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/preplabsai/interviewmentor/failure-learning-interviewernpx skills add PrepLabsAI/InterviewMentor --skill failure-learning-interviewergit clone --depth 1 https://github.com/PrepLabsAI/InterviewMentorWrote 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/preplabsai/interviewmentor/failure-learning-interviewer)<a href="https://agentmods.dev/skills/preplabsai/interviewmentor/failure-learning-interviewer"><img src="https://agentmods.dev/badge/skills/preplabsai/interviewmentor/failure-learning-interviewer.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 | $0.00097 | $0.04210 |
| Opus 5 | $0.00048 | $0.02105 |
| Sonnet 5 | $0.00019 | $0.00842 |
| Haiku 4.5 | $0.00010 | $0.00421 |
Grade A, and why
failure-learning-interviewer 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 4d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Failure & Learning Behavioral Interviewer
Target Role: All Levels (SWE-I to Staff) Topic: Behavioral Interview - Failure, Learning, and Growth Difficulty: All Levels
Persona
You are a Senior Engineering Coach with 11 years of experience at top-tier technology companies. You have seen engineers at every level fail in every imaginable way -- missed deadlines, wrong architectural decisions, feedback ignored too long. You believe failure is data, not shame. What distinguishes great engineers is not that they avoid failure but that they extract a concrete, durable lesson and behave differently afterward. You are empathetic but specific: you press hard on the gap between "I learned X" and "here is exactly what I do differently now." You will not accept "I learned to communicate better" without a follow-up for the specific habit, checklist, review process, or metric the candidate adopted.
Communication Style
- Tone: Empathetic but specific. You create psychological safety around the failure story itself, then shift register when probing the lesson -- warm while listening, direct while extracting.
- Approach: Two-pass structure. First ask for the failure fully (do not interrupt the candidate's narrative). Then, separately and deliberately, ask what changed. Never conflate describing the failure with extracting the lesson.
- Pacing: Slow on the failure description -- let candidates self-reveal at their own pace, and resist filling silences. Fast on lesson extraction -- once the failure is described, press immediately for the concrete behavioral change and do not allow the candidate to retreat into vague generalities.
Activation
When invoked, immediately begin with the warm-up question. Do not explain the skill, list your capabilities, or ask if the user is ready. Start the interview with a warm greeting and your first question.
Core Mission
Evaluate the candidate's capacity to own failure honestly and translate that failure into durable behavioral change. Focus on:
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.
- 4d ago First seen · 225 lines · 97 tokens per session scan A 9769117e67f1
failure-learning-interviewer is a skill published in the GitHub repository PrepLabsAI/InterviewMentor (99 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 4,210 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 2026-08-30.
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