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 Viy1204/recruiting-copilot --skill recruit-grillgit clone --depth 1 https://github.com/Viy1204/recruiting-copilotWrote 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/viy1204/recruiting-copilot/recruit-grill)<a href="https://agentmods.dev/skills/viy1204/recruiting-copilot/recruit-grill"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/recruit-grill/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/viy1204/recruiting-copilot/recruit-grill"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/recruit-grill.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 | $0.00133 | $0.01078 |
| Opus 5 | $0.00067 | $0.00539 |
| Sonnet 5 | $0.00027 | $0.00216 |
| Haiku 4.5 | $0.00013 | $0.00108 |
Grade A, and why
recruit-grill 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.
What it actually says
recruit-grill —— 逐岗梳理真实岗位需求
把用人需求从「我要一个厉害的 XX」逼问成可执行的初筛标准。一次只梳理一个岗位。
访谈纪律（最重要）
- 一次只问一个问题。问完等回答，再问下一个。一次抛一串问题会把用户问懵。
- 每个问题都给推荐答案：基于已知信息给出"我建议是 X，因为 Y"，让用户确认或纠正，比开放题快得多。
- 能自己查到的不问：用户已有旧 JD、公司介绍、已梳理过的其他岗位（CONTEXT.md），先读再问，只问文件里没有的。
- 追问模糊词：用户说"要资深的"、"能力强的"、"最好懂 AI"，必须逼问成可判断的标准 （几年算资深？看什么信号算能力强？懂 AI 是会用工具还是能落地到工作流？是硬要求还是加分项？）。
- 沉淀术语：访谈中出现公司黑话、岗位简称、内部代号，随手写进 CONTEXT.md「术语表」，之后统一用这些词。
问题清单
按 references/question-bank.md 的顺序走（有依赖关系，别乱序）。
核心七组：岗位存在意义 → 硬门槛 → 硬底子 vs 表层 → 命脉技能与验证方式 → 排除信号 → 目标公司与搜索词 → 渠道预判。
产出（每岗四件，全部写完才算梳理完）
-
对外 JD
01-jd/<role>.md（可以直接发给候选人/挂平台的版本）。结构：# 岗位名 > 背景一句话（为什么招） ## 一、招聘 spec（设计依据，内部对齐用，发布时可裁掉） | 维度 | 结论 | ← 定位/核心存在意义/汇报线/学历年限/地点/薪资带宽 ## 二、岗位详情 | 汇报对象 | 工作地点 | 薪资 | > 💡 关于这个岗位（人话版描述） ### 岗位职责 ### 任职要求（硬性） ### 加分项 > 📌 作品集/代码/案例要求（如适用）⚠️ 对外 JD 里不写商业敏感信息和寻源策略。
-
对内笔记
01-jd/_internal/<role>.md——套_shared/templates/jd-internal.md： 硬约束一句话锚定、命脉技能与验证方式、目标公司锚点、参考简历信号、排除信号、空的关键词迭代表。 写完提醒用户：这份不外发。 -
CONTEXT.md 更新：
- 「初筛硬规则」：本次访谈确认的通用硬门槛（年龄线/学历线/地点坐班/错位处理）。已有内容冲突时向用户确认后更新，并在「已对齐决策」记一条带日期的变更。
- 「在招岗位与优先级」：本岗状态从"待梳理"改为"在招"，补优先级和文件链接。
- 「术语表」：本次新沉淀的术语。
-
初始搜索关键词：把访谈得出的搜索词写进对内笔记关键词迭代表的 R1 行（日期留空，首轮寻源后回填效果）。
什么时候重新 grill
- 寻了几轮源发现标准明显不对（全是错的人/池子里根本没这种人）→ 带着台账数据重新走一遍硬门槛和命脉部分；
- 用人经理改需求 → 增量更新，改动记进「已对齐决策」。
What ships with it
1 file 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.
- 11d ago First seen · 62 lines · 133 tokens per session scan A d871084a3278
recruit-grill is a skill published in the GitHub repository Viy1204/recruiting-copilot (65 stars, last pushed 9d ago), licensed MIT. It adds 133 tokens to every session and 1,078 once invoked, about $0.0007 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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