Borrowing it
Nothing to install: this file belongs to cypggs/ai-job-search-cn. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cypggs/ai-job-search-cn/master/.claude/commands/interview.mdgit clone --depth 1 https://github.com/cypggs/ai-job-search-cnWrote 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.
[](https://agentmods.dev/commands/cypggs/ai-job-search-cn/interview)<a href="https://agentmods.dev/commands/cypggs/ai-job-search-cn/interview"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/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.
<a href="https://agentmods.dev/commands/cypggs/ai-job-search-cn/interview"><img src="https://agentmods.dev/badge/commands/cypggs/ai-job-search-cn/interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00000 | $0.02428 |
| Opus 5.5 | $0.00000 | $0.00971 |
| Sonnet 5.5 | $0.00000 | $0.00486 |
| Haiku 4.5 | $0.00000 | $0.00243 |
Grade A, and why
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 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.
This is a copy
94% identical to interview — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/interview - Prepare for an Interview on a Tracked Application
You are preparing the user for a real, scheduled interview on one of their applications. The frameworks for this already exist - 07-interview-prep.md (STAR examples, tough questions, questions to ask, roleplay protocol) and the Company Research Checklist in 04-job-evaluation.md - and the /outcome archive records which stage the user is at and what earlier stages surfaced. This command wires them together into a stage-specific prep pack and an optional mock interview.
/apply optimizes what the company reads; /interview optimizes what the company hears. The bridge between them is consistency: the interviewer has read the submitted CV and cover letter, so everything prepared here must match what those documents claim.
Follow these steps in order.
Step 0: Parse Input
$ARGUMENTS may contain a company name (optionally with a role), e.g. /interview acme.
- With an argument: match against
job_search_tracker.csvrows (case-insensitive on company, then role). One match → proceed. Several → list and ask. None → this application isn't tracked; suggest/outcome <company>to register it first, or accept the posting and role details directly if the user wants to prep anyway. - Without an argument: list tracker rows whose status suggests a live process — an open status per the Tracker status vocabulary in
/outcome(interview,offer, or recentlyapplied;draftedis open but nothing was sent, so it never qualifies) — and ask which one. If the tracker is empty, ask for the company, role, and posting.
v1 preps for a specific application. Generic no-target practice is out of scope - if asked, prep against a real tracked application instead.
Step 1: Load the Application Context
- The archive (started by
/apply, maintained by/outcome): derive<company>_<role>by the Subfolder naming rule indocuments/README.md, then usedocuments/applications/<company>_<role>/.job_posting.md- the exact posting the user applied tocv_draft.texandcover_letter.tex- what was actually submitted. These are what the interviewer read; every talking point must be consistent with their claims.outcome.md- the stage reached so far and any recorded feedback from earlier stages. Feedback from stage N is the highest-value input for stage N+1 prep.
- Fallbacks (the application may predate
/outcome): posting via WebFetch on the tracker row'ssourceURL, or ask the user to paste it; CV viacv/main_<company>_<role>.*and cover letter viacover_letters/cover_<company>_<role>.*, deriving<company>_<role>by the Subfolder naming rule indocuments/README.md. Never widen those globs to the company alone: with two roles at one company it would prep you from the sibling role's documents. State plainly which context is missing rather than guessing - and suggest/outcome <company>to build the archive for next time. - Ask the user what this interview is (skip anything
outcome.mdalready records): stage (phone screen / technical / case / final round), date, format (phone, video, onsite), and who is interviewing (names and titles, if known). - Read the frameworks once - do not re-read them in later steps:
.claude/skills/job-application-assistant/07-interview-prep.md.claude/skills/job-application-assistant/01-candidate-profile.md.claude/skills/job-application-assistant/02-behavioral-profile.md.claude/skills/job-application-assistant/04-job-evaluation.md
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.
- 11d ago First seen · 112 lines · 0 tokens per session scan A 6db14af66791
interview is a command published in the GitHub repository cypggs/ai-job-search-cn (90 stars, last pushed 12d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,428 tokens. A static security scan graded it A with 0 findings. It is 94% identical to interview, differing in 4 lines, and is treated as a copy.
Other commands, from other repositories
ws
Word summary — anything told at N levels, from N words down to 1 word, for anything: a topic ("russian literature"), a URL, YouTube link, file, pasted text, a question, or "this". A leading number sets N (5 by default). Shortcut for the Fabric skill's WordSummary intent.
cancel-ralph
Cancel active Ralph Loop.
engage.actions
Execute Phase 7 - Actions on Objectives and Goal Achievement.
r
Resume the last handoff and carry on with the outstanding work.
focus
You are an ADHD focus assistant. Help the user commit to ONE task and block mental distractions.
stuck
You are an ADHD-friendly unblocking assistant. Help users get unstuck when they don't know what to do.