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Agent skill

Code Quality Review

by paiml in paiml/aprender

A skill your agent uses to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks…

MITAuto-check passedDevelopment

Install Code Quality Review

skills CLI
$ npx skills add paiml/aprender --skill code-quality-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install paiml/aprender code-quality-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/paiml/aprender.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/analyst_4/skills/code-quality-review .claude/skills/code-quality-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
code-quality-review
GitHub stars
127
Token cost
~626 tokens
SKILL.md length
296 words
Files
1
Skills in repo
6
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks…

  • Works in 4 steps: Spawn Subagents: Use the invoke_subagent… → Define Prompts: For each subagent,… → Wait for Reports: Pause your execution… → …
  • Perform a comprehensive code quality and architecture review of the current project
  • Calls gh
  • Identifying code smells

What it does

Code Quality Review is an agent skill from paiml/aprender. Use this skill to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks, testing gaps, and logic issues. Trigger this whenever the user asks for a comprehensive code review or quality audit.

Its SKILL.md is about 630 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code quality, Refactoring and Software architecture. The repository describes itself as: Next Generation Machine Learning, Statistics and Deep Learning in PURE Rust. The licence is MIT.

When your agent uses it

  • Perform a comprehensive code quality and architecture review of the current project
  • Identifying code smells
  • Architectural debt
  • Performance bottlenecks

Example prompts

  • “/code-quality-review”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Spawn Subagents: Use the invoke_subagent tool to spawn a quorum of specialized subagents. Assign each subagent a highly specialized role.
  2. Define Prompts: For each subagent, provide a highly specific prompt instructing them to review the current project (or the specific…
  3. Wait for Reports: Pause your execution and wait for all subagents to report back. Do not poll. The system will automatically wake you up…
  4. Create Quality Report Epic: Once all subagents have submitted their findings, assimilate the entire report into a comprehensive Quality…

What it can do on your machine

Read from SKILL.md and the folder at commit 4098007. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Code Quality Review loads about 626 tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 296 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~626

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from paiml/aprender at commit 4098007, republished under its MIT licence (© paiml). 296 words, ~626 tokens.

Download SKILL.mdSave it as .claude/skills/code-quality-review/SKILL.md (or your agent's skills folder).
name
code-quality-review
description
Use this skill to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks, testing gaps, and logic issues. Trigger this whenever the user asks for a comprehensive code review or quality audit.

Code Quality Review

This skill orchestrates a parallel audit of a codebase using a quorum of highly specialized subagents. It is designed to work out-of-the-box on any PAIML project.

Workflow

  1. Spawn Subagents: Use the invoke_subagent tool to spawn a quorum of specialized subagents. Assign each subagent a highly specialized role. Ensure that the roles cover a wide range of engineering concerns. Example roles must include, but are not limited to:

    • Quantitative PMAT Auditor (Must use the pmat CLI to review quantitative quality and formal verification metrics)
    • Architecture Auditor: Dependencies
    • Architecture Auditor: State Management
    • Performance: Hot Paths
    • Performance: IO and Async
    • Performance: Data Structures
    • Testing: Unit Coverage
    • API Design Ergonomics
    • Build System Auditor
    • Documentation Auditor
    • Scalability & Large Data
    • Code Smell Auditor
    • Error Handling Auditor

  2. Define Prompts: For each subagent, provide a highly specific prompt instructing them to review the current project (or the specific repositories the user requested) for issues matching their domain. Instruct them to use fast tools (like grep_search or view_file) and return concise, high-impact findings (e.g. top 1-3 critical issues) to avoid overwhelming the context. Ensure the PMAT Auditor is instructed to run pmat.

  3. Wait for Reports: Pause your execution and wait for all subagents to report back. Do not poll. The system will automatically wake you up and notify you as messages arrive.

  4. Create Quality Report Epic: Once all subagents have submitted their findings, assimilate the entire report into a comprehensive Quality Report Epic. Use the gh CLI (via run_command executing gh issue create) to create and populate this Epic on the GitHub repository.

    Ensure the resulting Epic includes:

    • A categorized breakdown of all findings and architectural debt.
    • A roster of the names and roles of the agents that participated.
    • Individual feedback and findings attributed to each specific agent.

© paiml, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/analyst_4/skills/code-quality-review of paiml/aprender.

Open the folder on GitHubat commit 4098007

Compare with similar skills

Code Quality Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Code Quality Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Quality Review this skillpaiml/aprender127—~626Automated safety check: PassMIT
A Philosophy of Software Designciembor/agent-rules-books2.9k—~181Automated safety check: PassMIT
Typescript Best Practicesjwynia/agent-skills165—~2.5kAutomated safety check: PassMIT
Smellsmallnest/goal-workflow288—~10kAutomated safety check: PassMIT
Frontend Module Standardssiteboon/claudecodeui14k—~2.6kAutomated safety check: PassAGPL-3.0
Systematic Code Refactoringluongnv89/claude-howto42k—~3kAutomated safety check: PassMIT

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Categories

Questions about Code Quality Review

What does Code Quality Review do?

A skill your agent uses to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks…. Code Quality Review is an agent skill from paiml/aprender. Use this skill to perform a comprehensive code quality and architecture review of the current project or repository, identifying code smells, architectural debt, performance bottlenecks, testing gaps, and logic issues.

When should I use Code Quality Review?

Code Quality Review fits situations like: perform a comprehensive code quality and architecture review of the current project; identifying code smells; architectural debt; performance bottlenecks.

How do I install Code Quality Review in Claude Code?

Run `npx skills add paiml/aprender --skill code-quality-review -a claude-code`. Or copy the skill folder (.agents/analyst_4/skills/code-quality-review in paiml/aprender) into .claude/skills/code-quality-review in your project. Claude Code loads it when a task matches its description.

How do I install Code Quality Review in Codex?

Run `npx skills add paiml/aprender --skill code-quality-review -a codex`. Or copy the skill folder (.agents/analyst_4/skills/code-quality-review in paiml/aprender) into .agents/skills/code-quality-review in your project. Codex loads it when a task matches its description.

Can I use Code Quality Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add paiml/aprender --skill code-quality-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-quality-review, .gemini/skills/code-quality-review, .github/skills/code-quality-review and .opencode/skills/code-quality-review in your project.

What does Code Quality Review need to run?

Going by SKILL.md and its folder, Code Quality Review needs the command-line tools its instructions call (gh).

Does Code Quality Review access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Code Quality Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Code Quality Review use?

Code Quality Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Quality Review use?

About 626 tokens (SKILL.md is roughly 2.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Quality Review?

Skills that share tags, products or a category with Code Quality Review: A Philosophy of Software Design (ciembor/agent-rules-books, 2.9k stars), Typescript Best Practices (jwynia/agent-skills, 165 stars), Smell (smallnest/goal-workflow, 288 stars) and Frontend Module Standards (siteboon/claudecodeui, 14k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Quality Review?

paiml (a GitHub organization) maintains it in paiml/aprender, which has 127 GitHub stars. The repository holds 6 skills in this directory. The repository was last updated on October 7, 2026.

Source: paiml/aprender on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.