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 ask-viygit 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/ask-viy)<a href="https://agentmods.dev/skills/viy1204/recruiting-copilot/ask-viy"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/ask-viy/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/ask-viy"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/ask-viy.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 | $0.00037 | $0.01360 |
| Opus 5 | $0.00018 | $0.00680 |
| Sonnet 5 | $0.00007 | $0.00272 |
| Haiku 4.5 | $0.00004 | $0.00136 |
Grade A, and why
ask-viy 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
Ask Viy —— 这套工具怎么用
你不需要记住任何 skill 的名字。跟 AI 说人话("我想看看这个岗位好不好招"),它自己会找到对应流程。这张图给你看全貌,也给 AI 当路标。
被问到时:先看用户处在下面哪个位置,把对应的流程名和入口告诉他,再帮他直接开工。设计理念的完整版在仓库的 docs/DESIGN.md,用户想深究"为什么这么设计"时带他去读。
主线:从零到每天
绝大多数时候,你走的是这条线:
-
建工作区(
recruit-init,只做一次) 说"帮我初始化招聘工作区"。AI 检查该装的工具、建好文件夹、放好模板。 已经有工作区了就跳过;重跑也不怕,它只补缺的,不覆盖你的数据。 -
梳理岗位标准(
recruit-grill,每个岗位做一次) 说"帮我梳理 XX 岗位"。AI 一次一个问题地问你,把"要个厉害的"逼成一条条能执行的标准, 写进 CONTEXT.md(你的招聘标准手册)。半小时到一小时,别糊弄——你随口答的每一句, 都会变成它之后每天筛掉几十个人的依据。 -
每天跑流水线(
recruit-daily,天天用) 说"处理今天的招聘"。查未读 → 按你的标准搜人初筛 → 经你确认后打招呼 → 记台账 → 出日报。 这就是日常的全部。聊出苗子要进面试了,走下面岔路里的"面试预约"。
岔路:什么时候离开主线
-
某个岗心里没底 / 老板问市场行情 / 招了很久没起色 → 做一次市场盘点(
market-talent-mapping)。说"帮我盘一下 XX 岗的市场"。 它会把两个平台的真实人才数据拉下来做统计和深挖,告诉你:这岗市场上有多少人、 薪资什么水位、目标公司的人能不能挖、你的门槛卡得合不合理,附一份能直接执行的名单。 和每日流水线的区别:流水线是每天的增量动作,盘点是一次性的深度调研。 -
寻了几轮,来的全是错的人 → 大概率标准歪了。带着台账数据回去重新梳理(
recruit-grill),重点过硬门槛和命脉两块。 -
猎头/内推/直投简历要评估,或猎聘/BOSS 简历邮件都在飞书邮箱里 → 简历收取与评估(
resume-review)。可以把文件丢给 AI 说"帮我看看这几份简历",也可说"查一下飞书邮箱最近三天的猎聘/BOSS 简历,下载后 review"。 用的是和每日流水线同一套标准,评完自动进同一本台账;"约面"级的还会建好面试档案。 想回忆某个人当时的评价,问"XX 的评级是啥"就行。 -
候选人要进面试了 / 面试要改期 → 面试预约(
interview-schedule)。说"约一下张三的二面,面试官是老王,明天下午"。 AI 查面试官忙闲、建带视频会议链接的日历日程、把面试官拉进去、给你一段发给候选人的邀约话术, 面试档案和台账同步更新。改期取消也走它,三处状态一起改,不放候选人鸽子。 -
新开了岗位 → 先梳理(
recruit-grill),再进每日流水线。没梳理过的岗位,流水线会拒绝开工。
三个日常习惯
- 一天一个新会话;梳理岗位也是一岗一个会话。AI 的对话越长判断越钝, 而你的数据都在文件里,新会话什么都不丢。
- 打招呼授权只管当天。你说"合适的直接打",它今天放开手、打了谁逐个报,明天要重新授权。
- 所有数据就在你的工作文件夹里:标准是 CONTEXT.md,候选人是 02-sourcing 里的台账,
日报在
runtime/reports/,邮件简历和导入索引在runtime/resumes/。备份整个文件夹 = 备份一切。
常见问题
- 没有飞书? 基本不影响。日报自动落成本地文档;约面试拿到手动建会清单;邮箱简历改为用户本地提供,review、台账和档案照常。
- 换了 AI 工具? 不影响。用新工具打开同一个文件夹,一切照旧。
- AI 会不会替我拒人? 永远不会。"不合适"这个按钮它碰不了,它只能记在台账里等你拍板。
- 想改标准? 直接说"XX 岗的年龄线改一下"之类,AI 会更新 CONTEXT.md 并记一条带日期的决策。
- AI 干活的依据是什么? 只有你文件夹里的 CONTEXT.md 和对内笔记。它说的标准和文件不一致时,以文件为准,让它重读。
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 · 68 lines · 37 tokens per session scan A 36df5cd88776
ask-viy is a skill published in the GitHub repository Viy1204/recruiting-copilot (65 stars, last pushed 8d ago), licensed MIT. It adds 37 tokens to every session and 1,360 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 2026-08-30.
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