.

2) SOLID + architecture smells

  • Load references/solid-checklist.md for specific prompts.
  • Look for:
    • SRP: Overloaded modules with unrelated responsibilities.
    • OCP: Frequent edits to add behavior instead of extension points.
    • LSP: Subclasses that break expectations or require type checks.
    • ISP: Wide interfaces with unused methods.
    • DIP: High-level logic tied to low-level implementations.
  • When you propose a refactor, explain why it improves cohesion/coupling and outline a minimal, safe split.
  • If refactor is non-trivial, propose an incremental plan instead of a large rewrite.

3) Removal candidates + iteration plan

  • Load references/removal-plan.md for template.
  • Identify code that is unused, redundant, or feature-flagged off.
  • Distinguish safe delete now vs defer with plan.
  • Provide a follow-up plan with concrete steps and checkpoints (tests/metrics).

4) Security and reliability scan

  • Load references/security-checklist.md for coverage.
  • Check for:
    • XSS, injection (SQL/NoSQL/command), SSRF, path traversal
    • AuthZ/AuthN gaps, missing tenancy checks
    • Secret leakage or API keys in logs/env/files
    • Rate limits, unbounded loops, CPU/memory hotspots
    • Unsafe deserialization, weak crypto, insecure defaults
    • Race conditions: concurrent access, check-then-act, TOCTOU, missing locks
  • Call out both exploitability and impact.

5) Code quality scan

  • Load references/code-quality-checklist.md for coverage.
  • Check for:
    • Error handling: swallowed exceptions, overly broad catch, missing error handling, async errors
    • Performance: N+1 queries, CPU-intensive ops in hot paths, missing cache, unbounded memory
    • Boundary conditions: null/undefined handling, empty collections, numeric boundaries, off-by-one
  • Flag issues that may cause silent failures or production incidents.

6) Output format

Structure your review as follows:

markdown
## Code Review Summary

**Files reviewed**: X files, Y lines changed
**Overall assessment**: [APPROVE / REQUEST_CHANGES / COMMENT]

---

## Findings

### P0 - Critical
(none or list)

### P1 - High
1. **[file:line]** Brief title
  - Description of issue
  - Suggested fix

### P2 - Medium
2. (continue numbering across sections)
  - ...

### P3 - Low
...

---

## Removal/Iteration Plan
(if applicable)

## Additional Suggestions
(optional improvements, not blocking)

Inline comments: Use this format for file-specific findings:

::code-comment{file="path/to/file.ts" line="42" severity="P1"}
Description of the issue and suggested fix.
::

Clean review: If no issues found, explicitly state:

  • What was checked
  • Any areas not covered (e.g., "Did not verify database migrations")
  • Residual risks or recommended follow-up tests

7) Next steps confirmation

After presenting findings, ask user how to proceed:

markdown
---

## Next Steps

I found X issues (P0: _, P1: _, P2: _, P3: _).

**How would you like to proceed?**

1. **Fix all** - I'll implement all suggested fixes
2. **Fix P0/P1 only** - Address critical and high priority issues
3. **Fix specific items** - Tell me which issues to fix
4. **No changes** - Review complete, no implementation needed

Please choose an option or provide specific instructions.

Important: Do NOT implement any changes until user explicitly confirms. This is a review-first workflow.

Resources

references/

FilePurpose
solid-checklist.mdSOLID smell prompts and refactor heuristics
security-checklist.mdWeb/app security and runtime risk checklist
code-quality-checklist.mdError handling, performance, boundary conditions
removal-plan.mdTemplate for deletion candidates and follow-up 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 Code Review Expert AI skill do?

Expert code review of current git changes with a senior engineer lens. Detects SOLID violations, security risks, and proposes actionable improvements.

Why use Code Review Expert on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dtsola/xiaoyaosearch/tree/main/.claude/skills/code-review-expert. 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 Code Review Expert?

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 Code Review Expert?

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

Is the Code Review Expert AI skill free?

It is published on GitHub by dtsola. Check the repository for licensing terms. 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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