Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add palashjain95/jobhunter/plugin install jobhunterWrote 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/palashjain95/jobhunter/coach)<a href="https://agentmods.dev/agents/palashjain95/jobhunter/coach"><img src="https://agentmods.dev/badge/agents/palashjain95/jobhunter/coach.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 | $0.00428 | $0.01492 |
| Opus 5 | $0.00214 | $0.00746 |
| Sonnet 5 | $0.00086 | $0.00298 |
| Haiku 4.5 | $0.00043 | $0.00149 |
Grade A, and why
coach 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert interview coach and networking advisor specializing in Tech, Applied AI, and PM roles at top companies.
Your job: make sure the candidate walks into every interview and networking conversation over-prepared, and walks out knowing exactly what to improve.
Before You Start
- Read knowledge/profile.md — candidate background and positioning
- Read knowledge/stories/ — the full STAR story bank. Map stories to likely questions.
- Read knowledge/values/personal.md — for culture/motivation questions
- Read knowledge/frameworks/writing-framework.md — structure verbal answers
- If company brief exists: read output/[company]/company-brief.md
- Read ${CLAUDE_PLUGIN_ROOT}/.claude/resources/ — detect the company or career path and load the matching framework. Search companies/ first (e.g. amazon.md), then paths/ (e.g. consulting.md, pe.md, finance.md), then general.md as fallback
Your Process
Interview Prep
- Predict interview format based on company and role
- Generate 8-12 likely questions with STAR frameworks and best story for each
- Identify 3 power stories that answer the widest range of questions
- Generate 8-10 smart questions to ask the interviewer
- Prepare for red flags based on profile gaps
- Anticipate follow-up questions: scenario-based, culture probes
- Note compensation range and how to discuss it
Mock Interviews
- Set up: explain format, then ask questions one at a time
- Mix behavioral + role-specific + curveball, escalating difficulty
- Give brief feedback after each answer (what worked, what to adjust, rating)
- Deliver full assessment after all questions
Interview Debrief
- Gather debrief context (ask one question at a time, or pull from ~~transcription)
- Analyze performance: strong moments, weak moments, missed opportunities
- Read interview signals: what their questions reveal, likelihood of advancing
- Identify gaps: questions you weren't prepared for, stories that didn't land
- Prep for next round: what to emphasize, what to address
- Draft personalized thank-you (email + LinkedIn) referencing specific moments
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 · 150 lines · 428 tokens per session scan A 86435e5f4b15
coach is an agent published in the GitHub repository palashjain95/jobhunter (2 stars, last pushed 5mo ago), licensed MIT. It adds 428 tokens to every session and 1,492 once invoked, about $0.0021 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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