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 landedjobs/ai-job-hunt-os --skill resume-tailorgit clone --depth 1 https://github.com/landedjobs/ai-job-hunt-osWrote 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/landedjobs/ai-job-hunt-os/resume-tailor)<a href="https://agentmods.dev/skills/landedjobs/ai-job-hunt-os/resume-tailor"><img src="https://agentmods.dev/badge/skills/landedjobs/ai-job-hunt-os/resume-tailor/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/landedjobs/ai-job-hunt-os/resume-tailor"><img src="https://agentmods.dev/badge/skills/landedjobs/ai-job-hunt-os/resume-tailor.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 | $0.00049 | $0.00901 |
| Opus 5 | $0.00024 | $0.00451 |
| Sonnet 5 | $0.00010 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
resume-tailor 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 12d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Tailor
Rewrite resumes to match a specific job description without inventing anything. A resume must work in two modes: a fast human scan and structured retrieval inside an applicant-tracking system. Exact scan-time and ATS-adoption statistics vary by study and population, so do not repeat them as universal facts. The job is to make the resume parseable, make the first screen land, and make every line survive a follow-up question.
Before starting, collect
- The current resume.
- The full job description.
- Ask: "Tell me 2-3 things you actually did in that role that aren't on the resume, especially anything with a number attached (scale, latency, cost, users, revenue, time saved)." Most resumes undersell; the raw material lives in the person's head.
Do not generate anything until you have at least items 1 and 2.
Process
Step 1: Extract what the JD screens for
List in order of emphasis: hard requirements (named tools, domains; weight what appears early or repeats), the problem behind the role (visible in the responsibilities), and the JD's exact vocabulary. If they say "evals," never write "quality assessment."
Step 2: Map evidence into three buckets
- Direct hit: they did exactly this. Leads the relevant section.
- Adjacent: same muscle, different context. Reframe honestly.
- Gap: no evidence. Flag it; never fill it with wording. Gaps are handled in the cover letter or interview, not by lying.
Step 3: Rewrite
- Design the top third for a fast scan: target-matching title language, strongest company or project, domain fit, and proof links must be visible before any dense detail.
- Every bullet becomes evidence, not adjectives: use the XYZ pattern, "accomplished X, measured by Y, by doing Z." If Y is missing, ask for it; estimates marked with "~" are fine, invented numbers are not.
- Strip generic AI cadence: Phrases like "leveraged cutting-edge AI" get cut unless tied to a model, data, eval, or outcome the user actually touched. Tailoring must add relevant evidence, not synthetic polish.
- Keep it database-readable: standard section headers, real dates, exact role nouns from the JD, no tables or graphics in the text flow.
- For AI roles, surface proof links: GitHub, papers, demos, launches. Ask whether the linked repo shows tests, a clear README, and something deployed; a weak public repo is a risk, not an automatic plus.
- Same length or shorter than the original. Tailoring adds relevance, not volume.
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.
- 12d ago First seen · 51 lines · 49 tokens per session scan A 61d80c826f53
resume-tailor is a skill published in the GitHub repository landedjobs/ai-job-hunt-os (1 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 901 once invoked, about $0.0002 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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