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continuous-agent-loop

by @affaan-mv
4.4(20)

Focuses on designing and implementing quality-assured continuous autonomous agent loop patterns, ensuring AI systems can continuously learn, iterate, and optimize for improved performance and reliability.

ai-agent-loopautonomous-agentsreinforcement-learningagent-orchestrationGitHub
Installation
npx skills add affaan-m/everything-claude-code --skill continuous-agent-loop
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Before / After Comparison

1 组
Before

AI agents may experience performance degradation or behavioral deviations during long-term operation, lacking effective monitoring and self-correction mechanisms.

After

Adopt a continuous autonomous agent loop model, integrating quality gates, evaluation, and feedback mechanisms to ensure stable and efficient long-term operation of AI agents.

SKILL.md

Continuous Agent Loop

This is the v1.8+ canonical loop skill name. It supersedes autonomous-loops while keeping compatibility for one release.

Loop Selection Flow

Start
  |
  +-- Need strict CI/PR control? -- yes --> continuous-pr
  |                                    
  +-- Need RFC decomposition? -- yes --> rfc-dag
  |
  +-- Need exploratory parallel generation? -- yes --> infinite
  |
  +-- default --> sequential

Combined Pattern

Recommended production stack:

  1. RFC decomposition (ralphinho-rfc-pipeline)
  2. quality gates (plankton-code-quality + /quality-gate)
  3. eval loop (eval-harness)
  4. session persistence (nanoclaw-repl)

Failure Modes

  • loop churn without measurable progress
  • repeated retries with same root cause
  • merge queue stalls
  • cost drift from unbounded escalation

Recovery

  • freeze loop
  • run /harness-audit
  • reduce scope to failing unit
  • replay with explicit acceptance criteria

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Installs4.0K
Rating4.4 / 5.0
Version
Updated2026年5月23日
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Compatible Platforms

🔧Claude Code
🔧OpenClaw
🔧OpenCode
🔧Codex
🔧Gemini CLI
🔧GitHub Copilot
🔧Amp
🔧Kimi CLI

Timeline

Created2026年3月16日
Last Updated2026年5月23日