by vivekg13186 · Agent Tool · ★ 80
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/vivekg13186/Daisy-workflow.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 | 80 |
| Forks | 3 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69/100 |
| Last Updated | 2026-05-15 |
| Created | 2026-05-03 |
| Platforms | node |
| Est. Tokens | ~356k |
[!NOTE] Transforming Daisy into a more AI-centric workflow. A new release is currently in progress. Daisy AI Orchestrator is an AI orchestration platform built around a visual editor and a typed DSL. The runtime executes directed acyclic graphs (DAGs) of plugin actions, supports parallel execution with retries and batch fan-out, evaluates FEEL expressions in user-supplied placeholders, and...
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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: