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MomoDaviluke
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continuous-agent-loop

Automates continuous agent loops with quality gates, evals, and recovery controls.

2|Updated Mar 3, 2026
Search Tags:#recovery#ai-agent#workflow-orchestration#quality-gates#eval-harness#continuous-agent-loop
One-click install
npx skills add https://github.com/MomoDaviluke/star-citizen-promotion --skill continuous-agent-loop-momodaviluke
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: continuous-agent-loop
Source: https://github.com/MomoDaviluke/star-citizen-promotion/tree/main/.agents/skills/ecc/continuous-agent-loop
Command: npx skills add https://github.com/MomoDaviluke/star-citizen-promotion --skill continuous-agent-loop-momodaviluke

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automating continuous autonomous agent loops with built-in quality gates, evals, and recovery controls to reduce drift, manual tuning, and operational failures.

Core Features & Use Cases

  • Patterns for loop orchestration across RFC decomposition, quality gates, eval harnesses, and session persistence.
  • Suitable for AI agent workflows that demand deterministic progression, safe recovery, and modular loop templates in production environments.
  • Real-world use cases include long-running decision loops, iterative plan refinement, and recovery-driven failover strategies.

Quick Start

Provide a configured loop skeleton that starts with RFC decomposition and selects the appropriate loop pattern for the current task.

Frequently Asked Questions about continuous-agent-loop

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate continuous autonomous agent loops in production?▼

Continuous autonomous agent loops are orchestrated by applying structured loop patterns that enforce deterministic progression, modular templates, and session persistence. This approach reduces manual tuning and operational failures in production AI workflows.

What is the best way to add recovery controls to an AI agent workflow?▼

Recovery controls are added by integrating safe failover strategies and modular loop templates directly into the agent workflow. This ensures robust production behavior and allows the system to recover autonomously from operational failures.

How do I decompose an RFC for sequential execution in an AI agent?▼

RFC decomposition for sequential execution starts with a configured loop skeleton that breaks down the RFC and selects the appropriate loop pattern for the current task. This enforces deterministic progression throughout the workflow.

Can I use quality gates and eval harnesses within an autonomous agent loop?▼

Quality gates and eval harnesses can be integrated within autonomous agent loops to ensure robust production behavior. The orchestration pattern enforces structured loop selection and supports logging components to monitor deterministic progression.

Why does my long-running decision loop experience drift and manual tuning issues?▼

Long-running decision loops experience drift due to a lack of built-in quality gates and structured loop orchestration. Implementing continuous autonomous agent loops with deterministic progression and session persistence reduces this operational drift.

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