/trader-regime
Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be
$ npx -y skills add ruvnet/ruflo --skill trader-regime --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/trader-regime
Context preview
The summary Claude sees to decide when to auto-load this skill.
Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be
SKILL.md
trader-regime.SKILL.mdname: trader-regime
description: Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be regime-aware.
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_predict
argument-hint: "[--symbol SPY] [--symbols AAPL,MSFT]"
Detect the current market regime using neural-trader's regime detection engine.
Steps: 1. Ensure neural-trader is available: `npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader` 2. Run regime detection:
npx neural-trader --regime-detect --symbol TICKER
For multiple symbols:
npx neural-trader --regime-detect --symbols "AAPL,MSFT,GOOGL,AMZN"
3. Get technical indicators for context:
npx neural-trader --symbol TICKER --indicators rsi,macd,bollinger,adx,atr
4. Use SONA for regime prediction: `mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "indicators: RSI=X, ADX=Y, VIX=Z" })` 5. Search for similar historical regimes: `mcp__plugin_ruflo-core_ruflo__memory_search({ query: "regime similar to CURRENT", namespace: "trading-analysis" })` 6. Present: regime classification, confidence, recommended strategy type, historical precedents 7. Store analysis: `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "regime-DATE", value: "REGIME_ANALYSIS", namespace: "trading-analysis" })`
Read more
name: trader-regime description: Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy. Use when the user asks about market conditions, wants to pick a strategy for current conditions, or before running a backtest/signal that should be regime-aware. allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_predict argument-hint: "[--symbol SPY] [--symbols AAPL,MSFT]"
Detect the current market regime using neural-trader's regime detection engine.
Steps: 1. Ensure neural-trader is available: `npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader` 2. Run regime detection:
npx neural-trader --regime-detect --symbol TICKER
For multiple symbols:
npx neural-trader --regime-detect --symbols "AAPL,MSFT,GOOGL,AMZN"
3. Get technical indicators for context:
npx neural-trader --symbol TICKER --indicators rsi,macd,bollinger,adx,atr
4. Use SONA for regime prediction: `mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "indicators: RSI=X, ADX=Y, VIX=Z" })` 5. Search for similar historical regimes: `mcp__plugin_ruflo-core_ruflo__memory_search({ query: "regime similar to CURRENT", namespace: "trading-analysis" })` 6. Present: regime classification, confidence, recommended strategy type, historical precedents 7. Store analysis: `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "regime-DATE", value: "REGIME_ANALYSIS", namespace: "trading-analysis" })`
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
Other skills on claude-flow.
- /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill - /agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Open skill - /agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Open skill

