Ingest and normalize market data into OHLCV vectors with HNSW indexing.
npx skills add https://github.com/ruvnet/claude-flow --skill market-ingest
Please help me install this Agent Skill. Skill: market-ingest Source: https://github.com/ruvnet/claude-flow/tree/main/plugins/ruflo-market-data/skills/market-ingest Command: npx skills add https://github.com/ruvnet/claude-flow --skill market-ingest
Raw market data arrives in inconsistent formats and cannot be searched for similar patterns. This Skill fetches price and volume data for a symbol, normalizes it into comparable OHLCV vectors, and indexes them for fast nearest-neighbor pattern search.
Ingest market data for symbol AAPL and prepare it for pattern detection and similarity search.
High-intent search queries and answers about installing and using this skill.
Fetch OHLCV data for the symbol from a REST API, CSV file, or manual input, then normalize prices to relative values and volume to Z-scores. Store the result in the market-data namespace and add each candle vector to the HNSW index for nearest-neighbor search.
Convert open, high, low, and close to relative changes against the previous close or open, and compute volume as a Z-score against rolling mean and standard deviation. Each candle is then encoded as a 64-dimension padded vector.
HNSW (Hierarchical Navigable Small World) indexing enables fast approximate nearest-neighbor search over candle vectors. It lets you find historical candles with similar price and volume patterns without scanning the entire dataset.
Yes, the skill supports REST API, CSV file, or manual input as data sources. After loading, the same normalization, storage, and HNSW indexing steps apply regardless of the source.
The memory_* tool family routes data by namespace, which fits the market-data namespace used for symbol and date keys. The agentdb_hierarchical-* family routes by tier and ignores namespace strings, so it is not suitable here.
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