agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Ingest and normalize market data into OHLCV vectors with HNSW indexing
$ npx -y skills add ruvnet/ruflo --skill market-ingest --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/market-ingestContext preview
The summary Claude sees to decide when to auto-load this skill.
Ingest and normalize market data into OHLCV vectors with HNSW indexing
name: market-ingest description: Ingest and normalize market data into OHLCV vectors with HNSW indexing argument-hint: "<symbol> [--source api]" allowed-tools: Bash mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add mcp__plugin_ruflo-core_ruflo__embeddings_generate
Fetch market data for a symbol, normalize to OHLCV vectors, and store with HNSW indexing for fast pattern search.
When you need to ingest raw market data (price and volume) for a symbol and prepare it for pattern detection and similarity search. This is the first step before running pattern detection or comparison.
1. **Fetch data** -- retrieve OHLCV data for the symbol from the configured data source (REST API, CSV file, or manual input) 2. **Normalize** -- convert raw prices to relative values:
3. **Vectorize** -- encode each candle as a 64-dimension padded vector (5 normalized OHLCV values + padding). For semantic embeddings of pattern descriptions, use `mcp__plugin_ruflo-core_ruflo__embeddings_generate` (NOT `embeddings_embed` — that tool name does not exist). 4. **Store** -- call `mcp__plugin_ruflo-core_ruflo__memory_store --namespace market-data` to persist normalized OHLCV data with symbol+date keys. The `memory_*` tool family routes by namespace; the `agentdb_hierarchical-*` family routes by tier (`working|episodic|semantic`) and ignores namespace strings, so use `memory_*` here. 5. **Index** -- call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add` to add vectors to the HNSW index for nearest-neighbor search. 6. **Report** -- summarize: candles ingested, date range, price range, average volume
npx @claude-flow/cli@latest memory store --namespace market-data --key "symbol-SYMBOL-DATE" --value "OHLCV_JSON"
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/ruflo
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination