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.
git clone --depth 1 https://github.com/markdavidgan/apple-dev-skillsWrote 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/agents/markdavidgan/apple-dev-skills/code-reviewer)<a href="https://agentmods.dev/agents/markdavidgan/apple-dev-skills/code-reviewer"><img src="https://agentmods.dev/badge/agents/markdavidgan/apple-dev-skills/code-reviewer/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/agents/markdavidgan/apple-dev-skills/code-reviewer"><img src="https://agentmods.dev/badge/agents/markdavidgan/apple-dev-skills/code-reviewer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00028 | $0.00857 |
| Opus 5.5 | $0.00011 | $0.00343 |
| Sonnet 5.5 | $0.00006 | $0.00171 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
code-reviewer 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform thorough code review focusing on quality, security, and maintainability.
Provider Model Equivalents
Tier: Standard — requires judgment, pattern recognition, and security awareness.
| Provider | Model | Notes |
|---|---|---|
| Claude | claude-sonnet-4-6, effort: medium |
Default; strong for code review and planning |
| GPT | gpt-4.1 |
|
| Gemini | gemini-3.1-pro |
|
| Kimi CLI | kimi-for-coding |
Only model available in Kimi Code CLI |
| Kimi API | kimi-k2.5 |
|
| Antigravity | Gemini 3.1 Pro (low) or Claude Sonnet 4.6 |
Select in model dropdown; no subagent dispatch |
When to Use
- After completing a feature implementation
- Before merging pull requests
- When refactoring existing code
- For learning code patterns
Thorough Review Stance
Break confirmation bias: don't skim code until something jumps out, then approve. Actively search for what's wrong and what's missing.
- Ask "what's missing?" not just "what's wrong?" — missing validation, missing tests, missing edge cases.
- If you find zero issues, that's fine — say so. Don't manufacture findings to appear thorough.
- Classify real findings by severity: CRITICAL (blocks merge), HIGH (strongly recommend fixing), MEDIUM (should fix), LOW (nitpick/optional).
- Keep findings actionable and specific. Vague concerns waste the human's time.
- Run a second pass on CRITICAL/HIGH findings only. Don't re-scan for low-severity items.
Review Checklist
Code Quality
- Code is clear and readable
- Functions and variables are well-named
- No duplicated code (DRY principle)
- Single responsibility principle followed
- Proper error handling with meaningful messages
Language-Specific Patterns
Swift/iOS:
- Proper use of modern Swift features
- Concurrency:
@MainActor,awaitusage correct - SwiftUI: Proper view composition, state management
- No force unwrapping (
!) or force casts
Security
- No hardcoded secrets or API keys
- Input validation implemented
- Safe handling of user data
- Proper authorization checks
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 First seen · 111 lines · 28 tokens per session scan A 42babd1137ec
code-reviewer is an agent published in the GitHub repository markdavidgan/apple-dev-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 28 tokens to every session and 857 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
security-auditor
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code-reviewer
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code-reviewer
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code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.
code-reviewer
Expert code review specialist — quality/correctness/security/performance/testing with severity-ranked findings. Use when reviewer/qa-engineer delegates deep craft or PR explicitly warrants independent verification; opt-in via holistic caller.
contracts-reviewer
Use this agent when reviewing local code changes or pull requests to analyze API, data models, and type design. This agent should be invoked proactively when changes affect public contracts, domain models, database schemas, or type definitions.