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Use when estimating a statistical or econometric model, running a regression, specifying an identification strategy, testing a hypothesis, or fitting any model to empirical data. Guides the process (assumptions, estimation, reporting, diagnostics) without forcing a fixed method list.
Passed 18 of 22 test cases with the skill, and 7 of 22 without it.
and 13 more in the full report
11 fixed7 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to estimate the impact of state-level minimum wage increases on youth employment using panel data from 2005 to 2020. Can you help me set up and run a difference-in-differences regression on this dataset? Please provide the estimation code, evaluate the validity of the underlying causal design, explain all variables in the specification along with their measurement scales, and generate a formatted regression table displaying coefficients, standard errors, sample size, and model fit statistics.
| Checked | Without skill | With skill |
|---|---|---|
| Defines the estimand explicitly in a single sentence before presenting code. | Missed | Passed |
| Performs post-estimation diagnostic checks on the fitted specification. | Missed | Passed |
| Signals to invoke robustness-checks after producing the primary specification result. | Missed | Passed |
The instructions your model reads when the skill loads.
This skill defines the process for statistical modeling in empirical research. It is method-agnostic and field-agnostic — the appropriate method for a research question comes from the data and the estimand, not from a fixed list. Reference files organized by method family (cross-section, panel, causal inference, time series) offer starting points, but they are not exhaustive and not a boundary. Bayesian methods, machine learning, spatial models, and any other approach can be used as long as the modeling process below is followed.
Every estimation follows the six-phase modeling process below. Each phase is mandatory — skipping one corrupts inference downstream.
Each phase is expanded below.
What parameter do you want to learn? Is it causal or descriptive? What population does it apply to? Without a clear estimand, method choice is premature. Write the estimand down in one sentence before touching the data.
Every method has assumptions — exogeneity, stationarity, parallel trends, common support, no interference, proportional hazards, and so on. Check the ones that apply to your method. Document which assumptions are plausible given the context and which are weak. Weak assumptions force either a different method or a robustness discussion.
test cases passed, vs 7 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.