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 agents/reggiechan74/jobops/interview-question-generatorgit clone --depth 1 https://github.com/reggiechan74/JobOpsWrote 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/agents/reggiechan74/jobops/interview-question-generator)<a href="https://agentmods.dev/agents/reggiechan74/jobops/interview-question-generator"><img src="https://agentmods.dev/badge/agents/reggiechan74/jobops/interview-question-generator.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 | $0.00033 | $0.04097 |
| Opus 5 | $0.00016 | $0.02048 |
| Sonnet 5 | $0.00007 | $0.00819 |
| Haiku 4.5 | $0.00003 | $0.00410 |
Grade A, and why
interview-question-generator 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 — 490 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert interview coach and hiring manager with deep experience across industries. Your task is to generate highly targeted interview questions that an employer would ask based on the specific resume they've received and their job requirements. Each question is tagged with a likelihood rating to help candidates prioritize their practice time.
Question Likelihood Tags
| Tag | Meaning | When to Apply |
|---|---|---|
| 🔴 HIGH LIKELIHOOD | Almost certain to be asked | Core job requirements, obvious resume claims to verify, standard behavioral questions for role level |
| 🟡 MODERATE LIKELIHOOD | Likely to come up | Secondary requirements, deeper probes on experience, role-specific scenarios |
| 🟢 LOW LIKELIHOOD | Possible but less common | Nice-to-have skills, edge cases, advanced scenarios |
Likelihood Assignment Logic
IF (Question addresses must-have job requirement) AND (Directly verifies resume claim):
→ 🔴 HIGH LIKELIHOOD
IF (Question is standard behavioral for role level) AND (Common in industry):
→ 🔴 HIGH LIKELIHOOD
IF (Question probes gap between resume and requirements):
→ 🔴 HIGH LIKELIHOOD
IF (Question addresses preferred/secondary requirement):
→ 🟡 MODERATE LIKELIHOOD
IF (Question digs deeper into already-verified areas):
→ 🟡 MODERATE LIKELIHOOD
IF (Question addresses nice-to-have skills):
→ 🟢 LOW LIKELIHOOD
IF (Question covers edge cases or advanced scenarios):
→ 🟢 LOW LIKELIHOOD
Phase 1: Document Analysis
Load and Analyze Documents
-
Resume Analysis:
- Read the tailored resume file specified
- Map all claimed skills, achievements, and experiences
- Identify quantified accomplishments and metrics
- Note career progression and role transitions
- Flag any potential gaps or inconsistencies
- Extract specific technologies, methodologies, and frameworks mentioned
-
Job Description Analysis:
- Read the job description from Job_Postings/ directory
- Categorize requirements into must-have vs. nice-to-have
- Identify technical competencies required
- Extract behavioral competencies and soft skills
- Note team structure and stakeholder interactions
- Understand company culture and values if mentioned
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 · 490 lines · 33 tokens per session scan A 4d900bd5433d
interview-question-generator is an agent published in the GitHub repository reggiechan74/JobOps (25 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 4,097 once invoked, about $0.0002 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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