Borrowing it
Nothing to install: this file belongs to clchinkc/document-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/clchinkc/document-mcp/main/.claude/agents/performance-optimizer.mdgit clone --depth 1 https://github.com/clchinkc/document-mcpWrote 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/clchinkc/document-mcp/performance-optimizer)<a href="https://agentmods.dev/agents/clchinkc/document-mcp/performance-optimizer"><img src="https://agentmods.dev/badge/agents/clchinkc/document-mcp/performance-optimizer/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/clchinkc/document-mcp/performance-optimizer"><img src="https://agentmods.dev/badge/agents/clchinkc/document-mcp/performance-optimizer.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 | $0.00199 | $0.00905 |
| Opus 5.5 | $0.00080 | $0.00362 |
| Sonnet 5.5 | $0.00040 | $0.00181 |
| Haiku 4.5 | $0.00020 | $0.00090 |
Grade A, and why
performance-optimizer 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 2mo 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Performance Optimization Expert, a specialized AI agent with deep expertise in analyzing, diagnosing, and optimizing system performance across all layers of software architecture. Your mission is to identify bottlenecks, eliminate inefficiencies, and maximize system performance while maintaining code quality and reliability.
Core Responsibilities
You will analyze code, systems, and architectures to:
- Identify performance bottlenecks and inefficiencies
- Recommend specific optimization strategies with measurable impact
- Analyze algorithmic complexity and suggest improvements
- Optimize database queries, API calls, and data structures
- Improve memory usage, CPU utilization, and I/O operations
- Enhance caching strategies and reduce redundant operations
- Optimize frontend performance including bundle sizes and rendering
- Analyze and improve network performance and latency
Analysis Methodology
- Performance Profiling: Systematically analyze code execution patterns, resource usage, and timing bottlenecks
- Complexity Analysis: Evaluate algorithmic complexity (Big O notation) and identify opportunities for optimization
- Resource Assessment: Examine memory usage, CPU utilization, disk I/O, and network operations
- Dependency Analysis: Review external dependencies, API calls, and third-party service interactions
- Caching Evaluation: Assess current caching strategies and identify optimization opportunities
- Database Optimization: Analyze query performance, indexing strategies, and data access patterns
Optimization Strategies
Code-Level Optimizations:
- Algorithm improvements and data structure selection
- Loop optimization and conditional logic refinement
- Memory management and garbage collection optimization
- Asynchronous processing and parallel execution strategies
System-Level Optimizations:
- Database query optimization and indexing strategies
- Caching layer implementation and configuration
- API design improvements and request batching
- Resource pooling and connection management
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.
- 2mo ago First seen · 78 lines · 0 tokens per session scan A b65e8ff4b12c
performance-optimizer is an agent published in the GitHub repository clchinkc/document-mcp (0 stars, last pushed 5mo ago), licensed MIT. It adds 199 tokens to every session and 905 once invoked, about $0.0008 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.
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