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 choxos/BiostatAgent --skill stc-methodologygit clone --depth 1 https://github.com/choxos/BiostatAgentWrote 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/choxos/biostatagent/stc-methodology)<a href="https://agentmods.dev/skills/choxos/biostatagent/stc-methodology"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/stc-methodology/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/choxos/biostatagent/stc-methodology"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/stc-methodology.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 | $0.00040 | $0.02722 |
| Opus 5.5 | $0.00016 | $0.01089 |
| Sonnet 5.5 | $0.00008 | $0.00544 |
| Haiku 4.5 | $0.00004 | $0.00272 |
Grade A, and why
stc-methodology 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 1mo 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STC Methodology
Comprehensive methodological guidance for conducting rigorous Simulated Treatment Comparisons following NICE DSU TSD 18.
When to Use This Skill
- Deciding between STC and MAIC
- Selecting effect modifiers for an STC model
- Implementing covariate centering on an aggregate target population
- Reviewing STC code or results
- Planning Bayesian or frequentist sensitivity analyses
Fundamental Concept
Outcome Regression vs Propensity Weighting
STC approach
- Fit an outcome regression model in the IPD study.
- Include treatment and treatment-covariate interactions for relevant effect modifiers.
- Center covariates on the external aggregate population.
- Interpret the treatment coefficient as the adjusted effect in that external population.
MAIC approach
- Reweight IPD to match external aggregate covariate summaries.
- Estimate the weighted treatment effect in the target population.
- Use weight diagnostics and effective sample size as core feasibility checks.
Key Equation for a Binary Anchored STC
logit{P(Y = 1)} = beta_0 + beta_trt * Treatment
+ beta_X * X_centered
+ beta_trt_X * Treatment * X_centered
X_centered = X - X_external
With centered covariates, beta_trt estimates the treatment effect in the external population, because X_centered = 0 corresponds to the aggregate target values.
Assumptions
Conditional Constancy of Relative Effects
- Anchored STC assumes relative effects are constant across populations after adjustment for all relevant effect modifiers.
- The assumption is not testable with the available data alone.
- Effect modifiers must be measured in the IPD and reported as compatible aggregate summaries in the external study.
Model Specification
STC additionally assumes that the outcome model is correctly specified:
- Appropriate link function for the endpoint.
- Defensible functional forms for continuous covariates.
- Required treatment-covariate interactions included.
- No unsupported extrapolation beyond the IPD covariate support.
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.
- 1mo ago First seen · 338 lines · 40 tokens per session scan A ee20ea855c0d
stc-methodology is a skill published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 2,722 once invoked, about $0.0002 per session on Opus 5.5. 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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