Prepare market history for pattern detection and comparison. Import opening, high, low, and closing prices plus volume, validate and normalize the values, then store them by symbol and date for later retrieval. It does not place orders.
Ingest and normalize market data into OHLCV vectors stored by symbol and date for later pattern detection. Use when you need to load price and volume history for a symbol before running pattern detection or comparison. Read-only analysis; it never places orders.
market-ingest imports price and volume history for a market symbol and prepares it for pattern detection and comparison. It handles OHLCV: opening, high, low, and closing prices, plus trading volume. It checks data obtained through a REST API, CSV, or manual input, converts prices into relative values, and transforms volume using a moving average and its variation. Each candlestick is then represented as a sequence of 64 numbers and stored with the symbol and date as its key.
Use it to gather historical data before examining price patterns, or to prepare data with different price levels for comparison. After importing, it summarizes the number of records, date range, price range, and average volume so you can check what was loaded.
It uses a configured data source and Ruflo memory storage and search tools. Data is stored in the market-data namespace. Rows with invalid numbers or a high below the low are excluded. It prepares data for analysis and does not place orders or determine order quantities.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Fetch market data for a symbol, normalize to OHLCV vectors, and store them by symbol and date. Ingest is read-only analysis: nothing here places or sizes an order.
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.
(open - prev_close) / prev_close(high - open) / open(low - open) / open(close - open) / openmcp__plugin_ruflo-core_ruflo__embeddings_generate (NOT embeddings_embed — that tool name does not exist).mcp__plugin_ruflo-core_ruflo__memory_store({ key, value, 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.symbol-SYMBOL-DATE) with memory_search/memory_list on the market-data namespace. Do not load candles into the ruvllm_hnsw_* router: it is a WASM router for a handful of patterns (the tool documents a limit of about 11), not a history index. For nearest-neighbour search over candle windows use ruflo-ruvector (vector-embed), or compare windows in code.npx @claude-flow/cli@latest memory store --namespace market-data --key "symbol-SYMBOL-DATE" --value "OHLCV_JSON"
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.