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.
npx agentmods add skills/surfmind-space/awesome-surfmind/interview-prepnpx skills add surfmind-space/awesome-surfmind --skill interview-prepgit clone --depth 1 https://github.com/surfmind-space/awesome-surfmindWrote 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/skills/surfmind-space/awesome-surfmind/interview-prep)<a href="https://agentmods.dev/skills/surfmind-space/awesome-surfmind/interview-prep"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/interview-prep.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.00957 |
| Opus 5 | $0.00032 | $0.00478 |
| Sonnet 5 | $0.00013 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00096 |
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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Prep
Prepare the user for an interview at a specific company and role using their selected text, the visible page, a pasted job description, or details they provide. Work only from sourced facts and the user's own background; never attribute an interview question to a source that didn't report it, and label job-description-derived questions as inferred. Use web search or browser tools for current interview-process, product, compensation, and culture signals when available. Adapted from santifer/career-ops interview-prep and deep modes.
- Confirm the company and role. If either is missing, ask before producing a full prep pack.
- Map the likely rounds and their audiences — recruiter screen, hiring manager, peer or technical panel, and mixed panels. See references/audience-playbook.md for each audience's focus and question bank. If the schedule is unknown, say so and give the likely audiences separately.
- Map likely questions to STAR or STAR+R story prompts and draft result first answer outlines from the user's real background only. See references/star-framework.md for the outline order, headline building, and gap handling. If they haven't supplied enough stories, flag the missing topics and ask for one detail at a time.
- Suggest technical or case prep grounded in the job description, company materials, or reported process, and include questions to ask back that reference the role, team, product, or company.
- Coach disclosure per audience: lead with what lands for each interviewer while staying consistent on comp, timeline, and motivation. The per audience strategy, compensation deflection phrasing, and honesty guardrails live in references/audience-playbook.md.
Return concise headings, including these when they fit: Process overview, Audience map, Likely questions and answer angles, Story map, Technical or case prep, Company signals, Questions to ask, Prep priorities. Cite sources, say "unknown" when evidence is thin, and never invent questions, ratings, compensation, or interview statistics. Don't tell the user to lie about compensation, competing offers, authorization, notice period, or availability. If they give an interview date, add a short countdown based prep plan.
What ships with it
2 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.
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.
- 5d ago First seen · 56 lines · 64 tokens per session scan A 93fa92931aa1
interview-prep is a skill published in the GitHub repository surfmind-space/awesome-surfmind (5 stars, last pushed 1mo ago), licensed MIT. It adds 64 tokens to every session and 957 once invoked, about $0.0003 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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