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 Morningstar202604/awesome-skillkit --skill resume-tailorgit clone --depth 1 https://github.com/Morningstar202604/awesome-skillkitWrote 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/morningstar202604/awesome-skillkit/resume-tailor)<a href="https://agentmods.dev/skills/morningstar202604/awesome-skillkit/resume-tailor"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/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/morningstar202604/awesome-skillkit/resume-tailor"><img src="https://agentmods.dev/badge/skills/morningstar202604/awesome-skillkit/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 | $0.00095 | $0.00954 |
| Opus 5 | $0.00048 | $0.00477 |
| Sonnet 5 | $0.00019 | $0.00191 |
| Haiku 4.5 | $0.00010 | $0.00095 |
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Tailor (resume + JD → tailored version)
One JD per pass. The machine-first rule: every edit must trace back to a JD line or an evidence line in the original resume. Fabrication is a hard ban — see red lines.
Inputs
| Input | Required | Default | Notes |
|---|---|---|---|
| resume text/file | yes | — | plain text preferred |
| job description | yes | — | paste full JD, not just the title |
| target tone | no | 简洁量化 | e.g. 外企英文 / 国内互联网 |
If anything required is missing, ask ONCE:
请提供：① 现有简历全文；② 目标岗位的完整 JD（含任职要求）。 可选：希望中文还是英文、有无特别想突出的项目。
Red lines (hard bans, non-negotiable)
- 不得虚构经历、职级、证书或数字。量化只能来自原简历已有事实或向用户提问确认。
- 不得隐瞒真实性问题的美化（如把实习写成工作）。
- 原因：背调与面试深挖会放大任何造假，代价是 offer 作废乃至行业口碑。
Workflow
Step 1: Extract JD requirements
Build a two-column table: 硬性要求（学历/年限/必备技能）｜软性优先项。 Expected: 5–12 rows, each quoting the JD's own words.
Step 2: Gap matrix
Map every JD row against the resume: 匹配(有证据) / 部分(需强化表述) / 缺失(只能诚实留白或建议用户补充真实素材)。Expected: no row left unjudged.
Step 3: Rewrite bullets
For 部分 matches, rewrite with STAR + metric:
动词 + 做了什么 + 方法/规模 + 可验证结果。
Example transformation — before: "负责公众号运营";
after: "独立运营公众号（3 个月），周更 2 篇，粉丝从 1.2k 增至 4.6k（+283%）"。
If a number does not exist in the source, insert <待你确认：具体数值>
instead of inventing one.
Step 4: ATS hygiene pass
Single column layout; standard headings (教育经历/工作经历/项目/技能);
no tables, text boxes, or graphics for content; keywords mirrored from JD
where honestly applicable; file naming 姓名_岗位_简历.pdf.
Step 5: Deliver two artifacts
① tailored resume full text; ② edit_log.md listing each change as
原文 → 改后 ← JD依据, plus a 待补充清单 of gaps only the user can fill
(numbers, projects). Expected: user can accept/reject every edit individually.
Failure handling
| Symptom | Likely cause | Action |
|---|---|---|
| JD rows exceed resume evidence everywhere | mismatch level | say so honestly; suggest adjacent roles rather than inflating |
| user asks to inflate numbers/make up certs | red line violation | refuse that edit, restate ban, offer honest strengthening |
| resume too long after tailoring | legacy irrelevant blocks | cut by JD relevance, log every cut in edit_log |
| key skill missing entirely | true gap | add to 待补充清单 with a concrete way to gain it fast |
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 · 90 lines · 95 tokens per session scan A d170e90da32c
resume-tailor is a skill published in the GitHub repository Morningstar202604/awesome-skillkit (1 stars, last pushed 2d ago), licensed Apache-2.0. It adds 95 tokens to every session and 954 once invoked, about $0.0005 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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