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trader-signal

Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

DeepseekModel Curated skill Quality Excellent · 90 v1.0.0

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Download .skill Standard format with system_prompt and model_config, ready for any agent framework
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name trader-signal description Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction allowed-tools Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_delete mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search argument-hint [--strategy NAME] [--symbols AAPL,MSFT] Generate trading signals using neural-trader's anomaly detection engine. Steps: Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader Scan for signals: npx neural-trader --signal scan --symbols <TICKERS> With a specific strategy: npx neural-trader --signal scan --strategy <name> --symbols <TICKERS> If --strategy specified, load strategy filters: mcp__plugin_ruflo-core_ruflo__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" }) neural-trader classifies anomalies automatically: spike (maxZ > 5): breakout — momentum entry or mean-reversion fade drift (sustained high Z): trend forming — trend-following signal flatline (low Z): consolidation — prepare for breakout oscillation (alternating): range-bound — mean-reversion at extremes pattern-break (multiple dims): regime change — close and reassess cluster-outlier (>50% dims): multi-factor dislocation — arbitrage Use SONA for regime prediction: mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" }) Search historical pattern matches: mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" }) Present ranked signals: instrument, direction, confidence, anomaly type, entry/stop/target Store signals with a 24-hour TTL (intraday signals shouldn't pollute long-running memory; the MemoryConsolidator.sweepExpired() pass introduced in ADR-125 Phase 4 — shipped in @claude-flow/memory@3.0.0-alpha.18 — sweeps them out after they expire): mcp__plugin_ruflo-core_ruflo__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })
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The downloaded .skill package contains the following fields.
Field Description
formatFormat tag (skill/v1)
skill_idUnique skill ID
nameSkill name
versionVersion
descriptionDescription
categoryCategories (array)
trigger_wordsTrigger words
tagsTags
sourceSource
source_urlSource URL (this page)
exported_atExported at (set per download)
system_promptSystem prompt body
model_configModel config: provider / model / temperature / max_tokens / top_p
examplesExamples
install_guideImport guide for Coze / Dify / Claude / custom frameworks
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Coze Markdown with frontmatter, for Coze platform import Download
Dify Dify DSL, import directly after creating an app Download

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