Memory ManagementCAUTION

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: memory-management
description: >
  AgentDB memory system with HNSW vector search.
  Use when: need to store patterns, search for solutions, semantic lookup.
  Skip when: no learning needed, ephemeral tasks.
---

# Memory Management Skill

## Purpose
AgentDB memory system with HNSW vector search.

## When to Trigger
- need to store patterns
- search for solutions
- semantic lookup

## When to Skip
- no learning needed
- ephemeral tasks

## Commands

### Store Data
Store a pattern in memory

```bash
npx @claude-flow/cli memory store --key "key" --value "value" --namespace patterns
```

### Search Data
Semantic search in memory

```bash
npx @claude-flow/cli memory search --query "search terms" --limit 10
```



## Best Practices
1. Check memory for existing patterns before starting
2. Use hierarchical topology for coordination
3. Store successful patterns after completion
4. Document any new learnings

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (4)

crates

plugin/agents

plugin/commands

plugin/skills

Gates applied: no_behavioural_pass.