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OpenClaw Skills & Use Case Index

ClawIndex is an ecosystem-driven index of OpenClaw skills and real-world use cases.

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Skills with output: Prediction intervals

Browse skills that produce this output.

  • timesfm-forecasting - Zero-Shot Time Series Forecasting with TimesFM
    time-series forecastingzero-shot forecastingfoundation modelprediction intervals

    ★ 0 · Updated 2026-10-04

    Forecast univariate time series without custom training using TimesFM, with point predictions, calibrated quantile intervals, and optional exogenous covariates.

    ⚙ Run system preflight checks⚙ Validate dataset memory fit⚙ Forecast univariate time series
  • pymc-bayesian-modeling - Bayesian modeling and inference with PyMC
    Bayesian modelingProbabilistic programmingMCMCVariational inference

    ★ 2 · Updated 2026-10-02

    Build, fit, validate, compare, and use Bayesian models with PyMC, including hierarchical models, MCMC, variational inference, and predictive checks.

    ⚙ Build Bayesian models⚙ Run MCMC sampling⚙ Apply variational inference
  • pymc - Build and validate Bayesian models with PyMC
    Bayesian modelingProbabilistic programmingMCMCNUTS

    ★ 2 · Updated 2026-09-30

    Build, fit, validate, compare, and diagnose Bayesian models with PyMC using MCMC, variational inference, predictive checks, LOO, and WAIC.

    ⚙ Build Bayesian models⚙ Fit models with MCMC⚙ Run variational inference
  • pymc-bayesian-modeling - Build and validate Bayesian models with PyMC
    Bayesian modelingProbabilistic programmingPyMCMCMC

    ★ 0 · Updated 2026-09-30

    Build, fit, validate, diagnose, and compare Bayesian models with PyMC using MCMC, variational inference, and predictive checks.

    ⚙ Build Bayesian models⚙ Run NUTS sampling⚙ Perform variational inference
  • pymc-bayesian-modeling - Build and validate Bayesian models with PyMC
    Bayesian modelingPyMCProbabilistic programmingMCMC

    ★ 650 · Updated 2026-09-30

    Builds, fits, validates, diagnoses, and compares Bayesian models with PyMC, including hierarchical models, MCMC, variational inference, predictive checks, and LOO/WAIC.

    ⚙ Build Bayesian models⚙ Fit models with MCMC⚙ Run variational inference