Interview Kit logo

Interview Kit

Community
shawnpang
interview-kit

When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.

Overview

Publishershawnpang
Repositorystartup-founder-skills
Skill nameinterview-kit
Stars
368
Forks
43
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by shawnpang on GitHub. Read the source before you install it.

Installation

Install the Interview Kit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/shawnpang/startup-founder-skills/tree/main/skills/interview-kit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/shawnpang/startup-founder-skills.git /tmp/startup-founder-skills
mkdir -p .claude/skills
cp -r /tmp/startup-founder-skills/skills/interview-kit .claude/skills/interview-kit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview Kit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Interview Kit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Interview Kit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Interview Kit

When to Use

  • Designing a structured interview loop for a specific role and level
  • Creating standardized question banks organized by interview round type
  • Building scoring rubrics for consistent candidate evaluation across interviewers
  • Reducing interviewer bias with process controls and calibration
  • Turning a job description into a repeatable evaluation process
  • Calibrating interview panels after quarterly hiring outcome reviews

Context Required

  • From startup-context: Company stage, team size, engineering culture, current interview process (if any), hiring velocity
  • From user: Role title, level (junior/mid/senior/staff), key competencies to evaluate, number of interview rounds the team can support, whether a take-home or live exercise is preferred

Workflow

  1. Define competencies — Extract 4-6 core competencies from the job description. Split into technical skills, domain knowledge, collaboration traits, and startup-fit signals. Each competency must be evaluable with observable evidence.
  2. Design the interview loop — Map competencies to interview stages with explicit, non-overlapping objectives per round. Typical startup loop: recruiter/founder screen, technical assessment, team interview, values interview. Assign timing and interviewers to each stage.
  3. Write structured questions — For each stage, write 3-5 primary questions with follow-up probes. Every question must map to a specific competency. Include "what good looks like" answer guidance so interviewers know what signal they are looking for.
  4. Build scorecards — Create a 1-4 rating scale (not 1-5 — it creates a "3 means fine" dead zone). Define behavioral anchors at each level specific to the role. Interviewers must score independently before the debrief.
  5. Design take-home or live exercise — If applicable, create a practical assessment that mirrors real work. Time-cap it (2-4 hours max), share the evaluation rubric with the candidate upfront, and always follow up with a live walkthrough.
  6. Add anti-bias guardrails — Require structured debrief instructions, independent scoring protocol, and a checklist of common bias traps. Every candidate for the same role gets the same core questions in the same order.
  7. Plan calibration cadence — Set quarterly recalibration using hiring outcome data. Review whether loop design still surfaces the right signals based on quality-of-hire metrics.

Output Format

A complete interview kit document containing:

  • Role summary and competency matrix (4-6 competencies with definitions)
  • Interview loop overview (stages, duration, interviewers, competency mapping)
  • Per-stage question sets with follow-up probes and scoring rubrics
  • Take-home or live exercise brief with time cap and evaluation criteria
  • Scorecard template (1-4 scale with behavioral anchors)
  • Debrief protocol with independent scoring and evidence-based discussion rules
  • Compensation benchmarking notes (if requested)

Frameworks & Best Practices

Competency Categories by Role Type

  • Engineering: System design, code quality, debugging approach, technical communication, ownership/initiative
  • Product: Customer empathy, prioritization frameworks, cross-functional communication, data-informed thinking, shipping velocity
  • Go-to-market: Discovery/qualification, storytelling, objection handling, pipeline management, customer orientation
  • Design: Design process, craft quality, user research fluency, systems thinking, collaboration with engineering

Scorecard Design (1-4 Scale)

  • 1 — Does not meet bar: Could not demonstrate the competency. Clear concerns.
  • 2 — Below bar: Showed partial ability but gaps are significant for the level.
  • 3 — Meets bar: Demonstrated the competency at the expected level. Solid hire signal.
  • 4 — Exceeds bar: Demonstrated exceptional strength. Would raise the team's capability.

Each score level must include 1-2 concrete behavioral anchors specific to the role being evaluated.

