prompt-compression ยท diff
git:20260507.44b06c0 to git:20260601.da7723a
66 added, 66 removed. Audit A to A.
- ---
- name: prompt-compression
- 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
+ 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