/ai--llm-cost-optimizer

/ai--llm-cost-optimizer is a Claude Code skill in the AI & Agents section. Measures where your app spends tokens, then applies model routing, caching and prompt compression, keeping each change only if an eval shows quality holds.

Author's description

Cut LLM costs: model routing, caching, prompt compression, batching without quality loss

How to ask for it

/ai--llm-cost-optimizer our Claude API bill spiked, help me bring it down
/ai--llm-cost-optimizer add caching so we don't repeat identical model calls
/ai--llm-cost-optimizer route simple queries to a cheaper model

Install

curl -fsSL https://raw.githubusercontent.com/sgomez-dev/claude-skills/main/install.sh | bash

After installing with the script, type /ai--llm-cost-optimizer. Using Cursor, Windsurf or Codex? Platform guides

Demo coming soon

/ai--llm-evalLLM output evaluation
Sets up an evaluation suite for an AI feature: test cases, rubrics, code-based checks, LLM-as-judge with bias controls and a CI gate.
Built here
/ai--llm-observabilityLLM call monitoring
Instruments your AI app with traces, token and cost tracking, quality signals, dashboards and alerts for every model call.
Built here
/ai--mcp-serverBuilding an MCP server
Builds a Model Context Protocol server that exposes your project's functions, APIs or data to clients like Claude Code, with transport, auth and tests.
Built here
/ai--multi-agentMulti-agent system design
Designs a system of several AI agents with an orchestration pattern, contracts between agents and failure handling, or advises a single agent instead.
Built here
/ai--prompt-engineerBetter AI prompts
Reviews and rewrites your system prompts, user prompts and tool definitions, then gives an A/B comparison of the original and improved versions.
Built here
/ai--rag-evalRAG pipeline evaluation
Builds a test harness that measures a RAG pipeline: a golden question set, retrieval metrics, groundedness checks and a regression gate.
Built here

How to ask for it

Install

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