interview-prep-generator

interview-prep-generator is a skill for Claude Code, Codex from Paramchoudhary/ResumeSkills. It costs 18 tokens per session (2,734 once invoked), scanned A, original, MIT.

An interview-preparation skill that turns a résumé and job description into practice material. STAR means Situation, Task, Action, and Result, a structure for answering experience-based questions.

In plain words
What is it for?
It creates role-specific questions, STAR stories, sample answers, talking points, possible concerns, and questions to ask during an interview.
Why use it?
It helps organize past experience and anticipate what an employer may ask for a particular role.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

About the project

ResumeSkills is a collection of AI-agent skills for improving resumes, preparing job applications, practicing interviews, and planning career moves. It is intended for job seekers, career changers, and professionals using Claude Code for tasks such as ATS checks, job-description matching, resume tailoring, and salary negotiation. The catalogue entry consists of the project's career-focused skills.

Paramchoudhary/ResumeSkills · 2,131 stars · on GitHub

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.

agentmods
npx agentmods add skills/paramchoudhary/resumeskills/interview-prep-generator
Any agent
npx skills add Paramchoudhary/ResumeSkills --skill interview-prep-generator
Clone the repo
git clone --depth 1 https://github.com/Paramchoudhary/ResumeSkills

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-prep-generator

README.md
[![agentmods](https://agentmods.dev/badge/skills/paramchoudhary/resumeskills/interview-prep-generator.svg)](https://agentmods.dev/skills/paramchoudhary/resumeskills/interview-prep-generator)
Your own site
<a href="https://agentmods.dev/skills/paramchoudhary/resumeskills/interview-prep-generator"><img src="https://agentmods.dev/badge/skills/paramchoudhary/resumeskills/interview-prep-generator.svg" alt="Measured on agentmods" height="20"></a>
Per session 18 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,734 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00018 $0.02734
Opus 5 $0.00009 $0.01367
Sonnet 5 $0.00004 $0.00547
Haiku 4.5 $0.00002 $0.00273

Measured 6d ago against content hash 1c543ab0eb22, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

interview-prep-generator 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 6d 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

2 near-identical copies found in the catalogue:

.agents/skills/interview-prep-generator/SKILL.md · 376 lines

How it starts

The opening of the file, as written. The whole thing — 376 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Prep Generator

When to Use This Skill

Use this skill when the user wants to:

  • Prepare for a job interview
  • Practice answering interview questions
  • Create STAR stories from their experience
  • Anticipate questions for a specific role
  • Mentions: "interview prep", "prepare for interview", "STAR stories", "interview questions", "behavioral questions"

Core Capabilities

  • Generate role-specific interview questions
  • Create STAR stories from resume bullets
  • Predict questions based on job description
  • Prepare answers for common questions
  • Create talking points for each experience
  • Identify potential concerns and prepare responses

Interview Preparation Framework

Phase 1: Role Analysis

  • Extract likely questions from job description
  • Identify skills that will be tested
  • Research company interview style

Phase 2: Story Banking

  • Convert resume bullets into STAR stories
  • Create stories for common competencies
  • Practice concise delivery

Phase 3: Mock Preparation

  • Practice common questions
  • Prepare questions to ask
  • Research company-specific topics

The STAR Method Detailed

Structure

  • Situation: Set the context (1-2 sentences)
  • Task: Describe your responsibility (1 sentence)
  • Action: Explain what YOU did (2-3 sentences)
  • Result: Share the outcome with metrics (1-2 sentences)

STAR Story Template

SITUATION: "At [Company], we faced [specific challenge/context]..."

TASK: "I was responsible for [specific ownership]..."

ACTION: "I [specific action 1], [specific action 2], and [specific action 3]..."

RESULT: "As a result, [quantified outcome]. This led to [business impact]."

Example STAR Story

Question: "Tell me about a time you led a team through a difficult project."

Answer:

SITUATION: "At TechCorp, our main product was losing customers to a competitor who had launched a better mobile experience. We were seeing 5% monthly churn, up from our normal 2%."

TASK: "As the product manager, I was responsible for turning around our mobile product to stop the bleeding and win back customers."

ACTION: "I started by interviewing 30 churned customers to understand exactly why they left. Based on that research, I prioritized 5 critical features that would achieve parity with competitors. I then worked with engineering to restructure our roadmap, negotiated with leadership to add 2 contract developers, and implemented weekly sprint reviews to keep the project on track. I also started a beta program with 50 of our best customers to get feedback before full launch."

RESULT: "We launched the improved mobile app in 3 months, reducing churn from 5% back to 2% within 60 days. We recovered 35% of churned customers and the NPS for our mobile app increased from 32 to 58. This project was recognized in our company all-hands as a turnaround success."

Read the full file on GitHub · 376 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. 6d ago First seen · 376 lines · 18 tokens per session scan A 1c543ab0eb22

Subscribe to this mod's changes

interview-prep-generator is a skill published in the GitHub repository Paramchoudhary/ResumeSkills (2,131 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 2,734 once invoked, about $0.0001 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.

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