by agodianel · Claude Skill · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/agodianel/esp32-claude-workbench.gitTaken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 81 |
| Forks | 7 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 68/100 |
| Last Updated | 2026-11-02 |
| Created | 2026-03-26 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
⚡ ESP32 Claude Workbench Deterministic AI workflow for ESP32 firmware development. Stop prompting. Start engineering. Quick Start · Skills · Playbooks · Testing · Contributing 🎯 What Is This? ESP32 Claude Workbench is a structured workflow system for using Claude Code in ESP32 firmware development. It turns ad-hoc AI prompting into a disciplined, testable, repeatable development process.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: