agentmods.dev

bayesian-workflow

bayesian-workflow is a skill for Claude Code, Codex from Learning-Bayesian-Statistics/baygent-skills. It costs 195 tokens per session (4,661 once invoked), scanned A, original, MIT.

A step-by-step guide for Bayesian analysis, a way to update uncertain beliefs using data. It covers building models in PyMC, checking whether assumptions and results are reasonable, and testing whether conclusions depend on prior choices.

In plain words
What is it for?
Use it to formulate Bayesian models, choose priors, run inference, diagnose sampling, criticize models, and check prior sensitivity.
Why use it?
It prevents analysis from stopping after model fitting without checking convergence, model quality, or sensitivity to assumptions.

Skill for Claude CodeCodex

not rated 172 +1 changed today A scan 195 tokens original MIT
View the source file Learning-Bayesian-Statistics/baygent-skills
Install

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.

agentmods
npx agentmods add skills/learning-bayesian-statistics/baygent-skills/bayesian-workflow
Any agent
npx skills add Learning-Bayesian-Statistics/baygent-skills --skill bayesian-workflow
Clone the repo
git clone --depth 1 https://github.com/Learning-Bayesian-Statistics/baygent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bayesian-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/learning-bayesian-statistics/baygent-skills/bayesian-workflow.svg)](https://agentmods.dev/skills/learning-bayesian-statistics/baygent-skills/bayesian-workflow)
Your own site
<a href="https://agentmods.dev/skills/learning-bayesian-statistics/baygent-skills/bayesian-workflow"><img src="https://agentmods.dev/badge/skills/learning-bayesian-statistics/baygent-skills/bayesian-workflow.svg" alt="Measured on agentmods" height="20"></a>
Per session 195 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,661 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00195 $0.04661
Opus 5 $0.00097 $0.02330
Sonnet 5 $0.00039 $0.00932
Haiku 4.5 $0.00019 $0.00466

Measured today against content hash f33a8cef9154, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

bayesian-workflow 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 today.

The scan reads SKILL.md. This mod also ships 4 executable files (main.py, scripts/calibration_check.py, scripts/check_diagnostics.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

bayesian-workflow/SKILL.md · 205 lines

How it starts

The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Bayesian Workflow

Workflow overview

Every Bayesian analysis follows this sequence. Do not skip steps -- especially model criticism.

  1. Formulate — Define the generative story. What underlying process, that we're precisely trying to model, created the data?
  2. Specify priors — See references/priors.md
  3. Implement in PyMC — Write the model. Prefer PyMC 5+ syntax. Use the latest version possible.
  4. Run prior predictive checks — pm.sample_prior_predictive(). Verify priors produce plausible data ranges before fitting
  5. Inference — pm.sample(nuts_sampler="nutpie"). Always use nutpie for speed (the nutpie python package provides cutting-edge sampling). Don't hardcode the number of chains — let the sampler pick the best default for the platform.
  6. Diagnose convergence — Use arviz_stats.diagnose(idata) as the first check (requires arviz-stats >= 1.0.0). It covers R-hat, ESS, divergences, tree depth, and E-BFMI in one call. See references/diagnostics.md
  7. Criticize the model — See references/model-criticism.md
  8. Check prior sensitivity — Run psense_summary(idata) to verify conclusions are robust to prior choices. Visualize with plot_psense_dist(idata) from arviz_plots. Requires log_likelihood and log_prior in the InferenceData — compute them after sampling if needed. See references/sensitivity.md
  9. Compare models (if applicable) — See references/model-comparison.md
  10. Report results — Generate <slug>/report.md using the canonical template in references/reporting.md. Run scripts/check_diagnostics.py to turn raw diagnostics into qualitative ratings + an ordered next-steps list, and use that output to fill the Assessment lines and Suggested Next Steps section. When the user mentions a non-technical audience or is new to Bayesian stats, additionally adapt the prose to plain language and include a glossary — but keep the canonical report structure as the audit trail.

Read the full file on GitHub · 205 lines

Files

What ships with it

13 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.

Changes

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.

  1. today Changed · +195 tokens per session f33a8cef9154
  2. 5d ago First seen · 205 lines · 0 tokens per session scan A 0f2b7d067da9

Subscribe to this mod's changes

bayesian-workflow is a skill published in the GitHub repository Learning-Bayesian-Statistics/baygent-skills (172 stars, last pushed yesterday), licensed MIT. It adds 195 tokens to every session and 4,661 once invoked, about $0.0010 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 2026-08-30.

Analytics and metricsScientific
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