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 commands/madebytokens/claude-code-plugins-madebytokens/interview-prepgit clone --depth 1 https://github.com/MadeByTokens/claude-code-plugins-madebytokensWrote 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/claude-code-plugins-madebytokens/interview-prep)<a href="https://agentmods.dev/commands/madebytokens/claude-code-plugins-madebytokens/interview-prep"><img src="https://agentmods.dev/badge/commands/madebytokens/claude-code-plugins-madebytokens/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 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.
This is a copy
100% identical to interview-prep — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 5d ago First seen · 210 lines · 7 tokens per session scan A 90739aa71c10
interview-prep is a command published in the GitHub repository MadeByTokens/claude-code-plugins-madebytokens (2 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. It is 100% identical to interview-prep, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.