Passed 24 of 24 test cases with the skill, and 22 of 24 without it.
Without skill92%
With skill100%
Checks it passed that the model alone missed
Uses QuantReg to fit conditional quantile regression models.
Recommends fitting a ZeroInflatedPoisson model for count data with excess zeros.
2 fixed22 passed either way0 still failing0 broke
Pass-rate lift
+8 pp
Tokens
+246%
Agent turns
0%
Response time
-8%
Model gemini-3.6-flashMeasured 8/28/2026v1 · latest is v224 of 24 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
See it in action
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 2
Prompt
I am modeling doctor visit counts. Over 70% of the sample has zero visits, and standard Poisson or Negative Binomial GLMs severely underpredict the proportion of zeros. What specialized statistical model class should I use to handle this two-component zero-inflated process?
What was checked, without and with the skill
Checked
Without skill
With skill
Recommends fitting a ZeroInflatedPoisson model for count data with excess zeros.
Missed
Passed
case-14 · gemini-3.6-flash · 8/28/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md24 lines2 files
Statsmodels: Statistical Modeling and Econometrics
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.