Memory SearchCAUTION
🌊 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
Overview
🌊 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
ef7d4f0535e5OBSERVED · 2026-09-26What it tells the agent
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-search
description: SOTA semantic search — hybrid (sparse+dense), Graph RAG multi-hop, MMR diversity reranking, recency weighting
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_list mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search_unified mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize
argument-hint: "<query> [--hybrid] [--graph-rag] [--namespace NAME]"
---
# Memory Search (SOTA)
State-of-the-art semantic search across Ruflo memory with multiple retrieval strategies.
## Strategy Selection
Choose based on query type:
- **Default** (dense): fast single-hop semantic match
- **--hybrid**: sparse + dense with RRF fusion (20-49% better for keyword+semantic queries)
- **--graph-rag**: multi-hop knowledge retrieval (30-60% better for reasoning queries)
## Steps
1. **Parse query and flags** — extract search text and strategy flags from arguments
2. **Select retrieval strategy**:
**Dense search (default)**:
```bash
npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10
```
Or via MCP: `mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", namespace: "NAMESPACE", limit: 10 })`
**Hybrid search** (when --hybrid or query has specific keywords):
```bash
npx ruvector search "QUERY" --hybrid --limit 10
```
**Graph RAG** (when --graph-rag or multi-hop reasoning needed):
```bash
npx ruvector search "QUERY" --graph-rag --limit 10
```
**Smart retrieval** (when --smart or complex recall needed):
```bash
npx @claude-flow/cli@latest memory search --query "QUERY" --smart --limit 10
```
Or via MCP: `mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", smart: true, limit: 10 })`
Applies 5-phase pipeline: query expansion, RRF fusion, recency boost, MMR diversity, session round-robin.
Best for: multi-session recall, temporal queries, diverse result sets.
**Unified cross-namespace**:
`mcp__plugin_ruflo-core_ruflo__memory_search_unified({ query: "QUERY", limit: 10 })`
3. **Apply MMR reranking** — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance
4. **Apply recency weighting** — boost recent entries with exponential decay (0.95/day)
5. **Synthesize context** (for complex queries):
`mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize({ query: "QUERY", sources: ["patterns", "tasks", "solutions"] })`
6. **Present results** — ranked by composite score (relevance * diversity * recency), with source namespace attribution
## Namespace Guide
| Namespace | Best For |
|-----------|----------|
| `patterns` | "How did we handle X?" |
| `tasks` | "What was the context for Y?" |
| `solutions` | "How did we fix Z?" |
| `feedback` | "What did the user prefer?" |
| `security` | "Known vulnerabilities in..." |
| (omit) | Search all namespaces |Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
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.
ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__memory-search.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-26 | ef7d4f0535e5 | CAUTION | B | 89 | first audit |
Questions
What does the Memory Search skill do?
🌊 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
Is Memory Search safe to install?
With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Memory Search access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (ef7d4f0535e5), read on 2026-09-26. The repository is watched, and a new audit runs when it changes — this is the first audit.