Provides a structure for developing Bayesian models and diagnostic workflows in Python.
Install
mkdir -p .claude/skills/pymc && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/18166" && unzip -o skill.zip -d .claude/skills/pymc && rm skill.zipInstalls to .claude/skills/pymc
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Build Bayesian models and probabilistic analysis workflows.Key capabilities
- →Build Bayesian models for probabilistic analysis
- →Define priors for model parameters
- →Specify likelihood functions for observed data
- →Perform inference using `pm.sample`
- →Generate trace plots for visual diagnostics
- →Summarize model parameters and diagnostics
How it works
The skill uses PyMC to define a Bayesian model with priors and likelihood, then performs inference to generate a trace and provides diagnostic checks.
Inputs & outputs
When to use pymc
- →Building probabilistic models
- →Quantifying uncertainty in data
- →Running Bayesian statistical inference
About this skill
pymc
Build Bayesian models and probabilistic analysis workflows.
When to use
When uncertainty quantification, prior knowledge incorporation, or small-data inference is needed.
Basic model structure
import pymc as pm
import numpy as np
with pm.Model() as model:
# Priors
mu = pm.Normal("mu", mu=0, sigma=10)
sigma = pm.HalfNormal("sigma", sigma=1)
# Likelihood
obs = pm.Normal("obs", mu=mu, sigma=sigma, observed=data)
# Inference
trace = pm.sample(2000, return_inferencedata=True)
# Diagnostics
pm.plot_trace(trace)
pm.summary(trace)
Diagnostics checklist
- R-hat < 1.01 for all parameters
- Effective sample size (ESS) > 400
- No divergences
- Trace plots show good mixing
Source
Scientific Agent Skills (K-Dense-AI/scientific-agent-skills)
When not to use it
- →When uncertainty quantification is not needed
- →When prior knowledge incorporation is not needed
- →When small-data inference is not needed
Limitations
- →Requires `pymc` and `numpy` libraries
- →Focuses on basic model structure and diagnostics
- →Diagnostics checklist includes R-hat, Effective sample size, and trace plots
How it compares
This skill provides a structured approach to building and analyzing Bayesian models with built-in diagnostics, offering a systematic way to quantify uncertainty compared to traditional statistical methods.
Compared to similar skills
pymc side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| pymc (this skill) | 0 | 27d | No flags | Intermediate |
| quant-analyst | 103 | 2mo | No flags | Advanced |
| umap-learn | 6 | 1mo | Review | Intermediate |
| embedding-strategies | 8 | 2mo | No flags | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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