skills/14-luischanci-claude-code-research-starter/dot-claude/skills/interview-me/SKILL.md
Interactive interview to formalize a research idea into a structured specification with hypotheses and empirical strategy.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research interview-meInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Conduct a structured interview through 6 phases to formalize a research idea.
After the interview, produce a Research Specification Document:
# Research Specification: [Topic]
## Research Question
[Clear, specific, answerable]
## Motivation
[Why this matters -- policy relevance, theoretical gap, empirical puzzle]
## Hypothesis
[Testable prediction with expected sign/magnitude]
## Empirical Strategy
- **Method:** [DiD, IV, RDD, etc.]
- **Treatment/Variation:** [What provides identification]
- **Control group:** [Comparison group]
- **Identifying assumption:** [What must hold]
- **Robustness checks:** [Planned sensitivity analyses]
## Data
[Sources, sample, key variables, time period]
## Expected Results
[What you expect to find and why]
## Contribution
[How this advances the literature]
## Open Questions
[What remains uncertain]
Save to quality_reports/research_spec_[sanitized_topic].md.
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Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".
tools
Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".