The STAR-B Question Framework

Structure behavioral questions to elicit complete, pattern-revealing answers:

  • Situation: Set the scene
  • Task: What was your responsibility
  • Action: What specifically did you do
  • Result: What happened
  • Behavior pattern: Is this a repeatable pattern or a one-off

Example: "Tell me about a time you had to ship something with significant technical debt. What was the situation, what did you decide, and how did it play out? Would you make the same call again?"

Anti-Bias Techniques

  • Structured questions: Every candidate for the same role gets the same core questions in the same order
  • Independent scoring: Interviewers submit scores before the debrief meeting — no anchoring on a senior person's opinion
  • Blind resume review: Strip names, photos, school names, and company names in the initial screen where possible
  • Diverse interview panels: Include at least one interviewer from an underrepresented background when possible
  • Language check: Before writing feedback, ask "Would I say this about a different candidate?" to catch biased framing
  • Replace "culture fit" with "values alignment" and require specific behavioral evidence

Take-Home Assignment Guidelines

  • Time-capped: 2-4 hours maximum. State this explicitly and mean it.
  • Mirrors real work: The exercise should resemble an actual task the person would do in the role
  • Transparent criteria: Share the rubric with the candidate upfront so they know what you value
  • Equitable access: Offer a paid alternative if the candidate cannot invest unpaid time
  • Debrief required: Always follow up with a live walkthrough where the candidate explains their choices

Common Pitfalls

  • Overweighting one round while ignoring other competency signals
  • Using unstructured interviews without standardized scoring
  • Skipping calibration sessions for interviewers
  • Changing the hiring bar without documenting rationale
  • Letting round objectives overlap so multiple stages test the same thing

Compensation Benchmarking Framework

Use three inputs to triangulate:

  1. Market data: Levels.fyi, Pave, Carta Total Comp, Glassdoor as directional
  2. Stage multiplier: Seed pays 70-85% of big-co base with 0.5-2% equity; Series A narrows to 80-95%
  3. Candidate calibration: Adjust for experience, competing offers, and location within the level band

Always present comp as a range with a target midpoint, not a single number.

Related Skills

  • job-description — Use the JD's competency requirements as input for the interview loop
  • sourcing-outreach — Align outreach messaging with the interview process so candidates know what to expect

Examples

Prompt: "Design an interview loop for a senior backend engineer. 15-person startup."

Good output snippet:

## Interview Loop — Senior Backend Engineer

### Competencies to Evaluate
1. System design & architecture (technical depth)
2. Code quality & testing practices (craft)
3. Debugging & production thinking (operational maturity)
4. Technical communication (collaboration)
5. Ownership & initiative (startup fit)

### Stage 1: Founder Screen (30 min)
- Evaluate: Motivation, communication, logistics
- Questions:
  - "What's drawing you to an early-stage company right now?"
  - "Walk me through the most impactful project you led in the last year."
- Scorecard: 1-4 on communication, motivation, startup-fit

### Stage 2: Technical Deep-Dive (60 min)
- Evaluate: System design, code quality
- Format: Live system design discussion + code review exercise
- Scorecard: 1-4 on architecture thinking, code craft, trade-off reasoning

Prompt: "Our interviewers keep disagreeing on candidates."

Good output snippet:

This usually means you lack structured evaluation criteria. Three-step fix:

1. Define 4-5 competencies per role with written behavioral descriptions
2. Give each interviewer a scorecard to fill out independently BEFORE debrief
3. In the debrief, discuss only scores that diverge by 2+ points —
   focus on evidence, not impressions

The goal is calibrated, evidence-based evaluation — not consensus.

Frequently asked questions

What does the Interview Kit AI skill do?

When the user needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.

Why use Interview Kit on TypingMind?

Because you install it once and use it with any model. Interview Kit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Interview Kit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shawnpang/startup-founder-skills/tree/main/skills/interview-kit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Interview Kit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Interview Kit?

As many as you like. As long as a model supports skills, you can use Interview Kit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Interview Kit AI skill free?

Yes. It is published on GitHub by shawnpang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