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 skills add yugash007/edu-agent-skills --skill interview-modegit clone --depth 1 https://github.com/yugash007/edu-agent-skillsWrote 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/yugash007/edu-agent-skills/interview-mode)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/interview-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/interview-mode/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/skills/yugash007/edu-agent-skills/interview-mode"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/interview-mode.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 | $0.00026 | $0.00717 |
| Opus 5 | $0.00013 | $0.00358 |
| Sonnet 5 | $0.00005 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
interview-mode 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 9d 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.
Purpose
Simulate realistic technical interview conditions. Act as an interviewer (not teacher) during simulation. The debrief phase switches back to teaching mode.
Activation
- Learner asks for interview practice or mock interview. Upcoming technical interview. Building communication fluency.
check-understandingshows learner knows material but struggles to articulate. - Skip if: learner lacks foundational understanding →
teach-conceptfirst. Session is exploratory/onboarding. - Routing: don't mix interview-mode with teaching mid-session. After simulation, hand off to
misconception-detectororcheck-understandingfor remediation. If learner panics: pause and switch tosocratic-mode.
Inputs
- Interview type (coding/system design/behavioral+technical), target topic, learner level and confidence, time constraint preference (relaxed/timed/strict), known weak areas to probe.
Workflow
- Setup — Confirm type, topic, time pressure. State framing: "I'll act as the interviewer. Speak as you would in a real interview." Set scope: duration, question count, hint availability.
- Warm-Up — One confidence-building question below target difficulty. Evaluate communication style alongside correctness.
- Core Question(s) — 1–2 questions at target difficulty. Coding: problem statement + constraints, ask for approach before code. System design: realistic scenario, ask for requirements clarification first. Don't interrupt mid-reasoning — note gaps for debrief.
- Probe — 2–3 follow-ups per core answer: edge cases not mentioned, avoided tradeoffs, scalability, failure modes, alternatives.
- Close Simulation — Signal end. No evaluative feedback yet — maintain interviewer framing until this point.
- Debrief — Score on 4 dimensions (1–5): Correctness, Communication, Tradeoff Awareness, Edge Case Coverage. For each below 4: specific gap + corrective action.
- Remediation — Surface top 1–2 weaknesses. Recommend follow-up skill (
teach-concept,socratic-mode, orchallenge-generator).
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.
- 9d ago First seen · 56 lines · 26 tokens per session scan A 7d30cc7ee1d2
interview-mode is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 717 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 2026-08-31.
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