Oraclaw Simulate
Skill Verified ActiveMonte Carlo simulation for AI agents. Run thousands of probabilistic scenarios to model risk, forecast revenue, estimate project timelines, and quantify uncertainty. Supports 6 distribution types.
To provide AI agents with mathematically sound Monte Carlo simulation capabilities for quantitative analysis, risk modeling, and forecasting.
Features
- Run thousands of probabilistic scenarios
- Model risk and forecast revenue
- Estimate project timelines
- Quantify uncertainty
- Support for 6 distribution types
Use Cases
- Estimate the probability of hitting a revenue target
- Model project timelines with uncertainty
- Calculate Value at Risk for a portfolio
- Run sensitivity analysis on business assumptions
Non-Goals
- Providing real-time trading execution
- Performing deterministic financial calculations without probabilistic inputs
- Replacing core LLM reasoning capabilities
Installation
npx skills add Whatsonyourmind/oraclawRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
Similar Extensions
Forecast Scenarios
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Financial Analyst
100Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.
Simulate Stochastic Process
97Simulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.
Model Markov Chain
97Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.
Trader Regime
100Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy
Trading Memory
100Domain knowledge for AI trading memory — Outcome-Weighted Memory (OWM) architecture, 5 memory types, recall scoring, and behavioral analysis. Use when recording trades, recalling similar contexts, analyzing performance, or checking behavioral drift. Triggers on "record trade", "remember trade", "recall", "similar trades", "performance", "behavioral", "disposition", "affective state", "confidence".