llm-token-optimization
See the main LLM Cost Optimization skill for comprehensive coverage of token economics and optimization strategies.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
LLM Token Optimization
This skill is covered in detail in the main LLM Cost Optimization skill.
Please refer to: 42-cost-engineering/llm-cost-optimization/SKILL.md
That skill covers:
- LLM pricing models (OpenAI, Anthropic, Google, Cohere)
- Token economics (input vs output tokens)
- Cost optimization strategies (model routing, prompt engineering, caching)
- Embedding and vector database costs
- RAG system cost breakdown
- Cost monitoring and attribution
- Budget controls and rate limiting
- Open-source model hosting trade-offs
- Tools for AI FinOps (Helicone, LangSmith, LiteLLM)
- Real-world case studies
Related Skills
-
42-cost-engineering/llm-cost-optimization(Main skill) -
44-ai-governance/model-risk-management -
42-cost-engineering/cost-observability
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-01-26
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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