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 duthaho/skillhub --skill jobfitgit clone --depth 1 https://github.com/duthaho/skillhubWrote 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/duthaho/skillhub/jobfit)<a href="https://agentmods.dev/skills/duthaho/skillhub/jobfit"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/jobfit/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/duthaho/skillhub/jobfit"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/jobfit.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 | $0.00178 | $0.03930 |
| Opus 5 | $0.00089 | $0.01965 |
| Sonnet 5 | $0.00036 | $0.00786 |
| Haiku 4.5 | $0.00018 | $0.00393 |
Grade A, and why
jobfit 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 10d 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 — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
jobfit — job-fit evaluation & tailoring
/jobfit [url(s) | pasted JD(s)] [profile path/URL or plain-language steering]
Answer one question per role: is this worth applying to, and if so, how do I tailor for it? You ground every judgment in the actual posting + light research, score against the user's real profile, and rank by fit.
Two modes (auto-detect)
- EVALUATE — the user provides one or more job URLs/JDs. Score and tailor those.
- DISCOVER — the user provides no jobs (e.g. "find jobs that fit my CV"). Source matching openings from the profile, let the user pick, then evaluate those.
If jobs are given → EVALUATE. If none are given → DISCOVER. If the user gives jobs but also says "and find more like these," do both: evaluate the given ones and run Step 0b.
Source-of-truth boundary (read first)
User-facing content and scores draw only from:
- the actual job posting(s) the user provides, points to, or that DISCOVER surfaces,
- the user's profile (see Step 0), and
- what the user states in this session.
Keywords get reformulated, never fabricated. Never invent experience, skills, metrics, titles, or achievements the profile doesn't support. Never pull "facts" about the user from memory or cross-session inference. Using a tool is not building it — never claim authorship of systems the profile only shows the user used. Tailoring = surfacing and rephrasing what's genuinely there to mirror the JD's language.
The tracker — memory across runs
Job search is a multi-week process; this skill must not start from zero every day.
Maintain out/jobfit/tracker.md in the working directory (gitignored — personal
data). One row per role ever evaluated:
| Date | Role @ Company | URL | Score | Verdict | Status | Notes |
|------|----------------|-----|-------|---------|--------|-------|
| 2026-07-04 | Senior BE @ Acme | <url> | 4.2/5 (B+) | APPLY | new | |
Status is the user's to update (new / applied / interviewing / offer /
rejected / passed) — never change it yourself, but read it.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 291 lines · 178 tokens per session scan A 23ac906318ef
jobfit is a skill published in the GitHub repository duthaho/skillhub (9 stars, last pushed 2d ago), licensed MIT. It adds 178 tokens to every session and 3,930 once invoked, about $0.0009 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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