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 AkbarDevop/ai-job-agent --skill job-interviewgit clone --depth 1 https://github.com/AkbarDevop/ai-job-agentWrote 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/akbardevop/ai-job-agent/job-interview)<a href="https://agentmods.dev/skills/akbardevop/ai-job-agent/job-interview"><img src="https://agentmods.dev/badge/skills/akbardevop/ai-job-agent/job-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/akbardevop/ai-job-agent/job-interview"><img src="https://agentmods.dev/badge/skills/akbardevop/ai-job-agent/job-interview.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 | $0.00163 | $0.02926 |
| Opus 5 | $0.00081 | $0.01463 |
| Sonnet 5 | $0.00033 | $0.00585 |
| Haiku 4.5 | $0.00016 | $0.00293 |
Grade A, and why
job-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 11d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Interview Prep
A skill (not an API call) for interview preparation. Claude — this agent — does the research and the STAR-answer generation, grounded in the candidate's real projects from config/candidate-profile.md and the real application history from application-tracker.csv and outreach-log.csv.
The output is a single prep doc the candidate can skim on the way to the interview. Specific, not generic. If you find yourself writing "demonstrate strong communication skills" — stop and rewrite with a real number from a real project.
Repo location
$AI_JOB_AGENT_ROOT → ~/.claude/skills/ai-job-agent/ → REPO_PATH marker file → ~/ai-job-agent/.
Prerequisites
Before drafting anything:
-
config/candidate-profile.mdexists (required — every STAR story must come from real projects listed here). If not, tell the user to run/job-setupfirst and stop. -
application-tracker.csvexists at$AI_JOB_AGENT_ROOT/application-tracker.csv. If not, tell the user to run/job-setup(or the skill can still proceed with company research only, but warn the user that the role context will be guessed). -
outreach-log.csvexists at$AI_JOB_AGENT_ROOT/outreach-log.csv. Optional — if missing, skip the outreach-history lookup.
Workflow
Step 1 — Resolve the target
Parse $ARGUMENTS. It might be:
- A company name (e.g. "GFT Infrastructure")
- A role title ("Power Systems Intern at GFT")
- A tracker key (the
keycolumn ofapplication-tracker.csv) - Freeform ("the one I applied to last week" — ask which one if ambiguous)
If only a company is given and the tracker has multiple rows for it, list them and ask the user which role this interview is for.
Step 2 — Pull internal context
Run all three reads in parallel:
Read $AI_JOB_AGENT_ROOT/config/candidate-profile.md— full profile (PAAL CV pipeline, ARC-AGI lab work, ai-job-agent OSS, GFT signed offer, F-1 status, resume variants, key projects with metrics, graduation date, target roles).
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.
- 11d ago First seen · 201 lines · 163 tokens per session scan A cf1aac96e669
job-interview is a skill published in the GitHub repository AkbarDevop/ai-job-agent (54 stars, last pushed 4mo ago), licensed MIT. It adds 163 tokens to every session and 2,926 once invoked, about $0.0008 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 2026-08-30.
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