No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by microsoft · Agent Tool · ★ 166
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SkillLens safe to install? View the security audit →
✨ Overview SkillLens is a framework for systematically studying model-generated agent skills across their full lifecycle: experience generation → skill extraction → skill consumption. It is built to answer the core question: What makes model-generated skills actually useful to a target model, and what drives skill utility across the experience → extraction → consumption lifecycle? The framework provides: 🧪 Unified trajectory loading across five agent benchmarks (SWE-bench, ALFWorld, SpreadsheetBench, BFCL v4, SEAL-0) ⚙️ Two extraction methods — (single-pass baseline) and (per-trajectory mode extraction + hierarchical merge, the primary method in the paper) 🚀 Unified inference CLI (
| Stars | 166 |
| Forks | 18 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.5783016694011/100 |
| Last Updated | 2026-05-25 |
| Created | 2026-05-08 |
| Platforms | python |
| Est. Tokens | ~15k |
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SkillLens is SkillLens: a framework for studying model-generated agent skills across the full raw experience generation → skill extraction → skill consumption lifecycle.. It is categorized as a Agent Tool with 166 GitHub stars.
SkillLens is primarily written in Python.
You can find installation instructions and usage details in the SkillLens GitHub repository at github.com/microsoft/SkillLens. The project has 166 stars and 18 forks, indicating an active community.
SkillLens is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to SkillLens on Agent Skills Hub include claude-plugins, tutor-skills, repren. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: