KS
Killer-Skills

continuous-agent-loop — how to use continuous-agent-loop how to use continuous-agent-loop, what is continuous-agent-loop, continuous-agent-loop vs autonomous-loops, continuous-agent-loop install, continuous-agent-loop setup guide, mcp continuous agent loops, claude code autonomous agent patterns, rfc-dag pattern implementation, ai agent quality gates

Verified
v1.0.0
GitHub

About this Skill

Ideal for Autonomous AI Agents requiring robust, self-correcting execution loops with quality control mechanisms. continuous-agent-loop is a canonical skill for v1.8+ that provides patterns for implementing continuous autonomous agent loops. It features a loop selection flow for different use cases and includes quality gates, evals, and recovery controls for building reliable AI agent systems.

Features

Provides a loop selection flow for sequential, continuous-pr, rfc-dag, and infinite patterns
Supersedes the `autonomous-loops` skill while maintaining backward compatibility
Includes built-in quality gates and evaluation controls for agent output
Implements recovery controls for handling agent loop failures
Supports RFC decomposition for complex task breakdown via the rfc-dag pattern

# Core Topics

how to use continuous-agent-loop what is continuous-agent-loop continuous-agent-loop vs autonomous-loops continuous-agent-loop install continuous-agent-loop setup guide mcp continuous agent loops
affaan-m affaan-m
[62.0k]
[7678]
Updated: 3/6/2026

↓ Quality Score

Top 5%
65
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add affaan-m/everything-claude-code/continuous-agent-loop

Agent Capability Analysis

The continuous-agent-loop MCP Server by affaan-m is an open-source Categories.official integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use continuous-agent-loop, what is continuous-agent-loop, continuous-agent-loop vs autonomous-loops.

Ideal Agent Persona

Ideal for Autonomous AI Agents requiring robust, self-correcting execution loops with quality control mechanisms.

Core Value

Enables agents to implement production-grade autonomous loops with built-in quality gates, evaluation protocols, and recovery controls. Supports multiple loop patterns including RFC decomposition, parallel generation, and CI/PR-controlled workflows for reliable continuous operation.

↓ Capabilities Granted for continuous-agent-loop MCP Server

Implementing strict CI/PR control workflows
Decomposing RFCs into executable DAGs
Running exploratory parallel generation tasks
Maintaining sequential execution with quality gates

! Prerequisites & Limits

  • Requires v1.8+ agent framework compatibility
  • Needs evaluation infrastructure for quality gates
  • Demands recovery control implementation
Project
SKILL.md
960 B
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8
SKILL.md
Readonly

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

text
1Start
2  |
3  +-- Need strict CI/PR control? -- yes --> continuous-pr
4  |                                    
5  +-- Need RFC decomposition? -- yes --> rfc-dag
6  |
7  +-- Need exploratory parallel generation? -- yes --> infinite
8  |
9  +-- 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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