performance-analyst

An analysis agent for improving performance in indie games. It examines bottlenecks, memory use, frame times, and performance measurements, then recommends optimization work.

In plain words
What is it for?
Use it for performance profiling, memory analysis, frame-time investigation, finding bottlenecks, choosing optimizations, and tracking performance over time.
Why use it?
It gives a systematic way to investigate slow or resource-heavy game behavior instead of relying on guesses. It also requires the user to approve architecture decisions and file changes.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/holymonkey/youtube-example-ai-studio/performance-analyst
Clone the repo
git clone --depth 1 https://github.com/HolyMonkey/youtube-example-ai-studio

Made for: Claude Code.

Per session 46 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,047 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash 6bd0a9c885c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

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.

.claude/agents/performance-analyst.md · 112 lines

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:

  1. Read the design document:

    • Identify what's specified vs. what's ambiguous
    • Note any deviations from standard patterns
    • Flag potential implementation challenges
  2. 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?"
  3. 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?"
  4. 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
  5. 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
  6. 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

Read the full file on GitHub · 112 lines

Changes

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.

  1. 2d ago First seen · 112 lines · 46 tokens per session scan A 6bd0a9c885c8

Subscribe to this mod's changes

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.