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 market-talent-mappinggit 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/market-talent-mapping)<a href="https://agentmods.dev/skills/viy1204/recruiting-copilot/market-talent-mapping"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/market-talent-mapping/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/market-talent-mapping"><img src="https://agentmods.dev/badge/skills/viy1204/recruiting-copilot/market-talent-mapping.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 79 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 80 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00166 | $0.02359 |
| Opus 5 | $0.00083 | $0.01179 |
| Sonnet 5 | $0.00033 | $0.00472 |
| Haiku 4.5 | $0.00017 | $0.00236 |
Grade A, and why
market-talent-mapping 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
市场人才盘点与招聘打法
把 Boss 直聘 + 猎聘的实时求职人才数据，调研分析成一份能指导招聘实践的报告：讲清某岗市场真实情况（供给/薪资/学历/年限/命脉/地域），给出可直接执行的招聘动作。
产出：本地 markdown（事实源，必出）；有 lark-cli 再出飞书云文档（给老板/可脱敏喂猎头）；需要可扫读版再出 HTML。
这是编排文档：渠道命令细节走 boss help / liepin help 与 references/channels-and-analysis.md。本文档管顺序、判断、数据可信度、报告结构与安全。
铁律：先读事实源，再动手
进工作区先读（都在工作区根下）：
CONTEXT.md—— 术语、在招岗位与命脉、初筛硬规则（年龄/学历/地点线）、薪资带宽。唯一事实源。01-jd/_internal/<role>.md—— 各岗目标公司锚点（就是你的挖人对标企业地图）、关键词迭代表（有效/无效搜索词）、排除信号。寻源前必读，越搜越准。02-sourcing/dedup-ledger.csv—— 已收录候选人，其中目标公司背景的直接作 mapping 种子，不重复拉。
命脉定义、目标公司分层、薪资带宽、硬门槛都以 CONTEXT 与对内笔记为准，本文档不写任何具体数字和公司名。
主流程（按需跳步）
1. 锁定范围与锚点
按 CONTEXT 确认要盘点的岗位、命脉、目标公司、薪资带宽、硬门槛。地域以你的办公城市为中心分两层盘：①本地/临近城市（relocate 阻力小）可直招 ②异地需 relocate/猎头。
2. 广度取数（每岗）
- Boss 人才库全国搜索（不限职位）= 目标公司直挖主力，本流程最核心工具。 详见 channels-and-analysis.md。用岗位关键词搜，一次出一批在职目标公司候选，带公司/年龄/薪资/城市/院校/经历。⚠️ 先确认搜索页岗位下拉是「不限职位」（否则会被锁在某岗类里搜出错岗的人——踩过坑）。
- 猎聘全国搜索（
liepin search --json）：字段最全（薪资/学历/年龄/公司/学校/年限/resume_id）、名字不打码，主要用来看分布统计。相关度较松、有噪声，按 current_title 关联度过滤。 - Boss 按岗 recommend：办公城市本地池（不打码），用来画"可直招层"。
- 每岗跑 2-3 个关键词（从关键词迭代表取）；多次调用间要 sleep 3-4s，否则会返回空。原始 JSON/文本落
runtime/mapping/，离线统计。 - 完成判据：每岗三渠道（Boss 全国搜 + 猎聘分布 + Boss recommend）都跑过，目标公司命中入 mapping CSV，猎聘分布样本每岗 ≥20；漏一个渠道=没做完。
3. 代表性深挖（每岗 5-8 人）
挑各岗命脉强 + 薪资/背景有代表性的样本，开在线简历看真实履历：
- 猎聘：
liepin resume <rid>（无查看限，深挖主力）。 - Boss：
boss preview <姓名>（须先重载该岗搜索加载列表；每日查看限次，省着用）。 - 深挖是本流程的灵魂——"列表一行"是线索，主体履历才是真相。开简历后反复照出的假信号：短任期贴金（名企只干 1-2 个月）、方向漂移（想转岗/期望岗与现岗不符）、年龄超线、方向错位。判断以简历主体履历为准，公司名气只作线索。
- 完成判据：每岗 5-8 份代表简历都开过、命脉判断落进名单表（强/中评级），"列表看着对但简历不对"的已剔除。
4. 统计分析
写 Python 算每岗分布（薪资中位/区间、学历本科率、年限、年龄、命脉命中率、目标公司分布、本地可招占比）。脚本模式与薪资解析见 channels-and-analysis.md。⚠️ Windows 下 Python 读中文源码要 PYTHONUTF8=1 python -X utf8。
5. 梳理观察与结论
- 观察要反映真实市场：每条带证据（样本量/中位数/具体实例）+ 标可信度——有分布支撑的=强，单个案例的=信号（非定论）。诚实交代"抽样非全量普查"。
- 结论要能指导实践：每岗落到可执行动作——推进谁（可推进名单+理由）、搜什么词/走哪渠道、门槛/薪资调不调、卡在哪。
- 结论建立在足够样本上（教训）：说"这岗没合适的/全是某类人"前，先把低于门槛、低薪、边缘的候选也深挖过——一两个样本得出的全称判断会翻车。用户质疑结论时，回去把没看的样本挖了再答。
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.
- 12d ago First seen · 81 lines · 166 tokens per session scan A e1dfdb3aa4d3
market-talent-mapping is a skill published in the GitHub repository Viy1204/recruiting-copilot (65 stars, last pushed 9d ago), licensed MIT. It adds 166 tokens to every session and 2,359 once invoked, about $0.0008 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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