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 skills add tmargolis/career-navigator --skill prep-interviewgit clone --depth 1 https://github.com/tmargolis/career-navigatorWrote 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/tmargolis/career-navigator/prep-interview)<a href="https://agentmods.dev/skills/tmargolis/career-navigator/prep-interview"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/prep-interview/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tmargolis/career-navigator/prep-interview"><img src="https://agentmods.dev/badge/skills/tmargolis/career-navigator/prep-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00104 | $0.01211 |
| Opus 5.5 | $0.00042 | $0.00484 |
| Sonnet 5.5 | $0.00021 | $0.00242 |
| Haiku 4.5 | $0.00010 | $0.00121 |
Grade A, and why
prep-interview 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 28d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow
1. Resolve {user_dir} and gate
Application data uses the split layout defined in references/tracker-schema.md — read it before any read or write.
Require CareerNavigator/profile.md, ExperienceLibrary.json, and tracker.json. If missing:
Run
/career-navigator:launchfirst to initialize Career Navigator.
Story corpus gate:
- Preferred source:
CareerNavigator/StoryCorpus.json. - If missing, run or suggest
mine-storiesfirst. - If user wants to continue immediately, proceed with
ExperienceLibrary.jsonfallback and clearly label lower story-specific confidence.
2. Identify target
From user message or tracker:
- Prefer
application_idif given; else match company + role againsttracker.jsonapplications[]. If anapplication_idmatches no row'sid, check each row'sprevious_idsbefore declaring no match — prep briefs saved under earlier ids still resolve. - Keep the matched row's
detail_file,contacts_file, andapplicationlabel; the write in step 6 needs them. - Pull prior stage history and notes from the row's
detail_file(CareerNavigator/applications/<application_id>.json) —tracker.jsonholds onlylatest_stageandlatest_stage_date. Load thecontacts_fileonly if interviewer background is needed, filtering itscontacts[]to entries whoseapplicationequals the row'sapplication. - If ambiguous, ask one short disambiguation question.
Collect if missing:
interview_stage:recruiter|hiring_manager|technical|panel|executive|final(default: infer from wording; recruiter call / phone screen →recruiter).- Optional: interviewer name/title, interview date, JD text or paste.
3. Optional dictated input
Text-only. If the user pastes a transcript from host-level speech-to-text instead of typing, pass it as user_audio_transcript in the handoff to interview-coach.
4. Retrieve targeted stories
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.
- 28d ago Changed · +1 lines · +9 tokens per session 3d0ea959f6fb
- 1mo ago First seen · 104 lines · 95 tokens per session scan A 6777be8affd5
prep-interview is a skill published in the GitHub repository tmargolis/career-navigator (16 stars, last pushed 28d ago), licensed Apache-2.0. It adds 104 tokens to every session and 1,211 once invoked, about $0.0004 per session on Opus 5.5. 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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