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/holymonkey/youtube-example-ai-studio/code-reviewnpx skills add HolyMonkey/youtube-example-ai-studio --skill code-reviewgit clone --depth 1 https://github.com/HolyMonkey/youtube-example-ai-studioWhat 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 | $0.00041 | $0.00603 |
| Opus 5 | $0.00020 | $0.00302 |
| Sonnet 5 | $0.00008 | $0.00121 |
| Haiku 4.5 | $0.00004 | $0.00060 |
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 3d 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
100% identical to code-review — 0 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.
What it actually says
When this skill is invoked:
-
Read the target file(s) in full.
-
Read the CLAUDE.md for project coding standards.
-
Identify the system category (engine, gameplay, AI, networking, UI, tools) and apply category-specific standards.
-
Evaluate against coding standards:
- Public methods and classes have doc comments
- Cyclomatic complexity under 10 per method
- No method exceeds 40 lines (excluding data declarations)
- Dependencies are injected (no static singletons for game state)
- Configuration values loaded from data files
- Systems expose interfaces (not concrete class dependencies)
-
Check architectural compliance:
- Correct dependency direction (engine <- gameplay, not reverse)
- No circular dependencies between modules
- Proper layer separation (UI does not own game state)
- Events/signals used for cross-system communication
- Consistent with established patterns in the codebase
-
Check SOLID compliance:
- Single Responsibility: Each class has one reason to change
- Open/Closed: Extendable without modification
- Liskov Substitution: Subtypes substitutable for base types
- Interface Segregation: No fat interfaces
- Dependency Inversion: Depends on abstractions, not concretions
-
Check for common game development issues:
- Frame-rate independence (delta time usage)
- No allocations in hot paths (update loops)
- Proper null/empty state handling
- Thread safety where required
- Resource cleanup (no leaks)
-
Output the review in this format:
## Code Review: [File/System Name]
### Standards Compliance: [X/6 passing]
[List failures with line references]
### Architecture: [CLEAN / MINOR ISSUES / VIOLATIONS FOUND]
[List specific architectural concerns]
### SOLID: [COMPLIANT / ISSUES FOUND]
[List specific violations]
### Game-Specific Concerns
[List game development specific issues]
### Positive Observations
[What is done well -- always include this section]
### Required Changes
[Must-fix items before approval]
### Suggestions
[Nice-to-have improvements]
### Verdict: [APPROVED / APPROVED WITH SUGGESTIONS / CHANGES REQUIRED]
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.
- 3d ago First seen · 75 lines · 41 tokens per session scan A 1b22674955b5
code-review is a skill published in the GitHub repository HolyMonkey/youtube-example-ai-studio (11 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 603 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to code-review, differing in 0 lines, and is treated as a copy.
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