Oraclaw Decision Intelligence

Select the next option to try from 2+ variants that each have observed pull/reward history, balancing exploitation against exploration (UCB1, Thompson sampling, or epsilon-greedy). Use when you must pick one arm now from A/B test variants, ad/email/copy options, or ranked recommendations and have past trial counts. Returns the chosen arm plus exploitation score, exploration bonus, and a regret estimate. For per-call context features use optimize_contextual; for continuous parameters use optimize_cmaes.

Select the best option given a numeric context/feature vector, using a LinUCB contextual bandit that learns per-context preferences from optional history. Use when the best choice changes with situational features that vary call-to-call (user/segment attributes, time of day, current regime). Returns the chosen arm with its LinUCB expected reward and confidence width. If you have no per-call features, use optimize_bandit.

[Premium] Optimize N continuous parameters against a weighted-sum objective using CMA-ES, suited to non-convex/noisy/gradient-free landscapes. Use for hyperparameter search, simulator calibration, or control-policy tuning where you supply per-dimension objective weights. Returns the best parameter vector, its objective value, iteration/evaluation counts, and a converged flag; stochastic init means repeated runs may differ. Use optimize_evolve for discrete spaces and solve_constraints for linear/MIP constraints. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

[Premium] Solve a linear / mixed-integer / quadratic program with the HiGHS solver and return a provably optimal assignment. Use when your objective and constraints are linear (or quadratic) over named continuous/integer/binary variables: budget allocation, supply or capacity planning with integer counts, allocation with hard caps. Returns solver status (optimal/infeasible/unbounded), the objective value, and the solved value per variable. Use optimize_cmaes for black-box objectives and solve_schedule for task-to-slot assignment. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

Assign tasks to time slots to maximize total score by matching each task's energy requirement to a slot's energy level (and respecting duration). Use for deep-work blocking, shift or session planning, or any task-to-slot fit where high-energy work should land in high-energy slots. Returns the assignments, any unassigned task IDs, and a total score. For arbitrary linear constraints use solve_constraints; for routing use plan_pathfind.

[Premium] Compute structural metrics of a directed weighted graph: PageRank centrality, Louvain community clusters, an optional critical path between two given nodes, and bottleneck nodes. Use to find the most influential nodes, cluster a dependency/knowledge graph, or locate chokepoints in supply or process networks. Returns per-node PageRank and community index, cluster summaries, the critical path with its weight, and bottlenecks. For a single source-to-goal route, use plan_pathfind (free). Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

[Premium] Compute portfolio Value-at-Risk and Conditional VaR (Expected Shortfall) from a historical [asset][time] return matrix and portfolio weights, accounting for cross-asset correlation. Use to size downside risk on a weighted multi-asset book, attribute risk, or run drawdown scenarios with auditable inputs. Returns VaR and CVaR (loss as a positive number) at the requested confidence, plus expected return, volatility, and the horizon used. To sample outcomes from a parametric distribution instead, use simulate_montecarlo. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

Score how strongly multiple independent sources agree on a single event's probability, using Hellinger-distance agreement plus penalties for dispersion/uncertainty and a freshness weight (recency, source volume, and confidence). Use to fuse 0..1 estimates from polls, prediction markets, or model outputs into one number. Returns a 0..1 convergence score, the volume-weighted consensus probability, source count, and component breakdown. To combine N point predictions instead, use predict_ensemble.

[Premium] Forecast the next N values of one evenly-spaced numeric time series using ARIMA (non-seasonal trend) or Holt-Winters (additive seasonal, set seasonLength). Use for short-to-medium horizon point forecasts of demand, KPIs, or capacity. Returns the point forecast array plus lower/upper confidence bands and the fitted model description. ARIMA requires at least 20 observations; Holt-Winters needs at least 2 x seasonLength. To flag outliers instead of projecting, use detect_anomaly. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

[Premium] Flag outlier points in a numeric series using a Z-score test (parametric, assumes near-normal) or IQR test (robust to skew/heavy tails). Use for metric monitoring, fraud/abuse signals, sensor noise, or quality control. Returns each anomaly's index, value, and score, plus the underlying statistics (mean/stdDev/threshold for Z-score; q1/q3/IQR/bounds for IQR) and an anomaly count. To project a series forward instead, use predict_forecast. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

Find the shortest path (or k-shortest paths) between a start and end node in a weighted directed graph using A* with selectable heuristic (zero=Dijkstra, time, cost, risk, weighted) and Yen's algorithm for alternatives. Use for routing, dependency resolution, or 'how do I get from X to Y' over a graph; set kPaths>1 for alternatives. Returns the path node IDs, total cost, a time/cost/risk breakdown, nodes explored, and a found flag. For centrality/communities use analyze_graph; for task-to-slot assignment use solve_schedule.

Draw N samples from one parametric distribution (normal, lognormal, uniform, triangular, beta, or exponential) and summarize the resulting spread. Use to quantify uncertainty around a single random factor: an NPV under an uncertain growth rate, a latency tail, or a reserve estimate. Returns the mean, standard deviation, p5/p25/p50/p75/p95 percentiles, a histogram, and the iteration count; each call re-samples (non-deterministic) and is capped at 2000 iterations. For correlated multi-asset risk, use analyze_risk.

Measure how well a set of probability predictions matched observed binary outcomes, returning the Brier score and log score (lower is better). Use to evaluate a forecaster's or model's calibration: predictions[i] is the probability assigned to event i and outcomes[i] is 1 if it occurred, else 0 (arrays must be equal length). Returns brier_score, log_score, the number of predictions, and the mean predicted vs mean observed rate. To measure agreement across multiple sources instead, use score_convergence.

Update a prior probability with weighted evidence signals using a Beta posterior (the prior seeds Beta(prior*10, (1-prior)*10)). Use for incremental belief revision: start from a baseline probability and fold in signals, each a value in [0,1] with a weight, to get a revised posterior. Returns the updated posterior, the prior, per-factor contributions, posterior mean and variance, and a sharpness/calibration score. To combine N independent point predictions use predict_ensemble; to sample a full distribution use simulate_montecarlo.

Combine 2+ model point predictions into one consensus using weighted voting, stacking, or Bayesian model averaging, weighting each model by its confidence or supplied historicalAccuracy. Use to fuse heterogeneous predictors (statistical, ML, and human forecasters) into a single number with an uncertainty estimate. Returns the consensus value and confidence, per-model weight share, Shannon entropy of the weights, a cross-model agreement score, epistemic/aleatoric/total uncertainty with a confidence interval, and per-model contributions. To score agreement on a single event probability instead, use score_convergence.

Run a genetic algorithm over a fixed-length gene vector (binary, integer, real, or permutation bounds) against a weighted-sum fitness, with an optional Pareto frontier for multi-objective runs. Use for discrete or mixed search spaces (feature selection, integer allocation, permutation/TSP-style problems) or when you want several non-dominated solutions. Returns the best chromosome and fitness, the Pareto frontier when applicable, the convergence generation, total generations, and recent fitness history; results vary run to run (stochastic). For smooth continuous objectives, use optimize_cmaes.

Compare named what-if scenarios against a base case where the outcome metric is the sum of the input variables, and rank which variables swing the outcome most. Use for budget sensitivity, deal/forecast what-ifs, or capacity planning across demand assumptions: define a base case of variable=value, then scenarios that override a subset. Returns the base outcome, each scenario's outcome with absolute and percent delta and per-variable changes, plus a sensitivity ranking by total absolute swing. For random sampling from a distribution, use simulate_montecarlo.