pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest +…
Start a new research project by conducting a structured interview to formalize a research idea, then generates research questions with identification strategies and a project spec. Make sure to use this skill whenever the user wants to develop or document a new research idea —
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill new-project --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/new-projectContext preview
The summary Claude sees to decide when to auto-load this skill.
Start a new research project by conducting a structured interview to formalize a research idea, then generates research questions with identification strategies and a project spec. Make sure to use this skill whenever the user wants to develop or document a new research idea —
name: new-project description: >- Start a new research project by conducting a structured interview to formalize a research idea, then generates research questions with identification strategies and a project spec. Make sure to use this skill whenever the user wants to develop or document a new research idea — not to search for literature or data. Triggers include: "new project", "start research", "I have an idea", "help me develop this", "I want to study X", "help me formalize this idea", "what's my research question", "what identification strategy should I use", "write up my project idea", or when the user describes a topic they want to turn into a paper. argument-hint: "[brief topic or 'start fresh']" allowed-tools: ["Read", "Grep", "Glob", "Write"]
Formalize a research idea into a concrete project specification with testable hypotheses and empirical strategies.
**Input:** `$ARGUMENTS` — a topic, phenomenon, dataset, or "start fresh" for open-ended exploration.
This skill runs in **three phases**. Phase 1 is conversational — ask one or two questions at a time and wait for responses. Phases 2 and 3 run automatically after the interview.
---
**Goal:** Draw out the researcher's thinking and establish a clear research question.
Ask questions **one or two at a time**. Build on each answer before moving to the next phase. Do NOT use AskUserQuestion — ask directly in your response. A good interview runs 4–6 exchanges.
**The Puzzle (start here):**
**Why It Matters:**
**Theoretical Motivation:**
**Data and Setting:**
**Identification:**
**Expected Results + Contribution:**
Move to Phase 2 when you have:
If after 3 exchanges the user keeps giving vague answers, move to Phase 2 anyway and flag the open questions.
---
**Goal:** Generate 3–5 structured research questions covering the full range from descriptive to causal.
Announce the transition: *"Great — I have enough to generate a structured set of research questions. Let me build that out now."*
Then generate **3–5 research questions** ordered by type:
| Type | What It Asks | |------|-------------| | **Descriptive** | What are the patterns? How has X evolved? | | **Correlational** | What factors are associated with X, controlling for Z? | | **Causal** | What is the causal effect of X on Y? | | **Mechanism** | Through what channel does X affect Y? | | **Policy** | Would intervention X improve outcome Y? |
**For each RQ, develop:**
**Rank the questions** by feasibility × contribution:
| RQ | Feasibility | Contribution | Priority | |----|-------------|-------------|----------| | 1 | High | High | ★★★ | | 2 | High | Medium | ★★ | | ... | ... | ... | ... |
---
Produce the unified project spec document and save it.
**Save to:** `quality_reports/project_spec_[sanitized_topic].md`
# Research Project: [Working Title] **Date:** [YYYY-MM-DD] **Researcher:** [from CLAUDE.md if available] --- ## Research Question [Single clear sentence] ## Motivation [2–3 paragraphs: why this matters, theoretical context, policy relevance, what the answer would change] ## Research Questions ### RQ1: [Question] — Priority: ★★★ (Feasibility: High / Contribution: High) **Type:** Causal **Hypothesis:** [Testable prediction with expected sign] **Identification Strategy:** - **Method:** [e.g., Staggered DiD with Sun–Abraham estimator] - **Treatment:** [What varies and when] - **Control group:** [Comparison units] - **Key assumption:** [e.g., Parallel pre-trends conditional on controls] - **Robustness:** [Pre-trends test, placebo outcomes, alternative control groups] **Data Requirements:** - [Dataset or data type needed] - [Key variables: treatment proxy, outcome, controls] - [Time period and geography] **Key Pitfalls:** 1. [Threat + mitigation] 2. [Threat + mitigation] **Related Work:** [Author (Year)], [Author (Year)] --- [Repeat for RQ2–RQ5] --- ## Priority Empirical Strategy [1 paragraph recommending the single highest-priority RQ and why, with the specific identification approach] ## Open Questions [Issues raised in the interview that need fur
📌 文档结构(2026-07-22 起): 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md(扩展正文,总表行内的 → 直接跳转到对应锚点)。 English version: README-en.md · 中文扩展正文:docs/CONTENT_ZH.md · README-zh-CN.md 已弃用(重定向占位) 🌐 语言: English |
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