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 huggingface · Codex Skill · ★ 750
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is upskill safe to install? View the security audit →
UPskill Generate and evaluate agent skills based on traces with agents. Create skills with teacher models (expensive/slow) that student models (cheap/fast) can use to perform harder tasks reliably. [!TIP] UPskill v2 - recommended default config file now runs evaluations on Hugging Face Jobs. Make sure to set your and use for job creation and result capture Quick Start Install upskill: Create a new skill Generate a skill with a teaching model and evaluate it on a student model. Benchmark a set of models against a skill. View the results later. Development checks This repo uses a CI flow inspired by with separate format, lint, typecheck, and test stages. Install dev dependencies: Run t
| Stars | 750 |
| Forks | 92 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 68.0219057551843/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-23 |
| Created | 2026-01-21 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~17k |
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upskill is Generate and evaluate agent skills for code agents like Claude Code, Open Code, OpenAI Codex. It is categorized as a Codex Skill with 750 GitHub stars.
upskill is primarily written in Python.
You can find installation instructions and usage details in the upskill GitHub repository at github.com/huggingface/upskill. The project has 750 stars and 92 forks, indicating an active community.
upskill is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to upskill on Agent Skills Hub include Claude-to-IM-skill, pi-skills, agent-skill-creator. 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: