Score breakdown0 Version history1 Source
Category Weight Category score Contribution
Security prompt, exec, net, exfil, eval
35%
100
35.0 pts
Supply chain hash, typosquat, maintainer, lockfile
20%
100
20.0 pts
Maintenance staleness, pinning, CI
15%
100
15.0 pts
Transparency SKILL.md, perms, README
15%
100
15.0 pts
Community installs, verify, response
15%
100
15.0 pts
Findings & checks · 0 flagged
Security score 100 · 0 findings
✓ — All security checks passedNo findings in this category for the latest scan. pass
Supply chain score 100 · 0 findings
✓ — All supply chain checks passedNo findings in this category for the latest scan. pass
Maintenance score 100 · 0 findings
✓ — All maintenance checks passedNo findings in this category for the latest scan. pass
Transparency score 100 · 0 findings
✓ — All transparency checks passedNo findings in this category for the latest scan. pass
Community score 100 · 0 findings
✓ — All community checks passedNo findings in this category for the latest scan. pass
Every scanned point with the score it earned and what moved between them.
1 scans · 90 days d97a2e4 latest
Jun 23, 2026 100 d97a2e4
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
library/specializations/ai-agents-conversational/skills/prompt-compression/SKILL.md · 1 file library/specializations/ai-agents-conversational/skills/prompt-compression/SKILL.md 1.2 KB · Markdown Rendered Raw ⧉ Copy
name : prompt-compression
description : Token-efficient prompt compression techniques for cost optimization
allowed-tools :
- Read
- Write
- Edit
- Bash
- Glob
- Grep
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 compressionSummary Compression : LLM-based summarizationSelective Context : Relevant section extractionToken Pruning : Remove low-importance tokensDocument Filtering : Pre-retrieval filteringConfiguration 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