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Agent skill

Interview Me

by flonat in flonat/flonat-research

Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly.

MITAuto-check passedAgent Workflows

Install Interview Me

skills CLI
$ npx skills add flonat/flonat-research --skill interview-me -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install flonat/flonat-research interview-me --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/interview-me .claude/skills/interview-me && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
interview-me
GitHub stars
145
Token cost
~1.6k tokens
SKILL.md length
665 words
Files
1
Skills in repo
83
Repo updated
First seen
Licence
MIT

At a glance

Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly.

  • Works in 7 steps: The Big Picture (1–2 questions) → Theoretical Motivation (1–2 questions) → Data and Setting (1–2 questions) → …
  • The needed information is in the users head and cannot be recovered from project files
  • SKILL.md covers How This Works, Interview Structure, Adapting to the Research Area and After the Interview, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Interview Me is an agent skill from flonat/flonat-research. Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly. Use when the needed information is in the user's head and cannot be recovered from project files. Not for adversarial oral examination; use $grill-me.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Requirements gathering. The repository describes itself as: Shareable Claude Code + Codex infrastructure for PhD researchers — skills, agents, hooks, and rules for academic workflows. The licence is MIT.

When your agent uses it

  • The needed information is in the users head and cannot be recovered from project files
  • Tasks that involve Requirements gathering

Example prompts

  • “/interview-me”

Requirements

  • Pre-approved tools (allowed-tools): Read, Write, Edit

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. The Big Picture (1–2 questions)
  2. Theoretical Motivation (1–2 questions)
  3. Data and Setting (1–2 questions)
  4. Identification (1–2 questions)
  5. Expected Results (1–2 questions)
  6. Contribution (1 question)
  7. Field Calibration (optional, auto-triggered)

What it can do on your machine

Read from SKILL.md and the folder at commit da27600. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Interview Me loads about 1.6k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 665 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from flonat/flonat-research at commit da27600, republished under its MIT licence (© flonat). 665 words, ~1,642 tokens.

Download SKILL.mdSave it as .claude/skills/interview-me/SKILL.md (or your agent's skills folder).
name
interview-me
description
Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly. Use when the needed information is in the user's head and cannot be recovered from project files. Not for adversarial oral examination; use $grill-me.
allowed-tools
Read, Write, Edit
disable-model-invocation
true
argument-hint
[brief topic or 'start fresh']

Research Interview

Conduct a structured interview to help formalise a research idea into a concrete specification.

Input: $ARGUMENTS — a brief topic description or "start fresh" for open-ended exploration.

How This Works

This is a conversational skill. Instead of producing a report immediately, you conduct an interview by asking questions one at a time, probing deeper based on answers, and building toward a structured research specification.

Do NOT use the available structured-question mechanism. Ask questions directly in your text responses, one or two at a time. Wait for the user to respond before continuing.

Before starting, read .context/profile.md and .context/projects/_index.md to understand the researcher's areas and active projects. If the topic relates to an existing project, read its context file too.

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?"
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?"

Adapting to the Research Area

the user's work spans multiple disciplines. Adapt the interview to the domain:

  • Human-AI collaboration / MCDM: Focus on decision architecture, experimental design, behavioural measures, and what "better" decisions look like.
  • Multi-agent systems: Focus on agent design, interaction protocols, equilibrium concepts, and simulation methodology.
  • Organisational behaviour: Focus on mechanisms, field vs. lab settings, mediators/moderators, and internal validity.
  • Carbon markets / environmental: Focus on policy variation, compliance data, market microstructure, and welfare implications.

If the research is non-quantitative (conceptual, design science, qualitative), adjust: replace "Identification" with "Analytical Framework" and "Data" with "Empirical/Evidence Strategy".

Show full SKILL.md (292 more words)Show less
Phase 7: Field Calibration (optional, auto-triggered)

Auto-triggers when: the project has no .context/field-calibration.md, or it exists but still contains <placeholders>.

Skip when: the file already exists with populated content, unless the user explicitly asks to update it.

Ask 2–3 targeted questions:

  • "Which journals or conferences are you targeting? I can cross-reference venue rankings." (Use .context/resources/venue-rankings.md to validate and suggest alternatives.)
  • "Which seminal papers would a reviewer in this subfield expect to see cited?"
  • "What's the typical identification strategy in this subfield — and what do reviewers most often attack?"

