prompt-compression
Token-efficient prompt compression techniques for cost optimization
Best use case
prompt-compression is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Token-efficient prompt compression techniques for cost optimization
Teams using prompt-compression should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/prompt-compression/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How prompt-compression Compares
| Feature / Agent | prompt-compression | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Token-efficient prompt compression techniques for cost optimization
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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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