Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Install
npx skills add https://github.com/ruvnet/ruflo --skill embeddingsSKILL.md
Embeddings Skill
Purpose
Vector embeddings for semantic search and pattern matching with HNSW indexing.
Features
| Feature | Description |
|---|---|
| sql.js | Cross-platform SQLite persistent cache (WASM) |
| HNSW | 150x-12,500x faster search |
| Hyperbolic | Poincare ball model for hierarchical data |
| Normalization | L2, L1, min-max, z-score |
| Chunking | Configurable overlap and size |
| 75x faster | With agentic-flow ONNX integration |
Commands
Initialize Embeddings
npx claude-flow embeddings init --backend sqlite
Embed Text
npx claude-flow embeddings embed --text "authentication patterns"
Batch Embed
npx claude-flow embeddings batch --file documents.json
Semantic Search
npx claude-flow embeddings search --query "security best practices" --top-k 5
Memory Integration
# Store with embeddings
npx claude-flow memory store --key "pattern-1" --value "description" --embed
# Search with embeddings
npx claude-flow memory search --query "related patterns" --semantic
Quantization
| Type | Memory Reduction | Speed |
|---|---|---|
| Int8 | 3.92x | Fast |
| Int4 | 7.84x | Faster |
| Binary | 32x | Fastest |
Best Practices
- Use HNSW for large pattern databases
- Enable quantization for memory efficiency
- Use hyperbolic for hierarchical relationships
- Normalize embeddings for consistency
Related skills
azure-kustomicrosoft605KQuery and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.supabase-postgres-best-practicessupabase421KPostgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or imporprisma-database-setupprisma321KGuides for configuring Prisma with different database providers (PostgreSQL, MySQL, SQLite, MongoDB, etc.). Use when setting up a new project, changing databases, or troubleshooting connection issues. Triggers on "configure postgres", "connect to mysql", "setup mongodb", "sqlite setup".prisma-client-apiprisma320KPrisma Client API reference covering model queries, filters, operators, and client methods. Use when writing database queries, using CRUD operations, filtering data, or configuring Prisma Client. Triggers on "prisma query", "findMany", "create", "update", "delete", "$transaction".