After the interview, populate .context/field-calibration.md from answers combined with Research Spec content. Use the template at skills/init-project-research/templates/field-calibration.md.

If field-calibration already exists with content: ask the user whether to update specific sections or keep as-is.

After the Interview

Once you have enough information (typically 5–8 exchanges), produce a Research Specification Document:

markdown
# Research Specification: [Title]

**Date:** [YYYY-MM-DD]
**Researcher:** the user

## 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., DiD, experiment, simulation, case study]
- **Treatment/Manipulation:** [What varies]
- **Control/Comparison:** [Comparison group or baseline]
- **Key identifying assumption:** [What must hold]
- **Robustness checks:** [Pre-trends, placebo, alternative specifications]

## Data

- **Primary dataset:** [Name, source, coverage]
- **Key variables:** [Treatment, outcome, controls]
- **Sample:** [Unit of observation, time period, N]

## 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]

Save to: the project root or docs/ if inside a research project, or present to the user for placement.

Also produces (if Phase 7 triggered): .context/field-calibration.md — the per-project domain profile that agents use to calibrate reviews.

Interview Style

  • Be curious, not prescriptive. Your job is to draw out the researcher's thinking, not impose your own ideas.
  • Probe weak spots gently. If the identification strategy sounds fragile, ask "What would a sceptic say about...?" rather than "This won't work because..."
  • 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. Don't over-interview.
  • Use British English throughout (the user's preference).

Cross-References

SkillWhen to use instead/alongside
devils-advocateAfter the spec is written — stress-test the idea
literatureTo find related work mentioned during the interview
init-project-researchTo scaffold a project once the spec is approved (seeds empty field-calibration)

© flonat, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/interview-me of flonat/flonat-research.

Open the folder on GitHubat commit da27600

Compare with similar skills

Interview Me next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Interview Me compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Interview Me this skillflonat/flonat-research145—~1.6kAutomated safety check: PassMIT
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Interview Meaddyosmani/agent-skills103k6 repos~3.8kAutomated safety check: PassMIT
Grillingpietheinstrengholt/rssmonster56432 repos~510Automated safety check: PassMIT
Agentic Workflow Designerdotnet/Open-XML-SDK4.6k2 repos~3.5kAutomated safety check: PassMIT
Ask User QuestionMemTensor/MemOS12k—~1kAutomated safety check: PassApache-2.0

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Categories

Questions about Interview Me

What does Interview Me do?

Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly. Interview Me is an agent skill from flonat/flonat-research. Conduct an adaptive structured interview that elicits tacit knowledge, requirements, preferences, or decisions and summarizes them explicitly.

When should I use Interview Me?

Interview Me fits situations like: the needed information is in the users head and cannot be recovered from project files; tasks that involve Requirements gathering.

How do I install Interview Me in Claude Code?

Run `npx skills add flonat/flonat-research --skill interview-me -a claude-code`. Or copy the skill folder (skills/interview-me in flonat/flonat-research) into .claude/skills/interview-me in your project. Claude Code loads it when a task matches its description.

How do I install Interview Me in Codex?

Run `npx skills add flonat/flonat-research --skill interview-me -a codex`. Or copy the skill folder (skills/interview-me in flonat/flonat-research) into .agents/skills/interview-me in your project. Codex loads it when a task matches its description.

Can I use Interview Me in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flonat/flonat-research --skill interview-me -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/interview-me, .gemini/skills/interview-me, .github/skills/interview-me and .opencode/skills/interview-me in your project.

What does Interview Me need to run?

SKILL.md names no scripts, command-line tools or credentials: Interview Me is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit.

Does Interview Me access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Interview Me safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Interview Me use?

Interview Me is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Interview Me use?

About 1.6k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Interview Me?

Skills that share tags, products or a category with Interview Me: Using Superpowers (farm-fe/farm, 5.6k stars), Interview Me (addyosmani/agent-skills, 103k stars), Grilling (pietheinstrengholt/rssmonster, 564 stars) and Agentic Workflow Designer (dotnet/Open-XML-SDK, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Interview Me?

flonat (a GitHub user) maintains it in flonat/flonat-research, which has 145 GitHub stars. The repository holds 83 skills in this directory. The repository was last updated on September 29, 2026.

Source: flonat/flonat-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.