LLM Context Window Optimizer

Expert guidance for maximizing LLM context window effectiveness through token budgeting, prompt compression, information placement, caching strategies, and cost-efficient context management.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.1

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.4

Practical Utility (15%)

8.6

Completeness (10%)

8.3

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.5

Best for

▸Optimizing RAG context assembly to fit within 128K token windows while maintaining retrieval quality
▸Reducing GPT-4 API costs by 40-60% through intelligent token budgeting and prompt compression
▸Implementing conversation memory hierarchies for multi-turn chat applications with 200K context limits
▸Designing semantic caching strategies to avoid redundant context window filling
▸Auditing existing LLM implementations for context window efficiency and cost optimization

What you'll get

●Token budget breakdown with percentages allocated to system instructions (15%), user query (20%), retrieved context (50%), conversation history (10%), and output reservation (25%)
●Compressed prompt template reducing original 15K tokens to 8K while maintaining semantic completeness through progressive summarization and relevance filtering
●Caching strategy implementation plan identifying 60% of prompt components as cacheable, with semantic similarity thresholds and cache invalidation rules

Not designed for ↓

×Training custom LLMs or fine-tuning model architectures
×Building RAG retrieval systems or vector database implementations
×General prompt engineering for creative writing or marketing copy
×Model selection or performance benchmarking across different LLM providers

Expects

Current prompt structure, target model context limits, token usage patterns, and specific cost or performance optimization goals.

Returns

Token budget allocation plan, compressed prompt templates, information placement strategy, and caching implementation recommendations with measurable efficiency gains.

Evidence Policy

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

llmcontext-windowtoken-optimizationprompt-compressionragcachingcost-optimizationtoken-budgetingmodel-routingchunkingconversation-memoryinformation-retrieval

Research Foundation: 8 sources (3 academic, 3 official docs, 1 community practice, 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

Works well with

AI Cost OptimizerPlatinum AI Model Routing StrategistGold Prompt Engineering StrategistGold RAG Architecture DesignerGold Token Optimization StrategistGold

Need more depth?

Specialist skills that go deeper in areas this skill touches.

RAG Chunking StrategistGold Vector Database OptimizationGold LLM Observability EngineerGold

Common Workflows

RAG Cost Optimization Pipeline

Design RAG system, optimize context window usage, then implement cost monitoring and budget controls

RAG Architecture Designer→llm-context-window-optimizer→AI Cost Optimizer

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