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 · ★ 17.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is SkillOpt safe to install? View the security audit →
SkillOpt: Executive Strategy for Self-Evolving Agent Skills Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights. 🎬 SkillOpt Demo Video https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7 ▶ Watch the full demo on YouTube Install Requirements: Python 3.10+ Configure API Credentials Azure OpenAI (recommended): bash export AZUREOPENAIENDPOINT="https://your-resource.openai.azure.com/" Option 1: API key auth export AZUREOPENAIAPIKEY="your-key" Option 2: Azure CLI auth (
| Stars | 17,913 |
| Forks | 1,685 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 74.0364977272224/100 |
| Open Issues | 45 |
| Last Updated | 2026-09-30 |
| Created | 2026-05-08 |
| Platforms | python |
| Est. Tokens | ~15k |
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SkillOpt is SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts.. It is categorized as a Agent Tool with 17.9k GitHub stars.
SkillOpt is primarily written in Python. It covers topics such as agent-skills, self-evolving-agents.
You can find installation instructions and usage details in the SkillOpt GitHub repository at github.com/microsoft/SkillOpt. The project has 17.9k stars and 1685 forks, indicating an active community.
SkillOpt is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to SkillOpt on Agent Skills Hub include awesome-claude-skills, awesome-claude-code, humanizer. 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: