📦 Pattern Store & Export

/docs/intelligence--learning/pattern-store--export

Share learned patterns across projects, teams, and the community via the decentralized pattern marketplace.

Asset Type	Description	Use Case
Patterns	Learned strategies from ReasoningBank	Share what works across projects
Agent Configs	Optimized YAML configurations	Pre-tuned agents for specific domains
Workflows	Multi-step task templates	Reusable automation sequences
Embeddings	Pre-computed vector indexes	Skip bootstrap time on new projects
Hooks	Custom hook implementations	Extend system behavior

Export Commands

bash
# Export learned patterns to file
npx ruflo@latest memory export --format json --output ./patterns.json

# Export specific namespace
npx ruflo@latest memory export --namespace "security" --output ./security-patterns.json

# Export with embeddings (larger file, faster import)
npx ruflo@latest memory export --include-embeddings --output ./full-export.json

# Export agent configurations
npx ruflo@latest config export --scope project --output ./agent-configs.json

# Export session state
npx ruflo@latest session export --session-id "my-session" --output ./session.json

Import Commands

bash
# Import patterns from file
npx ruflo@latest memory import --input ./patterns.json

# Import and merge with existing (don't overwrite)
npx ruflo@latest memory import --input ./patterns.json --merge

# Import from another project
npx ruflo@latest hooks transfer --source-path ../other-project

# Import agent configurations
npx ruflo@latest config import --input ./agent-configs.json --scope project

# Restore session
npx ruflo@latest session restore --session-id "my-session"

Pattern Store (IPFS Marketplace)

Decentralized pattern marketplace for sharing and discovering community patterns.

Command	Description
`transfer-store search`	Search patterns by keyword, category, or rating
`transfer-store info`	Get detailed info about a pattern
`transfer-store download`	Download pattern with integrity verification
`transfer-store publish`	Publish your patterns to the store
`transfer-store featured`	Browse featured/curated patterns
`transfer-store trending`	See what's popular

bash
# Search for authentication patterns
npx ruflo@latest transfer-store search --query "authentication" --min-rating 4.0

# Download a pattern
npx ruflo@latest transfer-store download --id "auth-jwt-patterns-v2" --verify

# Publish your patterns
npx ruflo@latest transfer-store publish --input ./my-patterns.json --category "security"

Plugin Store

Discover and install community plugins from the live IPFS registry with 19 official plugins and live ratings via Cloud Function.

Command	Description
`plugins list`	List available plugins with live ratings
`plugins rate`	Rate a plugin (1-5 stars)
`transfer plugin-search`	Search plugins by type or category
`transfer plugin-info`	Get plugin details and dependencies
`transfer plugin-featured`	Browse featured plugins
`transfer plugin-official`	List official/verified plugins

bash
# List plugins with live ratings from Cloud Function
npx ruflo@latest plugins list

# Filter by type
npx ruflo@latest plugins list --type integration

# Rate a plugin
npx ruflo@latest plugins rate --name @claude-flow/embeddings --rating 5

# Search for MCP tool plugins
npx ruflo@latest transfer plugin-search --type "mcp-tool" --verified

# Get plugin info
npx ruflo@latest transfer plugin-info --name "semantic-code-search"

# List official plugins
npx ruflo@latest transfer plugin-official

Live IPFS Plugin Registry

The official plugin registry is hosted on IPFS with Ed25519 signature verification:

Property	Value
Live CID	`bafkreiahw4ufxwycbwwswt7rgbx6hkgnvg3rophhocatgec4bu5e7tzk2a`
Plugins	19 official plugins
Verification	Ed25519 signed registry
Gateways	Pinata, ipfs.io, dweb.link, Cloudflare

bash
# Fetch live registry directly
curl -s "https://gateway.pinata.cloud/ipfs/bafkreiahw4ufxwycbwwswt7rgbx6hkgnvg3rophhocatgec4bu5e7tzk2a"

IPFS Integration

Patterns and models are distributed via IPFS for decentralization and integrity.

Feature	Benefit
Content Addressing	Patterns identified by hash, tamper-proof
Decentralized	No single point of failure
Ed25519 Signatures	Cryptographic registry verification
Multi-Gateway	Automatic failover (Pinata, ipfs.io, dweb.link)
PII Detection	Automatic scanning before publish

bash
# Resolve IPNS name to CID
npx ruflo@latest transfer ipfs-resolve --name "/ipns/patterns.ruflo.io"

# Detect PII before publishing
npx ruflo@latest transfer detect-pii --content "$(cat ./patterns.json)"

Model & Learning Pattern Import/Export

Share trained neural patterns and learning models via IPFS.

Operation	Description
Export	Pin learning patterns to IPFS, get shareable CID
Import	Fetch patterns from any IPFS CID
Analytics	Track downloads and sharing metrics

bash
# Export a learning pattern to IPFS
curl -X POST "https://api.pinata.cloud/pinning/pinJSONToIPFS" \
  -H "Authorization: Bearer $PINATA_JWT" \
  -d '{
    "pinataContent": {
      "type": "learning-pattern",
      "name": "my-patterns",
      "patterns": [...]
    },
    "pinataMetadata": {"name": "ruflo-learning-pattern"}
  }'

# Import a pattern from IPFS CID
curl -s "https://gateway.pinata.cloud/ipfs/QmYourCIDHere"

# Via Cloud Function (when deployed)
curl "https://publish-registry-xxx.cloudfunctions.net?action=export-model" -d @model.json
curl "https://publish-registry-xxx.cloudfunctions.net?action=import-model&cid=QmXxx"

Supported Model Types

Type	Description	Use Case
`learning-pattern`	Agent learning patterns	Code review, security analysis
`neural-weights`	Trained neural weights	SONA, MoE routing
`reasoning-bank`	Reasoning trajectories	Few-shot learning
`agent-config`	Agent configurations	Swarm templates

Pre-trained Model Registry

Import pre-defined rule-based patterns for common tasks. 40 patterns across 8 categories (trigger-action rules, not ML-trained models).

