ReddRadar
Agent skill
prompt-compression
Token-efficient prompt compression techniques for cost optimization
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/ai-agents-conversational/skills/prompt-compression
SKILL.md
Prompt Compression Skill
Capabilities
- Implement token-efficient prompt compression
- Design context pruning strategies
- Configure selective context inclusion
- Implement LLMLingua-style compression
- Design summary-based compression
- Create compression quality metrics
Target Processes
- cost-optimization-llm
- agent-performance-optimization
Implementation Details
Compression Techniques
- LLMLingua: Token-level compression
- Summary Compression: LLM-based summarization
- Selective Context: Relevant section extraction
- Token Pruning: Remove low-importance tokens
- Document Filtering: Pre-retrieval filtering
Configuration Options
- Compression ratio targets
- Quality threshold settings
- Token budget constraints
- Compression model selection
- Evaluation metrics
Best Practices
- Monitor quality vs compression tradeoff
- Test with representative prompts
- Set appropriate compression ratios
- Validate compressed prompt quality
- Track cost savings
Dependencies
- llmlingua (optional)
- tiktoken
- transformers
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