awesome-harness-engineering — security grade SAFE, quality 56/100

Security audit verdict: SAFE · quality 56/100

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 ai-boost · MCP Server · ★ 4.4k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is awesome-harness-engineering safe to install? View the security audit →

About awesome-harness-engineering

Awesome Harness Engineering Curated resources, patterns, and templates for building reliable AI agent harnesses. Deutsch | English | Español | Fr

agent-harnessagent-memoryagent-orchestrationai-agent-harnessai-agentsawesome-listcontext-engineeringharness-engineeringmcp

Quick Facts

Stars4,399
Forks559
LanguagePython
CategoryMCP Server
Quality Score55.9534840682948/100
Open Issues209
Last Updated2026-09-20
Created2026-03-29
Platformsmcp, python
Est. Tokens~25k

Compatible Skills

These tools work well together with awesome-harness-engineering for enhanced workflows:

  • awesome-agent-harness — semantic(0.64)+complementary+rare_topics+same_lang+similar_pop+shared_platform (76%)
  • sdd-riper — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • cc-harness-skills — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (58%)
  • LLM-Agent-Harness-Survey — semantic(0.52)+complementary+rare_topics+similar_pop (57%)
  • datachain — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

awesome-harness-engineering alternative? Top 6 similar tools

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.

  • WrenAI by Canner · ⭐ 17.7k

    GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that t

  • holaOS by holaboss-ai · ⭐ 10.6k

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  • honcho by plastic-labs · ⭐ 7.0k

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  • awesome-agent-skills by heilcheng · ⭐ 6.2k

    Tutorials, Guides and Agent Skills Directories

  • pro-workflow by rohitg00 · ⭐ 2.8k

    Claude Code learns from your corrections: self-correcting memory that compounds over 50+ sessions. Context eng

  • agentic-context-engine by kayba-ai · ⭐ 2.6k

    🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai

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Frequently Asked Questions

What is awesome-harness-engineering?

awesome-harness-engineering is Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration.. It is categorized as a MCP Server with 4.4k GitHub stars.

What programming language is awesome-harness-engineering written in?

awesome-harness-engineering is primarily written in Python. It covers topics such as agent-harness, agent-memory, agent-orchestration.

How do I install or use awesome-harness-engineering?

You can find installation instructions and usage details in the awesome-harness-engineering GitHub repository at github.com/ai-boost/awesome-harness-engineering. The project has 4.4k stars and 559 forks, indicating an active community.

What are the best alternatives to awesome-harness-engineering?

The top alternatives to awesome-harness-engineering on Agent Skills Hub include WrenAI, holaOS, honcho. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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