interview-prep

A guide for preparing for a specific job interview using the candidate's real experience. It covers behavioral questions, role-related questions, STAR stories, practice interviews, and questions to ask the interviewer.

In plain words
What is it for?
Use it to plan preparation for recruiter, hiring-manager, technical, panel, or final interviews; rehearse answers; handle gaps or difficult questions; and prepare questions for the employer.
Why use it?
It gives preparation a structure matched to the role and helps turn past work into clear, concise answers without inventing experience.

Skill for Claude CodeCodex

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/jayrha/agentskills/interview-prep
Any agent
npx skills add JayRHa/AgentSkills --skill interview-prep
Clone the repo
git clone --depth 1 https://github.com/JayRHa/AgentSkills

Made for: Claude Code, Codex.

Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,061 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 $0.00095 $0.01061
Opus 5 $0.00048 $0.00531
Sonnet 5 $0.00019 $0.00212
Haiku 4.5 $0.00010 $0.00106

Measured 2d ago against content hash ce4841dbc994, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

interview-prep 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 2d 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.

interview-prep/SKILL.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Interview Prep

Overview

This skill builds a focused interview-prep plan for a specific role: the questions most likely to come up, polished STAR stories from the candidate's real experience, crisp answers to the hard ones (weaknesses, gaps, "why us"), and strong questions to ask the interviewer. It can also run a mock interview loop with feedback.

Keywords: interview, interview prep, behavioral interview, STAR method, mock interview, technical interview, system design, tell me about yourself, why this company, salary question, questions to ask.

When to use vs. not

Use this to prepare for upcoming interviews (phone screen, behavioral, technical, panel, final). It coaches the candidate's own stories — it does not invent experience. For pure coding/system-design content, pair with engineering skills; this skill focuses on structure, narrative, and delivery.

Inputs to gather first

  1. Role + company + the job description.
  2. Interview stage & format (recruiter screen, hiring-manager, technical, panel, final).
  3. The candidate's key projects/achievements to mine for stories.
  4. Known concerns: gaps, a pivot, thin experience in one area, nerves.
  5. Whether they want a study plan or a live mock.

Workflow

  1. Map the question surface. From the JD + stage, generate the realistic question set: ~5 behavioral, ~3 role-specific, plus the universals ("tell me about yourself," "why this company," "biggest weakness"). See references/question-bank.md.
  2. Build a story bank. Identify 4–6 of the candidate's experiences and shape each into a STAR story (Situation, Task, Action, Result). Tag each story with the competencies it demonstrates (leadership, conflict, failure, ambiguity, impact) so one story can answer several questions. See references/star-method.md.
  3. Craft "Tell me about yourself." A 60–90 second present → past → future arc tuned to the role. Not a life history.
  4. Pre-write the hard ones. "Greatest weakness" (real + mitigation), "why leaving," employment gap, "why this company" (specific + true), and the salary question (deflect-then-range). See references/tricky-questions.md.
  5. Prepare questions to ask back. 4–6 thoughtful questions about the role, team, success metrics, and challenges — never things a 30-second site visit answers.
  6. Run a mock (optional). Ask one question at a time, let the candidate answer, then give specific feedback: structure, concreteness, length, filler. Iterate.
  7. Logistics + close. Confirm format/time-zone/dress, who they're meeting, a pre-interview warmup, and a thank-you-note plan.

Read the full file on GitHub · 60 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 2d ago First seen · 60 lines · 95 tokens per session scan A ce4841dbc994

Subscribe to this mod's changes

interview-prep is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 95 tokens to every session and 1,061 once invoked, about $0.0005 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 2026-08-31.

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