Chaospy uncertainty propagation starter
A framework for discovering, compiling, and validating reusable skills for scientific agents.
npx -y skills add ma-compbio-lab/SkillFoundry --skill chaospy-uncertainty-propagation-starterAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Chaospy Uncertainty Propagation Starter
Use this skill to run a deterministic uncertainty-propagation toy workflow with Chaospy and inspect a machine-readable summary of the propagated response distribution.
What This Skill Does
- defines a small two-parameter input distribution with one Gaussian and one uniform variable
- builds a polynomial-chaos surrogate with quadrature
- reports the propagated mean, standard deviation, percentiles, and representative quadrature evaluations
When To Use It
- when you need a runnable
uncertainty-aware-simulationstarter - when you want a local Chaospy example before wiring in an expensive scientific simulator
- when you need deterministic uncertainty summaries for repository tests
Run
./slurm/envs/numerics/bin/python skills/scientific-computing-and-numerical-methods/chaospy-uncertainty-propagation-starter/scripts/run_chaospy_uncertainty_propagation.py \
--config skills/scientific-computing-and-numerical-methods/chaospy-uncertainty-propagation-starter/examples/toy_parameters.json \
--out scratch/numerics/chaospy_uncertainty_summary.json
Notes
- The response model is intentionally synthetic; it exists to verify the UQ loop, not to stand in for a real simulator.
- The script uses deterministic quadrature rather than random sampling so the summary is stable across reruns.