
agent-coordination
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.
SKILL.md
name: agent-coordination description: Coordinate multiple specialized Skills and Task Agents through parallel, sequential, swarm, hybrid, or iterative execution strategies. Use when orchestrating multi-worker workflows, managing dependencies, or optimizing complex task execution with quality gates.
Agent Coordination
Coordinate multiple specialized Skills and Task Agents through strategic execution patterns.
Quick Reference
- Strategies - Parallel, sequential, swarm, hybrid, iterative
- Skills vs Agents - When to use each
- Quality Gates - Validation checkpoints
- Examples - Coordination examples
When to Use
- Orchestrating multi-worker workflows
- Managing dependencies between tasks
- Optimizing complex task execution
- Quality-critical work with validation
CRITICAL: Skills vs Task Agents
Skills (via Skill tool): Instruction sets that guide Claude
- Examples: rust-code-quality, architecture-validation, plan-gap-analysis
Agents (via Task tool): Autonomous sub-processes that execute
- Examples: code-reviewer, test-runner, debugger, loop-agent
See strategies.md for coordination patterns and skills-agents.md for when to use each.
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