interview-kit

interview-kit is a skill for Claude Code, Codex from shawnpang/startup-founder-skills. It costs 34 tokens per session (1,751 once invoked), scanned A, original, MIT.

A guide for designing structured job interviews, including questions, interview stages, scoring rubrics, and interviewer calibration. A scorecard is a shared method for judging candidates against defined skills and evidence.

In plain words
What is it for?
Use it to turn a job description into interview rounds, create question banks, define observable competencies, and build repeatable candidate evaluations.
Why use it?
It helps interviewers evaluate people consistently and reduces decisions based mainly on personal impressions or bias.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to turn a job description into interview rounds, create question banks, define observable competencies, and build repeatable candidate evaluations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/shawnpang/startup-founder-skills/interview-kit
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add shawnpang/startup-founder-skills --skill interview-kit
Clone the repo
git clone --depth 1 https://github.com/shawnpang/startup-founder-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for interview-kit

README.md
[![agentmods](https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/interview-kit/github.svg)](https://agentmods.dev/skills/shawnpang/startup-founder-skills/interview-kit)
Your own site
<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/interview-kit"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/interview-kit/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for interview-kit

Your own site · 80×15
<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/interview-kit"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/interview-kit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,751 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.01751
Opus 5 $0.00017 $0.00875
Sonnet 5 $0.00007 $0.00350
Haiku 4.5 $0.00003 $0.00175

Measured 11d ago against content hash d838727f6f05, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

interview-kit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/interview-kit/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 142 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 142 lines · 34 tokens per session scan A d838727f6f05

Subscribe to this mod's changes

interview-kit is a skill published in the GitHub repository shawnpang/startup-founder-skills (320 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 1,751 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens