Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/1xiaoyueryuer/boss-hr-agent-toolkitnpx agentmods add skills/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screenerWrote 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/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screener)<a href="https://agentmods.dev/skills/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screener"><img src="https://agentmods.dev/badge/skills/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screener/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/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screener"><img src="https://agentmods.dev/badge/skills/1xiaoyueryuer/boss-hr-agent-toolkit/resume-screener.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 | $0.00189 | $0.06984 |
| Opus 5 | $0.00095 | $0.03492 |
| Sonnet 5 | $0.00038 | $0.01397 |
| Haiku 4.5 | $0.00019 | $0.00698 |
Grade A, and why
resume-screener scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 479 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Screener
🔁 跨 run 评分去重（脚本自动执行）
score_resumes.py 自带去重，智能体不需要手工过滤已评分候选人：
- 入口：按
job_id:geek_id查state/scored_state.json，历史评过的自动跳过 - 出口：本轮评分结果自动回写
scored_state.json - 逃生门：
--rescore强制重评（换 JD、改评分口径时用）
# 正常评分（自动跳过历史已评人员）
python score_resumes.py --input _llm_scores.json --output screening_results.json \
--job-name "<岗位名>" --encrypt-job-id "<id>" --run-id "$RUN_ID"
# → ⏭ 跳过 12 位历史已评分候选人：张三、李四...
# 缺 _llm_scores.json → SystemExit(26) + JSON 错误提示
# 换了 JD 要全部重评
python score_resumes.py --input _llm_scores.json --output screening_results.json \
--job-name "<岗位名>" --encrypt-job-id "<id>" --run-id "$RUN_ID" --rescore
去重按 geek_id 而非姓名。BOSS 上「杨先生」「吕女士」这类匿名昵称会重名， 脚本对同一姓名维护 ID 列表：只要还有任一同名候选人未评分就放行 （宁可偶尔重复评分，也不把没评过的人误杀）。
姓名 → geek_id 的映射从
state/resumes_master.json反查，所以_llm_scores.json里的name必须与简历原始姓名一致，改写姓名会导致匹配失败（脚本会告警）。
评分架构
- LLM 评 4 维度最终分：
exp / skill / proj / major全部由 LLM 真实分析完整简历后给出 0–100 的最终分（已综合考虑年限、对口度、实操深度、复杂度等） - 脚本只覆盖 1 维度：用
scripts/school_tier.py查表覆盖edu - 公式重算：5 维度 × 权重 = total（按 25/25/25/15/10）
- Tier 判定：≥70 推荐 / 60-69 待定 / <60 不推荐
- 通用：不限岗位（任一 JD 都能用）
LLM 不输出
industry_fit之类的系数标签。exp分本身已综合考虑对口度，脚本不会再乘任何系数。
核心不变量
| 项 | 值 |
|---|---|
| 5 维度权重 | edu 25% / exp 25% / skill 25% / proj 15% / major 10% |
| Tier 阈值 | 推荐 ≥70 / 待定 60-69 / 不推荐 <60 |
| 公式 | total = Σ (raw × weight) |
| Tier 名称 | 推荐 / 待定 / 不推荐 |
| 学校分档 | 7 档（C9 / 985 / 211 / 双一流 / 一本公办 / 二本公办 / 民办） |
| 评分主体 | LLM 评 4 维度最终分 + 脚本查 edu（仅此一套） |
工具脚本
scripts/score_resumes.py
工具函数（agent 直接 import 调用）：
| 函数 | 作用 |
|---|---|
_extract_school_name(score) |
智能拆纯校名（优先 school_name，兜底从 school 按 /·（( 拆分） |
validate_score(score) |
LLM 评分收尾：用 school_tier 覆盖 edu + 重算 weighted + total + 判定 tier |
calc_tier(total) |
≥70 推荐 / 60-69 待定 / <60 不推荐 |
calc_weighted(dims) |
5 维度 × 权重 |
calc_total(weighted) |
求和 |
candidate_to_report(c, rank) |
list 元素 → candidates[] 格式 |
build_actions(candidates) |
生成 actions 三段式（recommend/pending/reject） |
build_meta(job_name, job_info) |
构造报告 meta |
What ships with it
4 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 · 479 lines · 189 tokens per session scan A 38ce0e6c507d
resume-screener is a skill published in the GitHub repository 1xiaoyueryuer/boss-hr-agent-toolkit (46 stars, last pushed 1mo ago), licensed MIT. It adds 189 tokens to every session and 6,984 once invoked, about $0.0009 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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