prompt-compression
Token-efficient prompt compression techniques for cost optimization
npx skills add a5c-ai/babysitter --skill prompt-compression --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Capabilities
- Target Processes
- Implementation Details
- Compression Techniques
- Configuration Options
- Best Practices
- Dependencies
What does the prompt-compression skill do?
Token-efficient prompt compression techniques for cost optimization
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill prompt-compression --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.