Ready to supercharge your AI agents?

Install any skill in seconds. Free to start, no credit card required.

Get Started Free →

For direct effect · brycewang-stanford | DecimalAI

scienceby brycewang-stanford

For direct effect

brycewang-stanford/r-bayes

Source brycewang-stanford/Auto-Empirical-Research-Skills ↗License NOASSERTION

Patterns for Bayesian inference in R using brms, including multilevel models, DAG validation, and marginal effects.

SkillScore77Maintained

Automatic

Data Analyst pack

Last verified 8/8/2026

What it changes

Passed 17 of 22 test cases with the skill, and 14 of 22 without it.

Without skill64%

With skill77%

Checks it passed that the model alone missed

Extracts Rhat from the model fixed summary.
Calculates probability by computing mean(diff > 0).
Extracts Bulk_ESS and Tail_ESS from the model fixed summary.

and 1 more in the full report

3 fixed14 passed either way5 still failing0 broke

Pass-rate lift: +14 pp
Tokens: +140%
Agent turns: 0%
Response time: -16%

Model gemini-3.6-flashMeasured 8/8/2026v117 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-r-bayes/SKILL.md https://app.decimal.ai/s/brycewang-stanford-r-bayes/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add brycewang-stanford-r-bayes, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/brycewang-stanford-r-bayes/ and pins it in .decimal/skills.lock

decimalai skills add brycewang-stanford-r-bayes

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-r-bayes/SKILL.md https://app.decimal.ai/s/brycewang-stanford-r-bayes/SKILL.md

Raw file: /s/brycewang-stanford-r-bayes/SKILL.md ↗ · machine-readable summary at /s/brycewang-stanford-r-bayes.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 3

Prompt

I have already fitted a Bayesian logistic model using brms and now need to summarize and interpret the results. Could you provide R code that processes the posterior draws to calculate medians, 95% uncertainty intervals, and odds ratios, while also using marginaleffects to calculate average marginal slopes on the probability scale and predicted probabilities across standardized predictor levels (-1, 0, +1 SD) without random effect conditioning?

What was checked, without and with the skill
Checked	Without skill	With skill
Uses predictions with newdata = datagrid(model = model, predictor_z = c(-1, 0, 1)).	Missed	Passed
Specifies re_formula = NA in marginal prediction functions to calculate population-level predictions without random effect conditioning.	Passed	Passed
Specifies type = "response" in avg_slopes to obtain slopes on the probability scale.	Passed	Passed

case-03 · gemini-3.6-flash · 8/8/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md428 linessingle file

Core Packages

library(brms)
library(cmdstanr)
library(dagitty)
library(ggdag)
library(marginaleffects)
library(tidybayes)
library(bayesplot)

Directed Acyclic Graphs (DAGs)

Prior to causal inference, create and validate DAGs with dagitty and ggdag.

Define DAG Structure

dag <- dagitty('
dag {
  # Node positions for visualization
  exposure [pos="0,1"]
  mediator [pos="1,1"]
  outcome [pos="2,1"]
  confounder [pos="1,0"]

  # Edges (arrows)
  confounder -> exposure
  confounder -> outcome
  exposure -> mediator
  mediator -> outcome
  exposure -> outcome
}
')

Identify Adjustment Sets

Track record

Benchmark

17 / 22

test cases passed, vs 14 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

brycewang-stanford / Auto-Empirical-Research-Skills

skills/55-ab604-claude-code-r-skills/skills/r-bayes/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/2/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.