ruvnet__ruflo/plugins/ruflo-intelligence/README.md

ruflo-intelligence

User-facing surface for Ruflo's self-learning system. Wraps 29 intelligence-related MCP tools across four families into discoverable skills, commands, and the canonical 4-step pipeline (RETRIEVE → JUDGE → DISTILL → CONSOLIDATE). Coordinates with ruflo-agentdb (namespace convention), ruflo-ruvector (trajectory recording substrate), and ruflo-browser (consumes trajectory hooks for session replay).

Status: ADR-0001 implemented. Plugin v0.3.0 targets @claude-flow/cli v3.6.x.

Install

/plugin marketplace add ruvnet/ruflo
/plugin install ruflo-intelligence@ruflo

Compatibility

• CLI: pinned to @claude-flow/cli v3.6 major+minor.
• Verification: bash plugins/ruflo-intelligence/scripts/smoke.sh is the contract.

Tool inventory

Family	Count	Source
neural_*	6	v3/@claude-flow/cli/src/mcp-tools/neural-tools.ts:195, 312, 413, 539, 651, 706
hooks_intelligence_* (incl. dispatcher + reset)	10	v3/@claude-flow/cli/src/mcp-tools/hooks-tools.ts:2093, 2226, 2296, 2355, 2404, 2556, 2634, 2741, 2952, 3027
Routing & meta hooks (hooks_route, hooks_explain, hooks_pretrain, hooks_build-agents, hooks_metrics, hooks_transfer)	6	hooks-tools.ts:884, 1062, 1420, 1499, 1593, 1664
hooks_model-* (3-tier routing)	3	hooks-tools.ts:3797, 3844, 3879
ruvllm_sona_* + ruvllm_microlora_*	4	v3/@claude-flow/cli/src/mcp-tools/ruvllm-tools.ts:142, 169, 192, 222
Total	29	—

The 4-step intelligence pipeline

CLAUDE.md describes the V3 intelligence loop as four discrete phases. This plugin operationalizes them:

Step	What happens	Tools
RETRIEVE	Pull relevant patterns + past trajectories from HNSW index	hooks_intelligence_pattern-search, agentdb_pattern-search, agentdb_semantic-route
JUDGE	Score retrieved candidates with verdicts (success / failure / partial)	hooks_intelligence_attention, neural_predict, hooks_explain
DISTILL	Extract the key learnings via LoRA / SONA adaptation	ruvllm_sona_adapt, ruvllm_microlora_adapt, neural_train, hooks_intelligence_learn
CONSOLIDATE	Prevent catastrophic forgetting via EWC++	agentdb_consolidate, ruvllm_microlora_adapt --consolidate, neural_compress

For an end-to-end run:

hooks_pretrain
  → hooks_intelligence_trajectory-start
    → (each step) hooks_intelligence_trajectory-step
  → hooks_intelligence_trajectory-end
  → hooks_intelligence_learn
  → ruvllm_sona_adapt    # DISTILL
  → agentdb_consolidate  # CONSOLIDATE
  → neural_compress      # storage efficiency

Cross-project pattern transfer (IPFS)

hooks_transfer is the substrate plugin's most underused capability. It publishes learned patterns to IPFS (via Pinata) so a different project — or a different machine — can fetch and apply them. Use the intelligence-transfer skill or call directly:

# Publish patterns from this project to IPFS
mcp tool call hooks_transfer --json -- '{"action": "store", "patterns": [...]}'

# Fetch and apply patterns from a CID
mcp tool call hooks_transfer --json -- '{"action": "load", "cid": "QmXyz..."}'

# Mirror an entire project's patterns
mcp tool call hooks_transfer --json -- '{"action": "from-project", "source": "/path/to/project"}'

Prerequisite: PINATA_API_JWT (or the equivalent endpoint env vars) must be configured. Without it, hooks_transfer returns a structured success: false with the missing-config error.

Hook integration

Several Claude Code hooks fire intelligence-side writes:

Hook	Tool invoked	Target
pre-task	hooks_route + hooks_intelligence_pattern-search	RETRIEVE phase
post-task --train-neural	agentdb_pattern-store (ReasoningBank) → falls back to memory_store --namespace pattern	DISTILL phase, writes to pattern namespace
pretrain (one-shot)	hooks_pretrain → seeds memory_store --namespace patterns	Bootstrap, writes to patterns namespace (plural)
Trajectory hooks (ruvector substrate)	intelligence_trajectory-*	Recorded by ruflo-ruvector; consumed by this plugin's pattern-store

Pluralization gotcha: ReasoningBank fallback writes to pattern (singular). The pretrain hook writes to patterns (plural). They are different namespaces. See ruflo-agentdb ADR-0001 §"Namespace convention" for the canonical contract.

Namespace coordination with ruflo-agentdb

This plugin defers to ruflo-agentdb ADR-0001 for namespace conventions. Three reserved namespaces are read by the intelligence pipeline:

Namespace	Read by	Source
pattern	hooks_intelligence_pattern-search, agentdb_pattern-search	ReasoningBank fallback target
patterns (plural)	hooks_pretrain, neural_train corpus	distinct from pattern
claude-memories	memory_search_unified (default include)	Claude Code auto-memory bridge

Do not invent new top-level namespaces for intelligence purposes — the convention is owned upstream.

EWC++ consolidation

The plugin claims EWC++ consolidation; here's how to actually invoke it:

1. At trajectory end, call hooks_intelligence_learn to register the outcome.
2. Periodically (or after N task completions), call agentdb_consolidate to fold patterns into the long-term store under EWC++ semantics.
3. For SONA / MicroLoRA adapters specifically, call ruvllm_microlora_adapt with the --consolidate flag to apply Elastic Weight Consolidation on the adapter's weight deltas. This prevents catastrophic forgetting when the adapter is trained on a new domain.

Without these calls, fresh trajectories overwrite older patterns without protection — the system "forgets". The pipeline diagram above bakes consolidation into step 4 deliberately.

MoE (Mixture of Experts) routing

hooks_intelligence accepts a mode parameter that selects the active learning architecture:

Mode	When to use
balanced (default)	General-purpose: SONA + HNSW retrieval, no MoE specialization
sona	Single-domain specialization with SONA adaptation
moe	Multi-domain expert routing — recommended when tasks span 3+ distinct domains
hnsw	Pure pattern retrieval, no online adaptation

Configure once via mcp tool call hooks_intelligence -- '{"mode": "moe", "enableSona": true}' and let the dispatcher route subsequent learning calls.

Commands

• /intelligence — Dashboard: stats, metrics, model-tier distribution, routing rationale on demand
• /neural — Neural training and prediction (train, status, patterns, predict, optimize, compress)

Skills

• neural-train — Train SONA + MicroLoRA patterns from successful tasks
• intelligence-route — Route tasks using learned patterns; produces a hooks_explain rationale
• intelligence-transfer — Publish/fetch patterns via IPFS (hooks_transfer)

Architecture Decisions

• ADR-0001 — Optimize ruflo-intelligence (surface completeness, 4-step pipeline, IPFS transfer, namespace coordination)

Related Plugins

• ruflo-agentdb — substrate for HNSW + namespace contract; agentdb_pattern-* is this plugin's storage backend
• ruflo-ruvector — trajectory hooks substrate; intelligence_trajectory-* calls land in ruvector's persisted trajectories
• ruflo-browser — consumes trajectory hooks for session replay (ADR-0001 there)
• ruflo-daa — Dynamic Agentic Architecture; cognitive patterns feed routing as inputs

License

MIT