agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
$ npx -y skills add ruvnet/ruflo --skill trader-risk --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/trader-riskContext preview
The summary Claude sees to decide when to auto-load this skill.
Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status
name: trader-risk description: Assess portfolio risk using npx neural-trader — VaR, CVaR, Sharpe, position sizing, circuit breaker status allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search argument-hint: "[--symbol TICKER] [--portfolio NAME]"
Assess portfolio and position risk using neural-trader's risk engine.
Steps: 1. Ensure neural-trader is available: `npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader` 2. Run risk assessment:
# Single position npx neural-trader --risk assess --symbol TICKER npx neural-trader --var --symbol TICKER --investment 10000 # Portfolio-wide npx neural-trader --risk assess --portfolio NAME npx neural-trader --correlation --portfolio NAME --flag-threshold 0.8
3. Calculate position sizing:
npx neural-trader --risk-tolerance 0.02 --symbol TICKER npx neural-trader --position-sizing kelly --symbol TICKER
4. Check circuit breaker status:
5. Present: risk metrics, position sizing recommendation, active breakers, alerts 6. Store assessment: `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "risk-TICKER-DATE", value: "RISK_METRICS", namespace: "trading-risk" })`
An agent meta-harness for Claude Code and Codex. 📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification.
Repo: ruvnet/ruflo
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems…
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and…
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use…
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing…
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG…
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination