skills/41-sticerd-eee-sewage-econometrics-check/skills/interview-me/SKILL.md
Structured 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.
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.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
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.
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.
For this project, also probe:
Once enough information is gathered (typically 5-8 exchanges), produce:
# 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.
development
Conduct rigorous thematic analysis (TA) of qualitative data following Braun and Clarke's (2006) six-phase framework. Use whenever the user mentions 'thematic analysis', 'TA', 'Braun and Clarke', 'qualitative coding', 'identifying themes', or asks for help analysing interviews, focus groups, open-ended survey responses, or transcripts to identify patterns. Also trigger for questions about inductive vs theoretical coding, semantic vs latent themes, essentialist vs constructionist epistemology, building a thematic map, or writing up a qualitative findings section. Covers all six phases, the four upfront analytic decisions, the 15-point quality checklist, and the five common pitfalls. Produces a Word document write-up and an annotated thematic map. Does NOT cover IPA, grounded theory, discourse analysis, conversation analysis, or narrative analysis — use a different method for those.
development
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework. Use this skill whenever the user mentions 'systematic review', 'systematic literature review', 'SLR', 'PRISMA', 'PRISMA 2020', 'PRISMA flow diagram', 'PRISMA checklist', or asks for help writing, structuring, or auditing a literature review that follows reporting guidelines. Also trigger when the user asks about inclusion/exclusion criteria for a review, search strategies for databases like Scopus/WoS/PubMed, study selection processes, risk of bias assessment, or narrative synthesis for a review paper. This skill covers the full PRISMA 2020 checklist (27 items), produces a Word document manuscript in strict journal article format, generates an annotated PRISMA flow diagram, and enforces APA 7th Edition referencing throughout. It does NOT cover meta-analysis or statistical pooling. By Chuah Kee Man.
testing
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
data-ai
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.