--- name: intelligence-transfer-86bb20037ad4 description: Publish or fetch learned patterns across projects via IPFS (Pinata) -- the cross-project pattern transfer that hooks_transfer enables argument-hint: " [--cid ] [--source ]" allowed-tools: mcp__claude-flow__hooks_transfer mcp__claude-flow__hooks_intelligence_pattern-search mcp__claude-flow__hooks_intelligence_pattern-store mcp__claude-flow__neural_patterns mcp__claude-flow__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": "", "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 ships --- Source attribution: wodeh Original project: https://github.com/wodeh/ruflo-kimi/blob/257ccb7e80a2619f8af236554ae6f0ca362abfa6/plugins/ruflo-intelligence/skills/intelligence-transfer/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2024-2026 ruvnet Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wodeh Original project: https://github.com/wodeh/ruflo-kimi/blob/257ccb7e80a2619f8af236554ae6f0ca362abfa6/plugins/ruflo-intelligence/skills/intelligence-transfer/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: API Tools: Ipfs, Pinata Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c7016ab941e189387b885da862f9613ad3ed0a38314286e7a225610c0a838053