Agent skill
Experiment design partner
Translate a hypothesis into an interpretable test with metrics and guardrails.
ClaudeCodexOpenClawData & Analytics
Dave AI
Prompt Compression Skill
Token-efficient prompt compression techniques for cost optimization
instructions.md
--- name: prompt-compression-6ef1afc08a12 description: Token-efficient prompt compression techniques for cost optimization allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:context-management, skill-area:prompt-engineering] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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 1. **LLMLingua**: Token-level compression 2. **Summary Compression**: LLM-based summarization 3. **Selective Context**: Relevant section extraction 4. **Token Pruning**: Remove low-importance tokens 5. **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--- name: prompt-compression-6ef1afc08a12 description: Token-efficient prompt compression techniques for cost optimization allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:context-management, skill-area:prompt-engineering] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development] --- # 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 1. **LLMLingua**: Token-level compression 2. **Summary Compression**: LLM-based summarization 3. **Selective Context**: Relevant section extraction 4. **Token Pruning**: Remove low-importance tokens 5. **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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