meta-analysis

meta-analysis is a skill for Claude Code, Codex from choxos/BiostatAgent. It costs 26 tokens per session (1,442 once invoked), scanned A, original, MIT.

A collection of Bayesian meta-analysis models, which combine results from multiple studies while accounting for their uncertainty and possible differences. It includes fixed-effect, random-effects, and network meta-analysis patterns in Stan and JAGS.

In plain words
What is it for?
Use it to estimate a common treatment effect, model differences between studies, make predictions for new studies, and build network comparisons.
Why use it?
It helps you combine study findings in a model that matches how similar or different the studies are. The examples make the assumptions and variation between studies explicit.

Skill for Claude CodeCodex

Part of the bayesian-modeling plugin — 9 skills, 3 commands, 6 agents shipped together

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/choxos/biostatagent/meta-analysis
Any agent
npx skills add choxos/BiostatAgent --skill meta-analysis
Clone the repo
git clone --depth 1 https://github.com/choxos/BiostatAgent

Made for: Claude Code, Codex.

Or install bayesian-modeling, the plugin that ships this one along with the rest of its 9 skills, 3 commands, 6 agents.

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 meta-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/choxos/biostatagent/meta-analysis.svg)](https://agentmods.dev/skills/choxos/biostatagent/meta-analysis)
Your own site
<a href="https://agentmods.dev/skills/choxos/biostatagent/meta-analysis"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/meta-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,442 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 $0.00026 $0.01442
Opus 5 $0.00013 $0.00721
Sonnet 5 $0.00005 $0.00288
Haiku 4.5 $0.00003 $0.00144

Measured 5d ago against content hash dea06440f333, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

meta-analysis 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 5d 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.

plugins/bayesian-modeling/skills/meta-analysis/SKILL.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Meta-Analysis Models

Fixed Effects Meta-Analysis

Stan

data {
  int<lower=0> K;           // Number of studies
  vector[K] y;              // Effect estimates
  vector<lower=0>[K] se;    // Standard errors
}
parameters {
  real theta;               // Common effect
}
model {
  theta ~ normal(0, 10);
  y ~ normal(theta, se);
}

JAGS

model {
  for (i in 1:K) {
    y[i] ~ dnorm(theta, prec[i])
    prec[i] <- pow(se[i], -2)
  }
  theta ~ dnorm(0, 0.0001)
}

Random Effects Meta-Analysis

Stan (Non-centered, recommended)

data {
  int<lower=0> K;
  vector[K] y;
  vector<lower=0>[K] se;
}
parameters {
  real mu;                  // Overall mean
  real<lower=0> tau;        // Between-study SD
  vector[K] eta;            // Study effects (standardized)
}
transformed parameters {
  vector[K] theta = mu + tau * eta;
}
model {
  // Priors
  mu ~ normal(0, 10);
  tau ~ cauchy(0, 0.5);     // Half-Cauchy
  eta ~ std_normal();

  // Likelihood
  y ~ normal(theta, se);
}
generated quantities {
  real theta_new = normal_rng(mu, tau);  // Predictive
  real I2 = square(tau) / (square(tau) + mean(square(se)));
}

JAGS

model {
  for (i in 1:K) {
    y[i] ~ dnorm(theta[i], prec[i])
    prec[i] <- pow(se[i], -2)
    theta[i] ~ dnorm(mu, tau.theta)
  }

  mu ~ dnorm(0, 0.0001)
  tau.theta <- pow(sigma.theta, -2)
  sigma.theta ~ dunif(0, 10)

  # Heterogeneity
  tau2 <- pow(sigma.theta, 2)
}

Binary Outcomes

Stan (Log-Odds)

data {
  int<lower=0> K;
  array[K] int<lower=0> r1;   // Events in treatment
  array[K] int<lower=0> n1;   // Total in treatment
  array[K] int<lower=0> r2;   // Events in control
  array[K] int<lower=0> n2;   // Total in control
}
parameters {
  real d;                     // Overall log-OR
  real<lower=0> tau;
  vector[K] delta;            // Study-specific log-OR
  vector[K] mu;               // Baseline log-odds
}
model {
  d ~ normal(0, 10);
  tau ~ cauchy(0, 0.5);
  delta ~ normal(d, tau);
  mu ~ normal(0, 10);

  r2 ~ binomial_logit(n2, mu);
  r1 ~ binomial_logit(n1, mu + delta);
}
generated quantities {
  real OR = exp(d);
}

Read the full file on GitHub · 212 lines

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. 5d ago First seen · 212 lines · 26 tokens per session scan A dea06440f333

Subscribe to this mod's changes

meta-analysis is a skill published in the GitHub repository choxos/BiostatAgent (11 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 1,442 once invoked, about $0.0001 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.

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