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.
git clone --depth 1 https://github.com/MadeByTokens/resume-helperWrote 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/madebytokens/resume-helper/interview-prep)<a href="https://agentmods.dev/commands/madebytokens/resume-helper/interview-prep"><img src="https://agentmods.dev/badge/commands/madebytokens/resume-helper/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 5.1 | $0.00007 | $0.01136 |
| Opus 5 | $0.00003 | $0.00568 |
| Sonnet 5 | $0.00001 | $0.00227 |
| Haiku 4.5 | $0.00001 | $0.00114 |
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 7d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- interview-prep — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview Prep Command
Generate an interview preparation document based on a resume.
Usage
/resume-helper:interview-prep "path/to/resume.md"
/resume-helper:interview-prep # Uses resume from last completed loop
Instructions
Step 1: Load Resume
- If path provided, read the resume file
- If no path provided:
- Read
working/state.json - If state exists and has completed, read
working/writer/output.md - If no state or no resume, ask user for resume path
- Read
Step 2: Analyze Resume
Invoke the Interviewer agent to generate likely interview questions:
You are the Interviewer agent. Analyze this resume and generate comprehensive interview questions.
## Resume
<resume content>
## Task
Generate interview questions that a hiring manager would likely ask based on this resume. For each claim or achievement, create questions that would verify or explore it deeper.
Organize questions by category:
1. Experience verification questions
2. Technical deep-dive questions
3. Behavioral/situational questions
4. Questions about gaps or transitions
5. Questions the candidate should ask the interviewer
Step 3: Generate Prep Document
Create working/output/interview_prep.md with the following structure:
# Interview Preparation Guide
Generated: [date]
Resume: [filename]
## Quick Reference
### Your Key Talking Points
- [Top 3-5 achievements to emphasize]
### Numbers to Remember
| Metric | Value | Context |
|--------|-------|---------|
| Team size | X | Project Y |
| Impact | X% | Achievement Z |
| ... | ... | ... |
## Likely Questions & Suggested Responses
### Experience Verification
**Q: [Question about specific role/achievement]**
*Why they're asking:* [What they want to verify]
*Suggested response framework:*
- Situation: [Context to set]
- Your role: [Clarify your specific contribution]
- Actions: [What you did]
- Result: [Quantified outcome]
*Key points to hit:*
- [Specific detail 1]
- [Specific detail 2]
---
### Technical Deep-Dives
**Q: [Technical question based on resume]**
*Why they're asking:* [What they want to assess]
*Suggested approach:*
- [How to structure your answer]
- [Technical details to include]
- [What to avoid]
---
### Behavioral Questions
**Q: "Tell me about a time when..." [scenario from resume]**
*STAR Response:*
- **Situation:** [Set the scene]
- **Task:** [Your responsibility]
- **Action:** [What you did - be specific]
- **Result:** [Quantified outcome]
---
### Questions About Gaps/Transitions
**Q: [Question about career gap or transition]**
*Suggested response:*
- [Honest, positive framing]
- [What you learned/did during that time]
- [How it makes you stronger for this role]
---
## Red Flags to Address Proactively
If these come up, here's how to handle them:
1. **[Potential concern]**
- Address by: [suggested approach]
2. **[Potential concern]**
- Address by: [suggested approach]
---
## Questions YOU Should Ask
Demonstrate engagement and evaluate fit:
### About the Role
- "What does success look like in the first 90 days?"
- "What are the biggest challenges facing the team right now?"
### About the Team
- "How is the team structured?"
- "What's the collaboration style?"
### About Growth
- "What opportunities for growth exist?"
- "How do you support professional development?"
### About the Company
- "What's the company's biggest priority this year?"
- "How would you describe the culture?"
---
## Final Checklist
Before the interview:
- [ ] Review all numbers and metrics in your resume
- [ ] Practice STAR responses for top 3 achievements
- [ ] Prepare 3-5 questions to ask
- [ ] Research the company and interviewer
- [ ] Review the job description again
- [ ] Prepare your "Tell me about yourself" (2-min version)
---
*Good luck! Remember: The best interviews feel like conversations, not interrogations.*
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.
- 7d ago First seen · 210 lines · 7 tokens per session scan A 90739aa71c10
interview-prep is a command published in the GitHub repository MadeByTokens/resume-helper (7 stars, last pushed 7mo ago), licensed MIT. It adds 7 tokens to every session and 1,136 once invoked, about $0.0000 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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