pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Run a structural estimation pipeline — routes to /workflows:work with estimation context from empirical-playbook
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill estimate -a windsurf -y
name: estimate description: "Run a structural estimation pipeline — routes to /workflows:work with estimation context from empirical-playbook" argument-hint: "<estimation specification or task>" disable-model-invocation: true
This command routes to `/workflows:work` with estimation pipeline context.
Before starting, load the `empirical-playbook` skill and its `references/estimation-pipeline.md` for the phased estimation workflow (data validation → identification → estimation → standard errors → robustness → results).
Now run `/workflows:work` with the estimation pipeline framing. Follow the phase gates in `estimation-pipeline.md` — do not proceed to the next phase until the current phase's gate conditions are met.
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