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 armelhbobdad · Agent Tool · ★ 91
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is bmad-module-skill-forge safe to install? View the security audit →
Featured in: Skill Creation & Meta-Tools
Skill Forge (SKF) Turn code and docs into instructions AI agents can actually follow. Skill Forge analyzes your code repositories, documentation, and developer discourse to bu
| Stars | 91 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 70.2846052401854/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-11 |
| Created | 2026-02-13 |
| Platforms | browser, python |
| Est. Tokens | ~17k |
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bmad-module-skill-forge is A standalone BMAD module that transforms code repositories, documentation websites, and developer discourse into agentskills.io-compliant, version-pinned, provenance-backed agent skills.. It is categorized as a Agent Tool with 91 GitHub stars.
bmad-module-skill-forge is primarily written in Python. It covers topics such as agent-skills, agents, agentskills.
You can find installation instructions and usage details in the bmad-module-skill-forge GitHub repository at github.com/armelhbobdad/bmad-module-skill-forge. The project has 91 stars and 10 forks, indicating an active community.
The top alternatives to bmad-module-skill-forge on Agent Skills Hub include claude-code-karma, Nexus-skills, dot-pi. 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: