awesome-loop-engineering: Build reliable recurring AI-agent systems: 1022 resources, 22 operational patterns, 22 loop contracts, 8

by ChaoYue0307 · Agent Tool · ★ 59

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

How to install awesome-loop-engineering

git clone https://github.com/ChaoYue0307/awesome-loop-engineering.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 58/100

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.

agent-evaluationagent-memoryagent-orchestrationagent-reliabilityagent-securityagentic-aiagentic-workflowsai-agentsautonomous-agentsawesome

Quick Facts

Stars59
Forks8
LanguagePython
CategoryAgent Tool
LicenseCC0-1.0
Quality Score58/100
Open Issues6
Last Updated2026-11-03
Created2026-06-09
Platformsclaude-code, python
Est. Tokens~25k

About awesome-loop-engineering

Awesome Loop Engineering A practical field guide and implementation kit for recurring, stateful, verified AI-agent systems. <img src="https://img.shields.io/badge/resources-1022-a78bfa"

Compatible Skills

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

  • cc10x — semantic(0.32)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)
  • manifest-dev — semantic(0.23)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)

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How this security grade is produced

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:

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