New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Code Quality Review

by jellydn in jellydn/my-ai-tools

Audit a diff for structural maintainability and unnecessary complexity.

MITAuto-check passedDevelopment

Install Code Quality Review

skills CLI
$ npx skills add jellydn/my-ai-tools --skill code-quality-review -a claude-code

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

GitHub CLI
$ gh skill install jellydn/my-ai-tools 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/jellydn/my-ai-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/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
123
Token cost
~817 tokens
SKILL.md length
397 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
MIT

At a glance

Audit a diff for structural maintainability and unnecessary complexity.

  • Works in 7 steps: Simpler structure — Can a different… → Cohesive control flow — Flag scattered… → Useful abstractions — Flag thin… → …
  • Tasks that involve Code quality
  • SKILL.md covers Review Boundaries, Review Criteria, Finding Bar and Output
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Quality Review is an agent skill from jellydn/my-ai-tools. Audit a diff for structural maintainability and unnecessary complexity.

Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: cline, claude, opencode, amp, codex, gemini, cursor, pi

It sits in Development, covering Code quality. The repository describes itself as: Comprehensive configuration management for AI coding tools - Replicate my complete setup for Claude Code, OpenCode, Amp, Li, Codex and Claude Code Switch with custom… The licence is MIT.

When your agent uses it

  • Tasks that involve Code quality

Example prompts

  • “/code-quality-review”

Requirements

  • Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi

Workflow steps

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

  1. Simpler structure — Can a different model or ownership boundary remove branches, modes, helpers, or layers?
  2. Cohesive control flow — Flag scattered special cases, repeated conditions, deep nesting, and mixed responsibilities.
  3. Useful abstractions — Flag thin wrappers, generic magic, speculative extension points, and duplicate helpers.
  4. Clean boundaries — Keep feature logic in its canonical module and make types and invariants explicit.
  5. Reasonable file size — Treat a change that pushes a file above 1,000 lines as a decomposition signal unless the
  6. Sound orchestration — Flag unnecessarily sequential independent work and updates that can leave related state
  7. Legibility — Prefer direct, boring code with clear names and comments that explain only non-obvious reasons.

What it can do on your machine

Read from SKILL.md and the folder at commit 7a06584. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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.

  • Compatibility

    cline, claude, opencode, amp, codex, gemini, cursor, pi

    From compatibility in the SKILL.md frontmatter.

Context cost

Code Quality Review loads about 817 tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 397 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~817

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 jellydn/my-ai-tools at commit 7a06584, republished under its MIT licence (© jellydn). 397 words, ~817 tokens.

Download SKILL.mdSave it as .claude/skills/code-quality-review/SKILL.md (or your agent's skills folder).
name
code-quality-review
description
Audit a diff for structural maintainability and unnecessary complexity.
compatibility
cline, claude, opencode, amp, codex, gemini, cursor, pi
license
MIT
hint
Use for a deep review of maintainability, abstraction quality, and avoidable complexity
user-invocable
true
disable-model-invocation
true

Code Quality Review

Review the current branch diff for structural quality. Preserve behavior, but actively look for a simpler design that deletes concepts, branches, wrappers, or layers instead of moving complexity around.

Review Boundaries

  • Review only the changed code and the surrounding code needed to judge it.
  • Treat repository conventions and existing canonical helpers as the source of truth.
  • Skip formatter and linter findings that automation already reports.
  • Prefer a small set of high-confidence structural findings over cosmetic notes.
  • Do not propose a large refactor unless its benefit is clear and it stays within the change's ownership boundary.

Review Criteria

Check each meaningful change for:

  1. Simpler structure — Can a different model or ownership boundary remove branches, modes, helpers, or layers?
  2. Cohesive control flow — Flag scattered special cases, repeated conditions, deep nesting, and mixed responsibilities.
  3. Useful abstractions — Flag thin wrappers, generic magic, speculative extension points, and duplicate helpers.
  4. Clean boundaries — Keep feature logic in its canonical module and make types and invariants explicit.
  5. Reasonable file size — Treat a change that pushes a file above 1,000 lines as a decomposition signal unless the file has a clear structural reason to stay whole.
  6. Sound orchestration — Flag unnecessarily sequential independent work and updates that can leave related state partially applied when a clearer atomic design exists.
  7. Legibility — Prefer direct, boring code with clear names and comments that explain only non-obvious reasons.
Show full SKILL.md (163 more words)Show less

Finding Bar

Report a finding only when you can name:

  • the file and relevant hunk;
  • the concrete maintenance cost or failure mode;
  • the smallest practical remedy; and
  • why the remedy is better than the current design.

Treat these as blockers unless the implementation has a clear justification:

  • a structural regression or avoidable boundary leak;
  • a file newly crossing 1,000 lines without useful decomposition;
  • ad hoc branching added to an already busy flow;
  • duplicated logic where a canonical helper exists;
  • an abstraction, cast, or optional contract that adds indirection without clarity; or
  • a clear simplification that removes substantial incidental complexity.

Do not block on personal style, hypothetical future needs, or a rewrite that is only differently complex.

Output

Order findings by impact:

  1. Structural regressions and simpler designs
  2. Control-flow, boundary, abstraction, and type problems
  3. File-size, modularity, and legibility concerns

For each finding, use:

text
[severity] file:line — finding
Impact: concrete cost or risk
Fix: smallest practical remedy

End with APPROVE when no blocking finding remains, or CHANGES REQUESTED with the blocker count. Be direct and respectful.

