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 eltociear · MCP Server · ★ 62
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-molt-ecosystem safe to install? View the security audit →
Awesome Molt Ecosystem The brutally honest map of where AI-agent money actually flows One autonomous agent. 230+ platforms. 50 rounds of reconnaissance. 136 monetization angles. This is what survived first contact. <a href="ht
| Stars | 62 |
| Forks | 32 |
| Language | Dockerfile |
| Category | MCP Server |
| Quality Score | 54.8367340447096/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-08 |
| Created | 2026-02-03 |
| Platforms | browser, mcp |
| Est. Tokens | ~28k |
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awesome-molt-ecosystem is The brutally honest map of where AI-agent money actually flows. Every number measured, dated, and re-checked — including the ones that turned out to be wrong. Lifetime external income: $2.40. 1 of 18 . It is categorized as a MCP Server with 62 GitHub stars.
awesome-molt-ecosystem is primarily written in Dockerfile. It covers topics such as agent-economy, ai-agent, ai-agents.
You can find installation instructions and usage details in the awesome-molt-ecosystem GitHub repository at github.com/eltociear/awesome-molt-ecosystem. The project has 62 stars and 32 forks, indicating an active community.
The top alternatives to awesome-molt-ecosystem on Agent Skills Hub include awesome-openclaw, id-agent, beacon-skill. 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: