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 titanwings · Claude Skill · ★ 24.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is distilly safe to install? View the security audit →
🧬 Distilly Formerly: Colleague Skill / colleague-skill. Distill a person's experience, judgment, voice, and ways of working into a reusable Person Profile for AI agents and compatible bots. Messages · documents · interviews · public sources → Distilly → Person Profile → Agent / Bot 🧑💼 Your colleague quit, your mentor graduated, your teammate transferred — taking their whole playbook and context with them? 💞 Your family, old friends, partner drifting apart — and you want to hold on to the way it felt to be with them? 🌟 Your favorite author, idol, thinker you'll never meet — but you want to know what they'd say about your question? ✨ One project, many kinds of people.
| Stars | 24,786 |
| Forks | 2,146 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 62.4117140911792/100 |
| Open Issues | 56 |
| Last Updated | 2026-09-16 |
| Created | 2026-03-30 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
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distilly is Distilly — Distill how they think into reusable Skills for any Agent or Bot. Formerly Colleague Skill（原同事 Skill）.. It is categorized as a Claude Skill with 24.8k GitHub stars.
distilly is primarily written in Python. It covers topics such as agent-skills, agentic-ai, ai-agent.
You can find installation instructions and usage details in the distilly GitHub repository at github.com/titanwings/distilly. The project has 24.8k stars and 2146 forks, indicating an active community.
distilly is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to distilly on Agent Skills Hub include iPolloWork, agents, claude-skills. 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: