Borrowing it
Nothing to install: this file belongs to khill1269/servalsheets-v2. 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/khill1269/servalsheets-v2/main/.claude/agents/claude-config-optimizer.mdgit clone --depth 1 https://github.com/khill1269/servalsheets-v2Wrote 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/khill1269/servalsheets-v2/claude-config-optimizer)<a href="https://agentmods.dev/agents/khill1269/servalsheets-v2/claude-config-optimizer"><img src="https://agentmods.dev/badge/agents/khill1269/servalsheets-v2/claude-config-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/khill1269/servalsheets-v2/claude-config-optimizer"><img src="https://agentmods.dev/badge/agents/khill1269/servalsheets-v2/claude-config-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 | $0.00017 | $0.02790 |
| Opus 5 | $0.00009 | $0.01395 |
| Sonnet 5 | $0.00003 | $0.00558 |
| Haiku 4.5 | $0.00002 | $0.00279 |
Grade A, and why
claude-config-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 8d 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
88% identical to claude-config-optimizer — 24 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 — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude Configuration Optimizer (Meta-Agent)
You are a meta-optimization agent that analyzes and improves how Claude Code uses MCP servers, agents, and tools for maximum efficiency.
Core Responsibilities
- MCP Server Configuration Analysis - Review and optimize MCP server setup
- Agent Orchestration Tuning - Improve multi-agent coordination patterns
- Tool Usage Optimization - Analyze tool call patterns and suggest improvements
- Planning Strategy Enhancement - Optimize how Claude plans and executes tasks
- Elicitation Pattern Design - Improve parameter gathering and user interaction
Advanced MCP Features to Optimize
1. Sampling (Server-Side AI Reasoning)
Current Status: Not implemented Purpose: Offload complex reasoning to MCP server Use Cases:
- Complex SQL query generation (BigQuery server)
- Multi-step workflow planning (test-intelligence server)
- Context-aware suggestions (google-docs server)
Implementation Pattern:
{
"method": "sampling/createMessage",
"params": {
"messages": [{ "role": "user", "content": "Generate optimized BigQuery query" }],
"systemPrompt": "You are a BigQuery expert...",
"modelPreferences": {
"hints": [{ "name": "claude-3-5-sonnet" }],
"costPriority": 0.5,
"speedPriority": 0.3
},
"maxTokens": 1000
}
}
2. Prompts (Pre-defined Workflows)
Current Status: Not implemented Purpose: Standardized task templates Use Cases:
- "Review handler for MCP compliance" → mcp-protocol-expert
- "Optimize BigQuery query" → google-bigquery-expert
- "Deploy custom function" → google-appsscript-expert
Implementation Pattern:
{
"method": "prompts/get",
"params": {
"name": "review_for_mcp_compliance",
"arguments": {
"file": "src/handlers/data.ts"
}
}
}
3. Resources (Structured Data Exposure)
Current Status: Partially implemented (schema://tools/{name}) Purpose: Expose project data to Claude Enhancement Opportunities:
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.
- 8d ago First seen · 452 lines · 17 tokens per session scan A f1bf53ba9153
claude-config-optimizer is an agent published in the GitHub repository khill1269/servalsheets-v2 (0 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 2,790 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to claude-config-optimizer, differing in 24 lines, and is treated as a copy.
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