© jellydn, 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 skills/code-quality-review of jellydn/my-ai-tools.

Open the folder on GitHubat commit 7a06584

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 skilljellydn/my-ai-tools123—~817Automated safety check: PassMIT
Install Anti-Slop Oxlint Rulesdmmulroy/anti-slop5.3k1 repos~2.2kAutomated safety check: PassMIT
WooCommerce Code Reviewwoocommerce/woocommerce11k3 repos~1.1kAutomated safety check: PassCustom licence
Systematic Code Refactoringluongnv89/claude-howto42k—~3kAutomated safety check: PassMIT
Constraint-Driven Developmentaddyosmani/agent-skills103k2 repos~5.2kAutomated safety check: PassMIT
Skill Doli Code ReviewDolibarr/dolibarr7.7k1 repos~1.1kAutomated safety check: PassMIT

Similar skills

  • Install Anti-Slop Oxlint Rules

    dmmulroy/anti-slop

    Installs, updates or migrates the vendored anti-slop Oxlint plugin in a repository, keeping local rule changes and the plugin's license and provenance files.

    5.3k GitHub starsUsed in 1 repo~2.2k tokens
    DevelopmentAuto-check passed

  • WooCommerce Code Review

    woocommerce/woocommerce

    Reviews WooCommerce code changes against the project's standards, flagging backend PHP architecture, naming, documentation, data integrity and testing violations.

    11k GitHub starsUsed in 3 repos~1.1k tokens
    DevelopmentAuto-check passed

  • Systematic Code Refactoring

    luongnv89/claude-howto

    Guides refactoring in phases based on Martin Fowler's method: research, test coverage check, planning and small tested steps, with your approval at each phase.

    42k GitHub stars~3k tokensUpdated 8 days ago
    DevelopmentAuto-check passed

  • Constraint-Driven Development

    addyosmani/agent-skills

    Records a project's quality bar in CONSTRAINTS.md and watches diffs for signs an agent quietly weakened it, such as suppressions, skipped tests or lowered thresholds.

    103k GitHub starsUsed in 2 repos~5.2k tokens
    DevelopmentAuto-check passed

  • Skill Doli Code Review

    Dolibarr/dolibarr

    Reviews Dolibarr PHP code for compliance with coding standards and security best practices, and fixes identified issues.

    7.7k GitHub starsUsed in 1 repo~1.1k tokens
    DevelopmentAuto-check passed

  • Ponytail Lazy Developer Mode

    DietrichGebert/ponytail

    Makes the agent pick the laziest solution that works: skip unneeded work, reuse what exists, prefer the standard library and platform features, and keep diffs small.

    158k GitHub stars~871 tokensUpdated today
    DevelopmentAuto-check passed

More from jellydn/my-ai-tools

All 33 skills in this repo

  • Babysit PR

    jellydn/my-ai-tools

    A skill your agent uses when monitoring an open GitHub PR for CI failures, review feedback, mergeability, and safe retries or fixes.

    123 GitHub stars~4.1k tokensUpdated today
    Auto-check passed

  • Visual PR

    jellydn/my-ai-tools

    Posts a concise visual outline as a GitHub pull request comment.

    123 GitHub stars~764 tokensUpdated today
    Auto-check passed

  • Qmd Knowledge

    jellydn/my-ai-tools

    Manage project knowledge with qmd — captures learnings, decisions, and conventions

    123 GitHub stars~2.1k tokensUpdated today
    Auto-check passed

  • Prd

    jellydn/my-ai-tools

    Generate Product Requirements Documents from feature ideas — plans specs and requirements

    123 GitHub starsUsed in 4 repos~1.8k tokens
    Auto-check passed

  • Capability Experiments

    jellydn/my-ai-tools

    Build an interactive report or experiment when the user asks to explore model capabilities.

    123 GitHub stars~2k tokensUpdated today
    Auto-check passed

  • PR Review

    jellydn/my-ai-tools

    Fix PR review comments by implementing requested changes. An agent skill from jellydn/my-ai-tools.

    123 GitHub stars~1.1k tokensUpdated today
    Auto-check passed

Categories

Questions about Code Quality Review

What does Code Quality Review do?

Audit a diff for structural maintainability and unnecessary complexity. Code Quality Review is an agent skill from jellydn/my-ai-tools. Audit a diff for structural maintainability and unnecessary complexity.

When should I use Code Quality Review?

Code Quality Review fits situations like: tasks that involve Code quality.

How do I install Code Quality Review in Claude Code?

Run `npx skills add jellydn/my-ai-tools --skill code-quality-review -a claude-code`. Or copy the skill folder (skills/code-quality-review in jellydn/my-ai-tools) 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 jellydn/my-ai-tools --skill code-quality-review -a codex`. Or copy the skill folder (skills/code-quality-review in jellydn/my-ai-tools) 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 jellydn/my-ai-tools --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?

SKILL.md names no scripts, command-line tools or credentials: Code Quality Review is instructions for the agent only. Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi.

Does Code Quality Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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 (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Quality Review use?

About 817 tokens (SKILL.md is roughly 3.3k 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: Install Anti-Slop Oxlint Rules (dmmulroy/anti-slop, 5.3k stars), WooCommerce Code Review (woocommerce/woocommerce, 11k stars), Systematic Code Refactoring (luongnv89/claude-howto, 42k stars) and Constraint-Driven Development (addyosmani/agent-skills, 103k 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?

jellydn (a GitHub user) maintains it in jellydn/my-ai-tools, which has 123 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 8, 2026.

Source: jellydn/my-ai-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.