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End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step.
Passed 8 of 15 test cases with the skill, and 9 of 15 without it.
1 fixed10 passed either way8 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A test case the skill fixed. Same model, same prompt, with and without the skill.
We want to perform a robust sensitivity analysis checking whether extreme exposure values bias our logistic regression result. What specific numeric z-score threshold should we use to filter out extreme exposure values?
| Checked | Without skill | With skill |
|---|---|---|
| Filters out observations with an exposure z-score magnitude of 3 or greater. | Missed | Passed |
The instructions your model reads when the skill loads.
Complete workflow for observational epidemiology — from research question to publication-ready report. Write and run Python code for every step. Never describe what you "would do" — do it.
Define Population, Exposure, Comparator, Outcome before touching data.
Study design check: Does the question require temporality?
If the question implies causation ("does X cause Y?") but only cross-sectional data is available, state the limitation explicitly and proceed with association language.
Use ToolUniverse to discover datasets and find what prior studies used:
# Search for relevant datasets — use find_tools to discover what's available
find_tools("dataset search")
find_tools("your domain keywords") # e.g., "cancer genomics", "clinical trial", "survey health"
# Search literature for study precedents — papers cite their data sources
execute_tool("PubMed_search_articles", {"query": "[exposure] [outcome] [study design]", "max_results": 5})
execute_tool("EuropePMC_search_articles", {"query": "[exposure] [outcome] cohort", "limit": 5})
Evaluate dataset fitness: Does it have the exposure variable? The outcome? Key confounders (age, sex, plus domain-specific)? Adequate sample size?
Power analysis (run before committing to a dataset):
test cases passed, vs 9 / 15 without
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