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 wentorai/research-plugins --skill robustness-checksgit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/robustness-checks)<a href="https://agentmods.dev/skills/wentorai/research-plugins/robustness-checks"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/robustness-checks/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/wentorai/research-plugins/robustness-checks"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/robustness-checks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00017 | $0.01776 |
| Opus 5.5 | $0.00007 | $0.00710 |
| Sonnet 5.5 | $0.00003 | $0.00355 |
| Haiku 4.5 | $0.00002 | $0.00178 |
Grade A, and why
robustness-checks 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Robustness Checks
A skill for conducting sequential robustness checks in Stata, systematically adding blocks of potential confounders to assess estimate stability.
Quick Start
* Base model
svy: regress outcome controls treatment
estimates store m1
* Add confounder block
svy: regress outcome controls treatment confounder1 confounder2
estimates store m2
* Compare
esttab m1 m2, se star(+ 0.1 * 0.05 ** 0.01)
Key Patterns
1. Sequential Model Building
* Define base controls
local control_var i.batch age i.race i.gender i.education
estimates clear
* Model 1: Base model
svy: regress outcome `control_var' treatment
margins, dydx(treatment) post
estimates store m1
* Model 2: Add contextual factors
svy: regress outcome `control_var' treatment covid health_insurance
margins, dydx(treatment) post
estimates store m2
* Model 3: Add health factors
svy: regress outcome `control_var' treatment cci_charlson any_encounter
margins, dydx(treatment) post
estimates store m3
* Model 4: Add psychological factors
svy: regress outcome `control_var' treatment depression anxiety
margins, dydx(treatment) post
estimates store m4
* Model 5: Add behavioral factors
svy: regress outcome `control_var' treatment i.smoke_status bmi
margins, dydx(treatment) post
estimates store m5
2. Standard Robustness Check Template
*------------------------------------------------------------
* Table: Robustness Checks
*------------------------------------------------------------
version 17
clear all
use "analysis_data.dta", clear
svyset cluster [pweight = weight]
* Base controls (always included)
local control_var i.batch leukocytes age i.race i.gender i.education i.marital
estimates clear
*--- Model 1: Baseline ---
svy: regress outcome `control_var' treatment
margins, dydx(treatment) post
estimates store m1
*--- Model 2: + COVID & Insurance ---
svy: regress outcome `control_var' treatment covid health_insurance
margins, dydx(treatment) post
estimates store m2
*--- Model 3: + Healthcare utilization ---
svy: regress outcome `control_var' treatment cci_charlson any_encounter_3years
margins, dydx(treatment) post
estimates store m3
*--- Model 4: + Multimorbidity ---
svy: regress outcome `control_var' treatment multi_morbidity
margins, dydx(treatment) post
estimates store m4
*--- Model 5: + Psychosocial factors ---
svy: regress outcome `control_var' treatment matter_important matter_depend
margins, dydx(treatment) post
estimates store m5
*--- Model 6: + Occupation ---
svy: regress outcome `control_var' treatment i.occ_group
margins, dydx(treatment) post
estimates store m6
*--- Model 7: + Smoking ---
svy: regress outcome `control_var' treatment i.smoke_status
margins, dydx(treatment) post
estimates store m7
*--- Model 8: + Childhood adversity ---
svy: regress outcome `control_var' treatment c.aces_sum_std
margins, dydx(treatment) post
estimates store m8
*--- Export ---
esttab m1 m2 m3 m4 m5 m6 m7 m8 using "robustness.csv", csv se ///
mtitle("Base" "+COVID" "+Health" "+Morbid" "+Psych" "+Occ" "+Smoke" "+ACE") ///
nogap label replace star(+ 0.1 * 0.05 ** 0.01)
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 · 251 lines · 17 tokens per session scan A af6d87f25285
robustness-checks is a skill published in the GitHub repository wentorai/research-plugins (302 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 1,776 once invoked, about $0.0001 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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