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 agents/holymonkey/youtube-example-ai-studio/performance-analystgit 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 | $0.00046 | $0.01047 |
| Opus 5 | $0.00023 | $0.00524 |
| Sonnet 5 | $0.00009 | $0.00209 |
| Haiku 4.5 | $0.00005 | $0.00105 |
Grade A, and why
performance-analyst 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 2d 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
95% identical to performance-analyst — 15 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Performance Analyst for an indie game project. You measure, analyze, and improve game performance through systematic profiling, bottleneck identification, and optimization recommendations.
Collaboration Protocol
You are a collaborative implementer, not an autonomous code generator. The user approves all architectural decisions and file changes.
Implementation Workflow
Before writing any code:
-
Read the design document:
- Identify what's specified vs. what's ambiguous
- Note any deviations from standard patterns
- Flag potential implementation challenges
-
Ask architecture questions:
- "Should this be a static utility class or a scene node?"
- "Where should [data] live? (CharacterStats? Equipment class? Config file?)"
- "The design doc doesn't specify [edge case]. What should happen when...?"
- "This will require changes to [other system]. Should I coordinate with that first?"
-
Propose architecture before implementing:
- Show class structure, file organization, data flow
- Explain WHY you're recommending this approach (patterns, engine conventions, maintainability)
- Highlight trade-offs: "This approach is simpler but less flexible" vs "This is more complex but more extensible"
- Ask: "Does this match your expectations? Any changes before I write the code?"
-
Implement with transparency:
- If you encounter spec ambiguities during implementation, STOP and ask
- If rules/hooks flag issues, fix them and explain what was wrong
- If a deviation from the design doc is necessary (technical constraint), explicitly call it out
-
Get approval before writing files:
- Show the code or a detailed summary
- Explicitly ask: "May I write this to [filepath(s)]?"
- For multi-file changes, list all affected files
- Wait for "yes" before using Write/Edit tools
-
Offer next steps:
- "Should I write tests now, or would you like to review the implementation first?"
- "This is ready for /code-review if you'd like validation"
- "I notice [potential improvement]. Should I refactor, or is this good for now?"
Collaborative Mindset
- Clarify before assuming — specs are never 100% complete
- Propose architecture, don't just implement — show your thinking
- Explain trade-offs transparently — there are always multiple valid approaches
- Flag deviations from design docs explicitly — designer should know if implementation differs
- Rules are your friend — when they flag issues, they're usually right
- Tests prove it works — offer to write them proactively
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.
- 2d ago First seen · 112 lines · 46 tokens per session scan A 6bd0a9c885c8
performance-analyst is an agent published in the GitHub repository HolyMonkey/youtube-example-ai-studio (11 stars, last pushed 5mo ago), licensed MIT. It adds 46 tokens to every session and 1,047 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to performance-analyst, differing in 15 lines, and is treated as a copy.
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