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.
npx agentmods add skills/kdcokenny/ocx/code-reviewnpx skills add kdcokenny/ocx --skill code-reviewgit clone --depth 1 https://github.com/kdcokenny/ocxWhat 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 | $0.00014 | $0.00841 |
| Opus 5 | $0.00007 | $0.00420 |
| Sonnet 5 | $0.00003 | $0.00168 |
| Haiku 4.5 | $0.00001 | $0.00084 |
Grade A, and why
code-review 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 yesterday.
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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Philosophy
TL;DR
Systematic code review across 4 layers with severity classification. Only report findings with ≥80% confidence. Include file:line references for all issues.
When to Use This Skill
- Before reporting implementation completion
- When explicitly asked to review code
- When using the
/reviewcommand - As an independent audit after code changes
The 4 Review Layers
Layer 1: Correctness
- Logic errors and edge cases
- Error handling completeness
- Type safety and null checks
- Algorithm correctness
- Off-by-one errors
Layer 2: Security
- No hardcoded secrets or API keys
- Input validation and sanitization
- Injection vulnerability prevention (SQL, XSS, command)
- Authentication and authorization checks
- Sensitive data not logged
- OWASP Top 10 awareness
Layer 3: Performance
- No N+1 query patterns
- Appropriate caching strategies
- No unnecessary re-renders (React/frontend)
- Lazy loading where appropriate
- Memory leak prevention
- Algorithmic complexity concerns
Layer 4: Style & Maintainability
- Adherence to project conventions (check AGENTS.md)
- Code duplication (DRY violations)
- Complexity management (cyclomatic complexity)
- Documentation completeness
- Test coverage gaps
Severity Classification
| Severity | Icon | Criteria | Action Required |
|---|---|---|---|
| Critical | 🔴 | Security vulnerabilities, crashes, data loss, corruption | Must fix before merge |
| Major | 🟠 | Bugs, performance issues, missing error handling | Should fix |
| Minor | 🟡 | Code smells, maintainability issues, test gaps | Nice to fix |
| Nitpick | 🟢 | Style preferences, naming suggestions, documentation | Optional |
Confidence Threshold
Only report findings with ≥80% confidence.
If uncertain about an issue:
- State the uncertainty explicitly: "Potential issue (70% confidence): ..."
- Suggest investigation rather than assert a problem
- Prefer false negatives over false positives (reduce noise)
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.
- yesterday First seen · 106 lines · 14 tokens per session scan A 98200cc8992c
code-review is a skill published in the GitHub repository kdcokenny/ocx (930 stars, last pushed 13d ago), licensed MIT. It adds 14 tokens to every session and 841 once invoked, about $0.0001 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.
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