Code Reviewer

Community

borghei

code-reviewer

Code review automation for TypeScript, JavaScript, Python, Go, Swift, and Kotlin. Analyzes PRs for complexity, risk, SOLID violations, and code smells. Use when reviewing PRs, analyzing code quality, or generating review checklists.

Overview

Publisher	borghei
Repository	`Claude-Skills`
Skill name	`code-reviewer`
Stars	856
Forks	154
Bundled files	7
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.
7 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by borghei on GitHub. Read the source before you install it.

Installation

Install the Code Reviewer 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.
https://github.com/borghei/Claude-Skills/tree/main/engineering/code-reviewer
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/borghei/Claude-Skills.git /tmp/Claude-Skills
mkdir -p .claude/skills
cp -r /tmp/Claude-Skills/engineering/code-reviewer .claude/skills/code-reviewer

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

Use it in TypingMind

Enable Code Reviewer 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 Reviewer 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 Reviewer 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

Automated code review tooling that analyzes pull requests for complexity and risk, scans source for code smells and SOLID violations, and consolidates findings into structured review reports with a verdict, score, and prioritized action items. Supports TypeScript, JavaScript, Python, Go, Swift, and Kotlin.

Core Capabilities

PR risk analysis — scan git diffs for hardcoded secrets, SQL injection, debug statements, lint bypasses, any types, and TODO/FIXME, with a 1-10 complexity score.
Code quality checking — detect long functions, large files, god classes, deep nesting, too many params, high cyclomatic complexity, missing error handling, unused imports, and magic numbers against fixed thresholds.
SOLID & antipattern detection — flag structural, logic, security, performance, testing, and async antipatterns with fixes.
Review reports — merge PR and quality findings into a verdict (approve / request changes / block), 0-100 score, and ranked action items in text, markdown, or JSON.
Review order prioritization — sort files so security-sensitive code is inspected first.
Commit hygiene — validate conventional-commit format across a branch.

When to Use

Reviewing a PR and needing fast pre-screening before manual logic review.
Analyzing code quality or auditing a codebase for smells and SOLID violations.
Generating a structured review report or review checklist.
Gating merges in CI/CD on a quality score or review verdict.

Clarify First

Before producing the review, confirm these inputs. If any is unknown or vague, ASK — do not assume:

What to review — the git diff/branch (base and head) or a source directory (sets --base/--head or the path the analyzers scan)
Language — TypeScript, JavaScript, Python, Go, Swift, or Kotlin (selects the rules code_quality_checker.py applies via --language)
Output & gate — report format (text/markdown/JSON) and any pass/fail score threshold (sets the artifact format and CI verdict)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Tool	Purpose	Command
`pr_analyzer.py`	Assess PR complexity and risk from a git diff	`python scripts/pr_analyzer.py . --base main --head feature-branch --json`
`code_quality_checker.py`	Detect code smells and SOLID violations in source	`python scripts/code_quality_checker.py ./src --language typescript --json`
`review_report_generator.py`	Combine PR + quality findings into a review report	`python scripts/review_report_generator.py . --format markdown --output review.md`

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

references/tool_reference.md — full per-tool usage, detection lists, threshold/verdict tables, all CLI flags, JSON output samples, supported-language extensions, troubleshooting table, and success criteria. Read when running, configuring, or debugging the scripts.
references/code_review_checklist.md — systematic checklists for pre-review, correctness, security, performance, maintainability, testing, and language-specific checks. Read when performing a manual review pass.
references/coding_standards.md — language-specific standards for TypeScript, JavaScript, Python, Go, Swift, and Kotlin (types, null safety, async, error handling, class design). Read when judging style and idiom compliance.
references/common_antipatterns.md — antipattern catalog (structural, logic, security, performance, testing, async) with examples, detection cues, and fixes. Read when explaining or fixing a flagged smell.

Scope & Limitations

Covers:

Static pattern-based risk detection in git diffs (secrets, SQL injection, debug statements, lint bypasses)
Structural code quality analysis: function length, class size, cyclomatic complexity, parameter count, SOLID violations
PR metadata assessment: file categorization by risk priority, commit message validation, complexity scoring
Consolidated review reports with verdicts, scores, and prioritized action items across text, markdown, and JSON formats

Does NOT cover:

Runtime or dynamic analysis -- use senior-qa for test execution and qa-browser-automation for end-to-end testing
Security vulnerability scanning (CVE databases, dependency audits) -- use senior-security or senior-secops for SAST/DAST and supply chain analysis
Performance profiling or benchmarking -- use senior-backend or senior-fullstack for performance optimization workflows
Architecture-level review (system design, service boundaries, API contract validation) -- use senior-architect for architectural decision records and design review

Integration Points

Skill	Integration	Data Flow
`senior-security`	Feed PR Analyzer critical findings into security review workflows for deeper SAST/DAST analysis	`pr_analyzer.py --json` output `risks.critical[]` → security assessment input
`senior-qa`	Gate test execution on review report verdict; block test suites when verdict is `block`	`review_report_generator.py --json` output `summary.verdict` → QA pipeline gate
`senior-architect`	Escalate high-complexity PRs (score 7+) to architecture review	`pr_analyzer.py` output `summary.complexity_score` → architecture review trigger
`senior-fullstack`	Combine code quality scores with fullstack quality analyzer for end-to-end project health	`code_quality_checker.py --json` output → merged with `code_quality_analyzer.py` metrics
`tdd-guide`	Cross-reference review findings with test coverage; flag untested code paths flagged by quality checker	Quality checker `smells[]` by file → TDD coverage gap analysis
`senior-devops`	Integrate review reports into CI/CD pipelines as automated quality gates	`review_report_generator.py --json` output `summary.score` → pipeline pass/fail threshold

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 Reviewer AI skill do?

Why use Code Reviewer on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/borghei/Claude-Skills/tree/main/engineering/code-reviewer. 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 Reviewer?

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 Reviewer?

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

Is the Code Reviewer AI skill free?

It is published on GitHub by borghei. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Code Reviewer

Core Capabilities

When to Use

Clarify First

Tools

References

Scope & Limitations

Integration Points

Bundled files

Frequently asked questions

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