code-quality

A two-stage process for reviewing and improving source code: first checking whether it meets the requested specification, then examining its quality.

In plain words
What is it for?
It supports code reviews, pull-request analysis, security and performance checks, refactoring, and self-critique of generated code.
Why use it?
It helps identify bugs, security problems, maintainability issues, and code that has become difficult to change.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/practicalswan/agent-skills/code-quality
Any agent
npx skills add PracticalSwan/agent-skills --skill code-quality
Clone the repo
git clone --depth 1 https://github.com/PracticalSwan/agent-skills

Made for: Claude Code, Codex.

Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00053 $0.04337
Opus 5 $0.00026 $0.02168
Sonnet 5 $0.00011 $0.00867
Haiku 4.5 $0.00005 $0.00434

Measured 2d ago against content hash f502b449ac27, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-quality scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/review-checklist.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

code-quality/SKILL.md · 588 lines

How it starts

The opening of the file, as written. The whole thing — 588 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Code Quality Management

Comprehensive skill for improving code quality through two-stage review (spec compliance first, then code quality), surgical refactoring, and self-evaluation loops.

  • Leverage native parallel subagent dispatch and 200k+ context windows where available.

Activation Conditions

Use symptom -> action triggers: when one matches, apply this skill and verify with the protocol below.

two-stage review (spec compliance first, then code quality):

  • Performing two-stage reviews (spec compliance first, then code quality), analyzing pull requests
  • Checking code quality, security auditing, performance reviews
  • Examining code for bugs, vulnerabilities, best practices violations
  • "Review code", "check for issues", "audit code", "analyze PR"

Refactoring:

  • Code is hard to understand or maintain
  • Functions/classes are too large, code smells need addressing
  • Adding features is difficult due to code structure
  • User asks "clean up this code", "refactor this", "improve this"

Self-Evaluation:

  • Implementing self-critique and reflection loops for agent outputs
  • Building evaluator-optimizer pipelines for quality-critical generation
  • Creating test-driven code refinement workflows
  • Designing rubric-based or LLM-as-judge evaluation systems
  • Adding iterative improvement to agent outputs (code, reports, analysis)
  • Measuring and improving agent response quality

Part 1: two-stage review (spec compliance first, then code quality)

Review Priorities

When performing a two-stage review (spec compliance first, then code quality), prioritize issues in this order:

🔴 CRITICAL (Block merge)
  • Security: Vulnerabilities, exposed secrets, authentication/authorization issues
  • Correctness: Logic errors, data corruption risks, race conditions
  • Breaking Changes: API contract changes without versioning
  • Data Loss: Risk of data loss or corruption
🟡 IMPORTANT (Requires discussion)
  • Code Quality: Severe violations of SOLID principles, excessive duplication
  • Test Coverage: Missing tests for critical paths or new functionality
  • Performance: Obvious performance bottlenecks (N+1 queries, memory leaks)
  • Architecture: Significant deviations from established patterns

Read the full file on GitHub · 588 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 588 lines · 53 tokens per session scan A f502b449ac27

Subscribe to this mod's changes

code-quality is a skill published in the GitHub repository PracticalSwan/agent-skills (11 stars, last pushed 4d ago), licensed MIT. It adds 53 tokens to every session and 4,337 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.