Intelligence TransferCAUTION
🌊 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: intelligence-transfer
description: Publish or fetch learned patterns across projects via IPFS (Pinata) -- the cross-project pattern transfer that hooks_transfer enables
argument-hint: "<store|load|from-project> [--cid <ipfs-cid>] [--source <project-path>]"
allowed-tools: mcp__plugin_ruflo-core_ruflo__hooks_transfer mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-search mcp__plugin_ruflo-core_ruflo__hooks_intelligence_pattern-store mcp__plugin_ruflo-core_ruflo__neural_patterns mcp__plugin_ruflo-core_ruflo__neural_status Bash
---
# Intelligence Transfer
Cross-project pattern sharing via IPFS. Lets a different project — or a different machine — fetch and apply patterns this project has already learned.
## Why this exists
Most learning is project-local. `hooks_transfer` is the escape hatch: publish patterns to IPFS, share the CID, and any peer can ingest them. Equivalent to "a deploy artifact for what your agents have learned."
## Prerequisite
```bash
# Required env var (or equivalent endpoint config)
echo $PINATA_API_JWT
```
If unset, `hooks_transfer` returns a structured `success: false` with `error: "PINATA_API_JWT not configured"`. Configure before running this skill.
## Workflows
### Publish current project's patterns
```bash
# Inspect what's stored locally first
mcp tool call neural_patterns --json -- '{"list": true}'
# Publish to IPFS — returns a CID
mcp tool call hooks_transfer --json -- '{"action": "store"}'
```
The response includes the IPFS CID. Save it; share it with peers who need the patterns.
### Fetch + apply a peer's patterns
```bash
# Pull a CID and apply locally
mcp tool call hooks_transfer --json -- '{"action": "load", "cid": "QmXyz..."}'
# Verify they landed
mcp tool call hooks_intelligence_pattern-search --json -- '{"query": "<test>", "limit": 5}'
```
Patterns are merged with local state, not replaced. Conflicts are resolved by recency (newer wins).
### Mirror an entire project's patterns
```bash
# Read patterns from a sibling project on disk and republish under a new CID
mcp tool call hooks_transfer --json -- '{"action": "from-project", "source": "/path/to/peer-project"}'
```
Useful for consolidating learnings across a monorepo or a fleet of related projects.
## When to use this skill
- **Before a fresh project starts** — fetch the relevant patterns from a parent project so the new project's agents start with prior knowledge instead of cold.
- **After a major learning milestone** — publish so other projects benefit.
- **When debugging a regression** — fetch a known-good pattern set to compare against.
## When NOT to use
- Daily — it's a heavyweight operation. `agentdb_consolidate` does the local equivalent.
- For sensitive patterns — IPFS is public by default. Pinata pinning does NOT make patterns private. Strip PII (use `aidefence_has_pii` first) before publishing.
## Caveats
- IPFS CIDs are content-addressed; republishing the same pattern set gives you the same CID.
- Patterns are stored as JSON; they include only the embedding hashes + metadata, not raw text. Decoding requires the same SONA / MicroLoRA adapter version that produced them.
- This skill does NOT publish AgentDB rows — only the intelligence-side patterns. To ship full memory, use `agentdb_*` export tools (out of scope here).
## Related
- `ruflo-agentdb` ADR-0001 §"Namespace convention" — defines `pattern` namespace that this transfer reads from
- `neural-train` skill — produces the patterns that this skill shipsTrust 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__intelligence-transfer.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 Intelligence Transfer 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 Intelligence Transfer 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 Intelligence Transfer 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.