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loop-agent
by d-o-hub
A modular Rust-based self-learning episodic memory system for AI agents, featuring hybrid storage with Turso (SQL) and redb (KV), async execution tracking, reward scoring, reflection, and pattern-based skill evolution. Designed for real-world applicability, maintainability, and scalable agent workflows.
⭐ 3🍴 0📅 2026年1月23日
Manusで実行SKILL.md
name: loop-agent description: Execute workflow agents iteratively for refinement and progressive improvement until quality criteria are met. Use when tasks require repetitive refinement, multi-iteration improvements, progressive optimization, or feedback loops until convergence.
Loop Agent Skill
Execute workflow agents iteratively for refinement and progressive improvement until quality criteria are met.
Quick Reference
- Modes - Loop termination modes (fixed, criteria, convergence, hybrid)
- Patterns - Common loop patterns (refinement, test-fix, optimization)
- Configuration - Loop setup and templates
- Examples - Complete loop examples
When to Use
- Code needs iterative refinement until quality standards met
- Tests need repeated fix-validate cycles
- Performance requires progressive optimization
- Quality improvements need multiple passes
- Feedback loops necessary for convergence
NOT Appropriate For
- Single-pass tasks (use specialized agent)
- Purely parallel work (use parallel-execution)
- Simple linear workflows (use sequential)
- One-time analysis
Core Concepts
Loop Termination Modes
| Mode | Description | Use When |
|---|---|---|
| Fixed | Run exactly N iterations | Known number of passes needed |
| Criteria | Until success criteria met | Specific quality/performance targets |
| Convergence | Stop at diminishing returns | Optimal result unknown |
| Hybrid | Combine multiple conditions | Complex requirements |
See modes.md for detailed mode documentation and patterns.md for common loop patterns.
スコア
総合スコア
75/100
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