Model	Category	Patterns	Accuracy	Use Case
`security-review-patterns`	security	5	94%	SQL injection, XSS, path traversal
`code-review-patterns`	quality	5	90%	SRP, error handling, type safety
`performance-optimization-patterns`	performance	5	89%	N+1 queries, memory leaks, caching
`testing-patterns`	testing	5	91%	Edge cases, mocking, contracts
`api-development-patterns`	api	5	92%	REST conventions, validation, pagination
`bug-fixing-patterns`	debugging	5	89%	Null tracing, race conditions, regressions
`refactoring-patterns`	refactoring	5	89%	Extract methods, DRY, value objects
`documentation-patterns`	documentation	5	90%	JSDoc, OpenAPI, ADRs

Registry CID: QmNr1yYMKi7YBaL8JSztQyuB5ZUaTdRMLxJC1pBpGbjsTc

bash
# Browse available models
curl -s "https://gateway.pinata.cloud/ipfs/QmNr1yYMKi7YBaL8JSztQyuB5ZUaTdRMLxJC1pBpGbjsTc" | jq '.models[].name'

# Import all models
npx ruflo@latest transfer import --cid QmNr1yYMKi7YBaL8JSztQyuB5ZUaTdRMLxJC1pBpGbjsTc

# Import specific category
npx ruflo@latest neural import --model security-review-patterns --source ipfs

# Use patterns in routing
npx ruflo@latest hooks route --task "review authentication code" --use-patterns

Benefits vs Fresh Install

Metric	Fresh Install	With Pre-trained
Patterns Available	0	40
Detection Patterns	~50-60%	40 rule-based patterns
Pattern Categories	0	8 categories
Issue Detection Rate	~60-70%	~90-95%
Time to First Insight	Discovery needed	Immediate

Pre-Built Pattern Packs

Pack	Patterns	Best For
security-essentials	45	Auth, validation, CVE patterns
testing-patterns	32	TDD, mocking, fixture strategies
performance-optimization	28	Caching, query optimization
api-development	38	REST, GraphQL, error handling
devops-automation	25	CI/CD, deployment, monitoring

bash
# Install a pattern pack
npx ruflo@latest transfer-store download --id "security-essentials" --apply

RuVector WASM Neural Training

Real WASM-accelerated neural training using @ruvector/learning-wasm and @ruvector/attention packages for state-of-the-art performance.

Component	Performance	Description
MicroLoRA	<3μs adaptation	Rank-2 LoRA with 105x faster than 100μs target
ScopedLoRA	17 operators	Per-task-type learning (coordination, security, testing)
FlashAttention	9,127 ops/sec	Memory-efficient attention mechanism
TrajectoryBuffer	10k capacity	Success/failure learning from patterns
InfoNCE Loss	Contrastive	Temperature-scaled contrastive learning
AdamW Optimizer	β1=0.9, β2=0.999	Weight decay training optimization

bash
# List available pre-trained models from IPFS registry
npx ruflo@latest neural list

# List models by category
npx ruflo@latest neural list --category security

# Train with WASM acceleration
npx ruflo@latest neural train -p coordination -e 100 --wasm --flash --contrastive

# Train security patterns
npx ruflo@latest neural train -p security --wasm --contrastive

# Benchmark WASM performance
npx ruflo@latest neural benchmark -d 256 -i 1000

# Import pre-trained models
npx ruflo@latest neural import --cid QmNr1yYMKi7YBaL8JSztQyuB5ZUaTdRMLxJC1pBpGbjsTc

# Export trained patterns to IPFS
npx ruflo@latest neural export --ipfs --sign

Benchmark Results

+---------------------+---------------+-------------+
| Mechanism           | Avg Time (ms) | Ops/sec     |
+---------------------+---------------+-------------+
| DotProduct          | 0.1063        | 9,410       |
| FlashAttention      | 0.1096        | 9,127       |
| MultiHead (4 heads) | 0.1661        | 6,020       |
| MicroLoRA           | 0.0026        | 383,901     |
+---------------------+---------------+-------------+
MicroLoRA Target (<100μs): ✓ PASS (2.60μs actual)

Training Options

Flag	Description	Default
`--wasm`	Enable RuVector WASM acceleration	`true`
`--flash`	Use Flash Attention	`true`
`--moe`	Enable Mixture of Experts routing	`false`
`--hyperbolic`	Hyperbolic attention for hierarchical patterns	`false`
`--contrastive`	InfoNCE contrastive learning	`true`
`--curriculum`	Progressive difficulty curriculum	`false`
`-e, --epochs`	Number of training epochs	`50`
`-d, --dim`	Embedding dimension (max 256)	`256`
`-l, --learning-rate`	Learning rate	`0.01`

🪝 Hooks, Event Hooks, Workers & Pattern Intelligence

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