Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-pymcgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pymc)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pymc"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pymc/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pymc"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-pymc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.02001 |
| Opus 5 | $0.00043 | $0.01001 |
| Sonnet 5 | $0.00017 | $0.00400 |
| Haiku 4.5 | $0.00009 | $0.00200 |
Grade A, and why
alterlab-pymc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyMC Bayesian Modeling
Overview
PyMC is a Python library for Bayesian modeling and probabilistic programming. Build, fit, validate, and compare Bayesian models using PyMC's modern API (version 5.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, and model comparison (LOO, WAIC).
When to Use This Skill
This skill should be used when:
- Building Bayesian models (linear/logistic regression, hierarchical models, time series, etc.)
- Performing MCMC sampling or variational inference
- Conducting prior/posterior predictive checks
- Diagnosing sampling issues (divergences, convergence, ESS)
- Comparing multiple models using information criteria (LOO, WAIC)
- Implementing uncertainty quantification through Bayesian methods
- Working with hierarchical/multilevel data structures
- Handling missing data or measurement error in a principled way
Standard Bayesian Workflow
Follow this 8-step workflow for building and validating Bayesian models:
- Data preparation — standardize predictors, handle missing data, set up
coords - Model building — weakly informative priors, named
dims,pm.Data()for predictables - Prior predictive check —
pm.sample_prior_predictive; validate priors before fitting - Fit —
pm.sample(draws=2000, tune=1000, chains=4, target_accept=0.9); includelog_likelihood=Truefor comparison - Diagnostics — R-hat < 1.01, ESS > 400, no divergences, good trace mixing
- Posterior predictive check —
pm.sample_posterior_predictive; check fit vs. observed data - Analyze —
az.summary,az.plot_posterior,az.plot_forest - Predict —
pm.set_datathenpm.sample_posterior_predictive; extract HDI intervals
Full step-by-step code: references/workflow_examples.md.
Common Model Patterns
PyMC supports linear/logistic/Poisson regression, hierarchical (multilevel) models, and
time-series (AR). Ready-to-adapt code for each lives in references/model_patterns.md.
Critical: Always use non-centered parameterization for hierarchical models to avoid divergences.
Templates: assets/linear_regression_template.py, assets/hierarchical_model_template.py.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/hierarchical_model_template.py 12 KB runs code
- assets/linear_regression_template.py 8.4 KB runs code
- evals/evals.json 5.4 KB
- references/distribution_selection.md 1.5 KB
- references/distributions.md 11 KB
- references/model_comparison.md 3.3 KB
- references/model_patterns.md 2.3 KB
- references/sampling_inference.md 10 KB
- references/workflow_examples.md 4.4 KB
- references/workflows.md 14 KB
- scripts/model_comparison.py 12 KB runs code
- scripts/model_diagnostics.py 11 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 172 lines · 86 tokens per session scan A 6edfbb9dff97
alterlab-pymc is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 2,001 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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