pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill workflows-ideate -a windsurf -y
name: workflows:ideate description: "Divergent research ideation — generate many candidate directions, then adversarially filter to the strongest" argument-hint: "<research question, puzzle, or data opportunity>" allowed-tools: Read, Glob, Bash
Divergent exploration before convergent brainstorming. Generate many candidates, then filter ruthlessly.
Read $ARGUMENTS. If the user provides a specific research question, ideate around it. If they provide a broad topic, explore broadly.
Generate 15-20 candidate research directions. Use these research-adapted ideation frames:
1. **Identification weakness** — What existing results have weak identification? What new variation could fix it? 2. **Computational bottleneck** — What problems are infeasible with current methods but tractable with new estimators or hardware? 3. **Data limitation workaround** — What would become possible with data that is now available but underexploited? 4. **Alternative estimator class** — What if the standard approach (e.g., linear IV) were replaced with a different class (e.g., ML, structural, Bayesian)? 5. **Relaxed assumption** — What results depend on assumptions that could be relaxed? What happens when you relax them? 6. **Literature gap** — What do practitioners need that academics haven't provided? What do adjacent fields know that this field doesn't?
Dispatch `methods-explorer` and `literature-scout` agents in parallel to ground the ideation in real methods and recent papers.
**Iron rule:** Generate the full candidate list before critiquing any idea. Push past the first few obvious directions.
**Entry condition:** Phase 1 produced at least 15 candidate directions (the iron rule). **Exit condition:** 5-7 survivors identified, all rejected candidates have one-line rejection reasons.
For each candidate, evaluate:
Dispatch `identification-critic` to attack the top 10 candidates. Only candidates surviving adversarial scrutiny advance.
**Target:** 5-7 survivors with explicit rejection reasons for all others.
Write the ideation document to `docs/ideation/` with YAML frontmatter:
--- status: complete date: YYYY-MM-DD topic: <descriptive topic> candidates_generated: <N> survivors: <N> ---
Content:
End with: "Ideation complete. Run `/workflows:brainstorm [top candidate]` to develop requirements for the strongest direction."
## 🔥 力荐 · StatsPAI — Stata 与 R 的 Python 平替,为 AI Agent 而生 🔁 Stata / R 平替:reghdfe、csdid、rdrobust、synth、fixest、did 等常用命令在 Python 中一站式替代,一个 import statspai as sp 搞定 DID / RD / IV / SCM / DML / matching 🤖 面向 Agent 设计:结构化结果对象 + 内置 MCP Server,Claude / Codex
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