market-pattern
Detect and classify candlestick patterns from ingested OHLCV data
Profile →npx skills add ruvnet/ruflo --skill market-pattern --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 Pattern Scan ingested OHLCV data for known candlestick patterns, classify them by type and reliability, and store for future reference. ## When to use When you need to identify candlestick patterns (doji, hammer, engulfing, head-shoulders, etc.) in market data. Requires data to be ingested first via `market-ingest`. ## Steps 1. **Load candles** -- call `mcp__plugin_ruflo-core_ruflo__memory_search` (or `memory_list`) on the `market-data` namespace to retrieve normalized OHLCV data for the symbol and period. The `memory_*` tool family routes by namespace; the `agentdb_hierarchical-*` family does NOT (it routes by tier), so use `memory_*` for namespaced reads. 2. **Scan for patterns** -- iterate through candle sequences looking for: - Single-candle: doji (open ~= close), hammer (long lower wick), inverted hammer - Two-candle: bullish/bearish engulfing - Three-candle: morning star, evening star, three white soldiers, three black crows - Multi-candle: head & shoulders, double top/bottom, cup & handle 3. **Classify** -- for each detection, assign: pattern name, type (reversal/continuation), direction (bullish/bearish), reliability score (0.0-1.0) 4. **Rank** -- sort by reliabili
- When to use
- Steps
- CLI alternative
npx @claude-flow/cli@latest memory search --query "bullish reversal patterns" --namespace market-patterns
npx @claude-flow/cli@latest memory store --key "pattern-AAPL-2026-05-04-doji" --value '{...}' --namespace market-patternsWhat does the market-pattern skill do?
Detect and classify candlestick patterns from ingested OHLCV data
How do I install it?
Run `npx skills add ruvnet/ruflo --skill market-pattern --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.