Cut LLM API costs while keeping output quality high.
Token Optimization Strategist
OpenAI, Anthropic, Token Optimization
Best for
- ▸Reduce OpenAI GPT-4/GPT-3.5 API costs by 30-80% through prompt compression and model routing
- ▸Implement LLMLingua or similar compression for large document processing workflows
- ▸Design caching strategies for repeated prompt patterns across Anthropic Claude calls
- ▸Optimize batch processing workflows to leverage provider-specific pricing advantages
What you'll get
- ▸Token audit showing current usage patterns with specific compression techniques achieving 40% reduction in input tokens
- ▸Caching architecture design with Redis implementation reducing repeat calls by 60% for common queries
- ▸Model routing decision tree with complexity scoring that routes 70% of requests to cheaper models
Current LLM usage data including API calls, token counts, monthly spend by model, and quality requirements for each use case.
Specific implementation plan with token reduction techniques, caching architecture, model routing logic, and projected 30-80% cost savings.
What's inside
“You are a Token Optimization Strategist. You reduce LLM API costs by 30-80% while maintaining output quality through tokenizer expertise, prompt engineering, caching, model routing, and batch processing. - Master token economics across OpenAI, Anthropic, Google, Cohere: input/output cost asymmetry, ...”
Covers
Not designed for ↓
- ×General API performance optimization unrelated to token costs
- ×Training or fine-tuning models (focuses on inference optimization only)
- ×Non-LLM API cost optimization (databases, CDNs, compute resources)
- ×Prompt engineering for quality improvement without cost consideration
SupaScore
88.25▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (5 official docs, 2 paper, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Prerequisites
Use these skills first for best results.
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
LLM Cost Optimization Pipeline
Start with prompt quality optimization, then apply token reduction techniques, finally implement cost monitoring and governance
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