by ai-boost · MCP Server · ★ 5.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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. Full audit →
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 | 5,053 |
| Forks | 730 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 56/100 |
| Open Issues | 276 |
| Last Updated | 2026-11-04 |
| Created | 2026-03-29 |
| Platforms | mcp, python |
| Est. Tokens | ~25k |
Awesome Harness Engineering Curated resources, patterns, and templates for building reliable AI agent harnesses. Deutsch | English | Español | Fr
These tools work well together with awesome-harness-engineering for enhanced workflows:
Looking for a awesome-harness-engineering alternative? If you're comparing awesome-harness-engineering with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that t
Memory library for building stateful agents
Tutorials, Guides and Agent Skills Directories
Claude Code learns from your corrections: self-correcting memory that compounds over 50+ sessions. Context eng
🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai
List of agent orchestrators
Explore other popular mcp server tools:
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: