Borrowing it
Nothing to install: this file belongs to muratgur/ordinus. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/muratgur/ordinus/master/.claude/skills/code-review-and-quality/SKILL.mdgit clone --depth 1 https://github.com/muratgur/ordinusWrote 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/muratgur/ordinus/code-review-and-quality)<a href="https://agentmods.dev/skills/muratgur/ordinus/code-review-and-quality"><img src="https://agentmods.dev/badge/skills/muratgur/ordinus/code-review-and-quality.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00051 | $0.02891 |
| Opus 5 | $0.00026 | $0.01445 |
| Sonnet 5 | $0.00010 | $0.00578 |
| Haiku 4.5 | $0.00005 | $0.00289 |
Grade A, and why
code-review-and-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 8d 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.
This is a copy
84% identical to code-review-and-quality — 86 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review and Quality
Overview
Multi-dimensional code review with quality gates. Every change gets reviewed before merge — no exceptions. Review covers five axes: correctness, readability, architecture, security, and performance.
The approval standard: Approve a change when it definitely improves overall code health, even if it isn't perfect. Perfect code doesn't exist — the goal is continuous improvement. Don't block a change because it isn't exactly how you would have written it. If it improves the codebase and follows the project's conventions, approve it.
When to Use
- Before merging any PR or change
- After completing a feature implementation
- When another agent or model produced code you need to evaluate
- When refactoring existing code
- After any bug fix (review both the fix and the regression test)
The Five-Axis Review
Every review evaluates code across these dimensions:
1. Correctness
Does the code do what it claims to do?
- Does it match the spec or task requirements?
- Are edge cases handled (null, empty, boundary values)?
- Are error paths handled (not just the happy path)?
- Does it pass all tests? Are the tests actually testing the right things?
- Are there off-by-one errors, race conditions, or state inconsistencies?
2. Readability & Simplicity
Can another engineer (or agent) understand this code without the author explaining it?
- Are names descriptive and consistent with project conventions? (No
temp,data,resultwithout context) - Is the control flow straightforward (avoid nested ternaries, deep callbacks)?
- Is the code organized logically (related code grouped, clear module boundaries)?
- Are there any "clever" tricks that should be simplified?
- Could this be done in fewer lines? (1000 lines where 100 suffice is a failure)
- Are abstractions earning their complexity? (Don't generalize until the third use case)
- Would comments help clarify non-obvious intent? (But don't comment obvious code.)
- Are there dead code artifacts: no-op variables (
_unused), backwards-compat shims, or// removedcomments?
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.
- 8d ago First seen · 315 lines · 51 tokens per session scan A 2c3b0969a807
code-review-and-quality is a skill published in the GitHub repository muratgur/ordinus (111 stars, last pushed 4d ago), licensed MIT. It adds 51 tokens to every session and 2,891 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to code-review-and-quality, differing in 86 lines, and is treated as a copy.
Other skills, from other repositories
code-reviewer
Becomes a senior code reviewer who evaluates pull requests and code changes for correctness, security, performance, and maintainability. Use when the user asks for code review, PR feedback, code quality assessment, or merge readiness evaluation. Do NOT use when writing new code, debugging runtime errors, designing…
update-pr
Update the pull request for the current session. Use when the user wants to push new changes to an existing PR.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
improve
Autonomous quality improvement loop. Scores a target against a rubric, selects the highest-leverage axis, attacks it, verifies, documents, and loops. No pre-planning between iterations — each loop re-scores from scratch.
code-review-pipeline
Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.
security-review
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.