Token Optimization Strategist

Reduces LLM API costs through systematic token optimization — prompt compression, caching strategies, model routing, and batch processing — while maintaining output quality across OpenAI, Anthropic, and open-source models.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.3

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.5

Completeness (10%)

8.3

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.4

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
▸Build token-aware model routing between expensive and cheaper LLMs based on complexity

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

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

Expects

Current LLM usage data including API calls, token counts, monthly spend by model, and quality requirements for each use case.

Returns

Specific implementation plan with token reduction techniques, caching architecture, model routing logic, and projected 30-80% cost savings.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

token-optimizationllm-costsprompt-compressionprompt-cachingmodel-routingbatch-processingtiktokenfrugalgptllmlinguaapi-cost-reductionai-infrastructurecost-governance

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

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

Prompt Engineering StrategistGold

Works well with

AI Cost OptimizerPlatinum API Performance Testing ExpertGold Caching Strategy ArchitectGold LLM Context Window OptimizerGold Prompt Engineering StrategistGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

LLM Fine-Tuning StrategistGold AI Model Routing StrategistGold API Monetization StrategistGold

Common Workflows

LLM Cost Optimization Pipeline

Start with prompt quality optimization, then apply token reduction techniques, finally implement cost monitoring and governance

Prompt Engineering Strategist→token-optimization-strategist→AI Cost Optimizer

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