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pymc

Builds and checks Bayesian models with PyMC: hierarchical models, NUTS MCMC, variational inference, posterior predictive checks, diagnostics and model comparison.

View on GitHub
Kind
Skill
Works with
Claude Code, Codex, Cursor, Gemini / Antigravity
Source
Scientific Agent Skills (K-Dense) @ 5f2bc0341354
Path
skills/pymc/SKILL.md
Jobs
Implementation
Topics
Machine learning, LLM apps & RAG, Fact-checking

Credit

Made by
K-Dense-AI
Original
github.com/K-Dense-AI/scientific-agent-skills/blob/5f2bc03413…
License
MIT

Listed by Skill Manifest; not made by us.

InstallInstall it by hand, following the source.
gh skill install K-Dense-AI/scientific-agent-skills pymc
Install factsScanned 2 Nov 2026
Adds
  • .claude/skills/pymc/ (the skill folder)
  • 5 file(s) the skill refers to
Runs scripts
Yes
  • assets/linear_regression_template.py
  • assets/hierarchical_model_template.py
  • allowed-tools includes Bash
  • shell block in the text
Uses the network
Yes
  • downloads Python packages
Needs secrets
None found
Source revision
5f2bc0341354
Licence
MIT
Last activity
26 Oct 2026
Review
Reviewed

Found by a static scan of the stored text; nothing is run. “None found” is not a guarantee, so read what you install before you run it.

  • Only the main file is read; other files in the folder are not scanned
# PyMC Bayesian Modeling

## Version and scope

Targets **PyMC 6.3.2**, **PyTensor 3.3.2**, and **ArviZ 1.3.0**. These are
local Python APIs, with no service endpoint, credentials, or remote inference
required. The native tests use the PyMC NUTS sampler on small synthetic models.
Optional nutpie/JAX samplers, SMC, and production-scale convergence are not
validated by those tests. Record the complete resolved environment, backend,
seeds, preprocessing, and data provenance for a scientific run.

Local macOS validation used `PYTENSOR_FLAGS="cxx="` after the C linker failed
with `library 'd64' not found`; this disables PyTensor C compilation for that
process. The Python library workflows passed under that setting; it does not
establish that the C backend works on this host.

ArviZ 1 splits into base/stats/plots packages, re-exported by `import arviz as az`.
PyMC returns an xarray `DataTree` despite the legacy `return_inferencedata` option
name. `az.summary(..., round_to="none")` returns numerical columns; the default
formats for display. Use `az.plot_ppc_dist`, `az.plot_dist`, and
`az.plot_trace_dist`; save their returned `PlotCollection`, not a separate pyplot
…