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 userInner/SKILLS --skill pymc-k-dense-ai-scientific-agent-skillsgit clone --depth 1 https://github.com/userInner/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/userinner/skills/pymc-k-dense-ai-scientific-agent-skills)<a href="https://agentmods.dev/skills/userinner/skills/pymc-k-dense-ai-scientific-agent-skills"><img src="https://agentmods.dev/badge/skills/userinner/skills/pymc-k-dense-ai-scientific-agent-skills/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/userinner/skills/pymc-k-dense-ai-scientific-agent-skills"><img src="https://agentmods.dev/badge/skills/userinner/skills/pymc-k-dense-ai-scientific-agent-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.02693 |
| Opus 5 | $0.00022 | $0.01347 |
| Sonnet 5 | $0.00009 | $0.00539 |
| Haiku 4.5 | $0.00004 | $0.00269 |
Grade A, and why
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 9d 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.
This is a copy
92% identical to pymc — 19 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 311 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 6.x+), including hierarchical models, MCMC sampling (NUTS), variational inference, posterior predictive checks, and model comparison (LOO, WAIC).
Current Version and Setup
PyMC 6.0.1 is the current stable release as of June 2026. It requires Python 3.12+, uses PyTensor 3 as the computational graph backend, and defaults to compiled backends such as Numba. For reproducible local environments, pin the version:
uv pip install "pymc[nutpie]==6.0.1"
The nutpie extra enables the faster Rust/Numba NUTS implementation. If using NumPyro or BlackJAX, install those optional sampler dependencies in the same environment and pin them in the project lockfile.
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
Never sample first and check later. The eight-step workflow — documented with code in references/standard_workflow.md — is:
- Data preparation — including standardizing predictors so priors are interpretable.
- Model building — priors and likelihood in a
pm.Modelcontext. - Prior predictive check — confirm the priors imply plausible data before fitting.
- Fit model —
pm.sample()with an explicit seed. - Check diagnostics — R-hat, ESS, divergences. Divergences invalidate the fit; fix
the model or reparameterize rather than raising
target_acceptand hoping. - Posterior predictive check — does the fitted model reproduce the observed data?
- Analyze results — summaries and intervals from the posterior.
- Make predictions — on new data via
pm.set_dataand posterior predictive sampling.
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 8.1 KB runs code
- effecta.manifest.json 779 B
- LICENSE 1.0 KB
- NOTICE.effecta 331 B
- references/distributions.md 12 KB
- references/model_patterns.md 3.5 KB
- references/sampling_inference.md 11 KB
- references/standard_workflow.md 4.7 KB
- references/workflows.md 14 KB
- scripts/model_comparison.py 13 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.
- 9d ago First seen · 311 lines · 43 tokens per session scan A 24d498c17b12
pymc is a skill published in the GitHub repository userInner/SKILLS (3 stars, last pushed 5d ago), licensed Apache-2.0. It adds 43 tokens to every session and 2,693 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to pymc, differing in 19 lines, and is treated as a copy.
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