Research Interview
SkillDev toolsStructured conversational interview to formalise a research idea or extension into a concrete specification with hypotheses and empirical strategy. This skill should be used when asked to "interview me", "help me think through an idea", "formalise this idea", or "start fresh" on a new research direction.
Use Research Interview in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Research Interview and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Research Interview skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/41-sticerd-eee-sewage-econometrics-check/skills/interview-me/SKILL.md and read by ahel’s review.
Conduct a structured interview to help formalise a research idea into a concrete specification.
Input: $ARGUMENTS — a brief topic description or "start fresh" for an open-ended exploration.
How This Works
This is a conversational skill. Ask questions one at a time, probe deeper based on answers, and build toward a structured research specification.
Ask questions directly in text responses, one or two at a time. Wait for the user to respond before continuing.
Interview Structure
Phase 1: The Big Picture (1-2 questions)
- "What phenomenon or puzzle are you trying to understand?"
- "Why does this matter? Who should care about the answer?"
Phase 2: Theoretical Motivation (1-2 questions)
- "What's your intuition for why X happens / what drives Y?"
- "What would standard theory predict? Do you expect something different?"
Phase 3: Data and Setting (1-2 questions)
- "What data do you have access to, or what data would you ideally want?"
- "Is there a specific context, time period, or institutional setting you're focused on?"
For this project, also probe:
- Can this be answered with the existing EDM + Land Registry + Zoopla data?
- Does this require new data (e.g. water company financials, bathing water quality, health data)?
Phase 4: Identification (1-2 questions)
- "Is there a natural experiment, policy change, or source of variation you can exploit?"
- "What's the biggest threat to a causal interpretation?"
Phase 5: Expected Results (1-2 questions)
- "What would you expect to find? What would surprise you?"
- "What would the results imply for policy or theory?"
Phase 6: Contribution (1 question)
- "How does this differ from what's already been done? What's the gap you're filling?"
After the Interview
Once enough information is gathered (typically 5-8 exchanges), produce:
Research Specification Document
# Research Specification: [Title]
**Date:** YYYY-MM-DD
## Research Question
[Clear, specific question in one sentence]
## Motivation
[2-3 paragraphs: why this matters, theoretical context, policy relevance]
## Hypothesis
[Testable prediction with expected direction]
## Empirical Strategy
- **Method:** [e.g., Difference-in-Differences]
- **Treatment:** [What varies]
- **Control:** [Comparison group]
- **Key identifying assumption:** [What must hold]
- **Robustness checks:** [Pre-trends, placebo tests, etc.]
## Data
- **Primary dataset:** [Name, source, coverage]
- **Key variables:** [Treatment, outcome, controls]
- **Sample:** [Unit of observation, time period, N]
- **Available in project:** [Yes/No — what exists vs what's needed]
## Expected Results
[What the researcher expects to find and why]
## Contribution
[How this advances the literature — 2-3 sentences]
## Open Questions
[Issues raised during the interview that need further thought]
## Feasibility Assessment
- Data availability: [Ready / Partially available / Needs collection]
- Infrastructure reuse: [What from the existing pipeline can be reused]
- Estimated effort: [Low / Medium / High]
Save to output/log/research_spec_[topic].md.
Interview Style
- Be curious, not prescriptive. Draw out the researcher's thinking, don't impose ideas.
- Probe weak spots gently. "What would a sceptic say about...?" rather than "This won't work."
- Build on answers. Each question should follow from the previous response.
- Know when to stop. If the researcher has a clear vision after 4-5 exchanges, move to the specification.
- Project-aware. Connect ideas to the existing sewage project infrastructure where relevant.
Signals
- GitHub stars
- 5k
- Forks
- 536
- Last commit
- Oct 2026
- Hacker News mentions
- 1
Advanced
- Item type
- skill
- Key
interview-me-brycewang-stanford- Source
- github.com/brycewang-stanford/auto-empirical-research-skills
github.com/brycewang-stanford/auto-empirical-research-skills
Related picks
Skill · larksuite
The pick for Markdownmarkdown-formatter
Skill · nvidia
The pick for Markdownacademic-paper-composer
Skill · brycewang-stanford
The pick for Academic03-academic-writing
Skill · 24kchengye
The pick for Academicteach
Skill · mattpocock
More in Dev toolsimplement
Skill · mattpocock
More in Dev tools