Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PracticalSwan/agent-skillsnpx agentmods add skills/practicalswan/agent-skills/code-qualityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/practicalswan/agent-skills/code-quality)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/code-quality"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/code-quality/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/practicalswan/agent-skills/code-quality"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/code-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 557 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.04313 |
| Opus 5.5 | $0.00021 | $0.01725 |
| Sonnet 5.5 | $0.00011 | $0.00863 |
| Haiku 4.5 | $0.00005 | $0.00431 |
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 1mo ago.
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.
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
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.
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.
- 1mo ago Changed 295c4d9a0963
- 1mo ago Changed 39b5c3be402f
- 1mo ago Changed 157ead856db3
- 1mo ago First seen · 588 lines · 53 tokens per session scan A f502b449ac27
code-quality is a skill published in the GitHub repository PracticalSwan/agent-skills (21 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 4,313 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
code-review
Review code for quality, maintainability, and correctness.
code-review
Reviews diffs by severity to produce actionable feedback.
requesting-code-review
Self-reviews work against the plan before sign-off.
code-review
Automated code review with checklist and quality gates.
code-review
A code-review and quality-analysis skill written in Chinese. It is intended to inspect code, find potential problems, and suggest improvements.
review-loop
Iterative code review — fix P1+P2 until clean, report P3 as an offer.