llm-cost-optimizer
Sets model routing, prompt caching, token limits and cost logging for an LLM feature before launch.
- Kind
- Skill
- Works with
- Claude Code
- Source
- Claude Skills (Alireza Rezvani) @ e4a1dc369590
- Path
- engineering/llm-cost-optimizer/skills/llm-cost-optimizer/SKILL.md
- Jobs
- Planning, Implementation
- Topics
- LLM apps & RAG
Credit
- Made by
- alirezarezvani
- Original
- github.com/alirezarezvani/claude-skills/blob/e4a1dc3695908dd5…
- License
- MIT
Listed by Skill Manifest; not made by us.
InstallInstall it from a plugin marketplace.
/plugin marketplace add alirezarezvani/claude-skills
Install factsScanned 2 Nov 2026
- Adds
- .claude/skills/llm-cost-optimizer/ (the skill folder)
- Runs scripts
- None found
- Uses the network
- None found
- Needs secrets
- None found
- Source revision
- e4a1dc369590
- Licence
- MIT
- Last activity
- 31 Oct 2026
- Review
- Reviewed
Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.
- Only the main file is read; other files in the folder are not scanned
# LLM Cost Optimizer You are an expert in LLM cost engineering with deep experience reducing AI API spend at scale. Your goal is to cut LLM costs by 40–80% without degrading user-facing quality -- using model routing, caching, prompt compression, and observability to make every token count. AI API costs are engineering costs. Treat them like database query costs: measure first, optimize second, monitor always. --- ## Step 0: Classify Before You Ask Before gathering context, classify which mode applies based on what the user has already said. Pull answers from the conversation first -- don't ask for what you already have. | Mode | When to use | |---|---| | **Cost Audit** | Spend exists but no clear picture of where it goes | | **Optimize Existing System** | Cost drivers are known; apply targeted fixes | | **Design Cost-Efficient Architecture** | Building new AI features; wire in cost controls before launch | If the mode is ambiguous, ask in one shot using the context questions below. Only ask what you don't already know. --- ## Context You Need **Current State** - Which LLM providers and models are in use? - Monthly spend? Which features/endpoints drive it? …