What is prep-interview?

An interview-preparation brief containing likely questions, profile-based STAR talking points, a checklist, and questions to ask the employer. STAR is a way to structure an answer around a situation, task, action, and result.

Use it to prepare for screening, technical, hiring-manager, panel, or final interviews, including stories based on real experience.

It turns a job and company context into a focused preparation plan instead of leaving the candidate to guess what to practise.

prep-interview — Skill for Claude Code by jain777/jobclaw-skills

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 jain777/jobclaw-skills --skill prep-interview

Clone the repo

git clone --depth 1 https://github.com/jain777/jobclaw-skills

Made for: Claude Code.

Or install jobclaw-skills, the plugin that ships this one along with the rest of its 20 skills.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/jain777/jobclaw-skills/prep-interview/github.svg)](https://agentmods.dev/skills/jain777/jobclaw-skills/prep-interview)

Your own site

<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/prep-interview"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/prep-interview/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/jain777/jobclaw-skills/prep-interview"><img src="https://agentmods.dev/badge/skills/jain777/jobclaw-skills/prep-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,403 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.

Model	Per session	Once invoked
Fable 5.1	$0.00067	$0.01403
Opus 5.5	$0.00027	$0.00561
Sonnet 5.5	$0.00013	$0.00281
Haiku 4.5	$0.00007	$0.00140

Measured 2mo ago against content hash 14199e6a3707, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

prep-interview 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 2mo 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.

skills/prep-interview/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

prep-interview

Walk into the room ready. Hard rules: ../_shared/RULES.md. Question quality bar: reference/prep-rubric.md. Verb sourcing per tailor-resume/reference/action-verbs.md.

Inputs

company + role (required).
Profile — profile/master-profile.md (Notes / voice for tone; experience for STAR stories).
Optional company brief — companies/<slug>.json from a prior /research-company run. If present, use it heavily; if absent, run a thin sweep here (≠ a full research pass).
Optional round — --round screen | technical | hiring-manager | panel | final. If omitted, generate questions across the whole likely loop.

Method

Region pack. Read knowledge/regions/<code>.md — US: behavioral often STAR-formal; IN: more credential-led at large MNCs. Note tone adjustments.
Hydrate the company. If companies/<slug>.json exists, harvest:
- jobSummary.dayToDayResponsibilities → likely "what would your first 30/60/90 look like" / "walk me through how you'd ..." Qs.
- news.recentNews → 1–2 "saw the X announcement — how would you think about Y" Qs.
- red_flags → honest concerns the user should diplomatically raise back.
If absent, run a thin sweep: 3–5 WebSearch hits about the role + product + any recent news; no Reddit / Blind pass (that's research-company's job, not this one).
Identify the loop. Map target.tracks to the canonical rounds from ../mock-interview/reference/report-schema.md:
- software → coding · system-design · behavioral
- product → product-sense · execution · strategy · behavioral
- design → portfolio-review · whiteboard/app-critique · behavioral
- data → coding/SQL · ML/stats · case · behavioral
- quant → math-stats · research · coding · behavioral
- finance → technical · case · fit
- marketing → case · craft · behavioral
- founders-office → case · ops · behavioral
- sales → discovery/role-play · pitch/demo · behavioral
- operations → case · execution · behavioral
- customer-success → scenario/role-play · case · behavioral
- content → portfolio/clips-review · writing-exercise · behavioral
- hr → case · craft · behavioral

Read the full file on GitHub · 118 lines

Files

What ships with it

1 file 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.

reference/prep-rubric.md 2.3 KB

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.

2mo ago First seen · 118 lines · 67 tokens per session scan A 14199e6a3707

Subscribe to this mod's changes

prep-interview is a skill published in the GitHub repository jain777/jobclaw-skills (7 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 1,403 once invoked, about $0.0003 per session on Opus 5.5. 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.

prep-interview next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
prep-interviewjain777/jobclaw-skills	Skill	7	5mo ago	MIT	A	67	Claude Code
imagegenopenai/codex	Skill	135k	today	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	100k	today	Apache-2.0	A	19	Claude Code
browser-automationopenclaw/openclaw	Skill	394k	today	MIT	A	36	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	316k	7d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code

prep-interview

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

prep-interview

Inputs

Method

What ships with it

What this file has done since we first saw it

prep-interview compared with skills from other repositories