market-ingest
Ingest and normalize market data into OHLCV vectors with HNSW indexing
Profile →npx skills add ruvnet/ruflo --skill market-ingest --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Market Ingest Fetch market data for a symbol, normalize to OHLCV vectors, and store with HNSW indexing for fast pattern search. ## When to use 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. ## Steps 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: - Open: `(open - prev_close) / prev_close` - High: `(high - open) / open` - Low: `(low - open) / open` - Close: `(close - open) / open` - Volume: Z-score against rolling mean/std 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
- When to use
- Steps
- CLI alternative
npx @claude-flow/cli@latest memory store --namespace market-data --key "symbol-SYMBOL-DATE" --value "OHLCV_JSON"
What does the market-ingest skill do?
Ingest and normalize market data into OHLCV vectors with HNSW indexing
How do I install it?
Run `npx skills add ruvnet/ruflo --skill market-ingest --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ruvnet/ruflo, a repository with 67,015 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.