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/muggl3mind/career-managerWrote 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/muggl3mind/career-manager/interview-prep)<a href="https://agentmods.dev/commands/muggl3mind/career-manager/interview-prep"><img src="https://agentmods.dev/badge/commands/muggl3mind/career-manager/interview-prep/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/commands/muggl3mind/career-manager/interview-prep"><img src="https://agentmods.dev/badge/commands/muggl3mind/career-manager/interview-prep.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.00018 |
| Opus 5.5 | $0.00000 | $0.00007 |
| Sonnet 5.5 | $0.00000 | $0.00004 |
| Haiku 4.5 | $0.00000 | $0.00002 |
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 17d 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.
What it actually says
Read interview-prep/SKILL.md. Generate interview prep for: $ARGUMENTS
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.
- 17d ago First seen · 1 lines · 0 tokens per session scan A 904a9882d0b4
interview-prep is a command published in the GitHub repository muggl3mind/career-manager (24 stars, last pushed 16d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 18 tokens. 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 2026-10-01.
Other commands, from other repositories
upskill
Find the skill gaps that keep recurring across your applications and turn them into a learning plan.
mock-interview
Rehearse a realistic mock interview, one question at a time.
interview
Start a mock interview session with Interviewer Bot.
start-10-1
A guided lesson on setting up clasp, a command-line tool for managing Google Apps Script projects, and connecting it to Google’s Apps Script API.
prep-role
Deep prep for a specific role's interview process. The user wants to know what to expect, how to win, and what kills candidates at their level.
setup-vercel.es
Command "setup-vercel.es" from minicoohei/ai-agent-camp, covering configuración de vercel cli, step 0: verificar progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: crear cuenta de vercel.