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 dizhouid-lgtm/liepin-ai-loop-recruiting --skill pxb-liepingit clone --depth 1 https://github.com/dizhouid-lgtm/liepin-ai-loop-recruitingWrote 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/dizhouid-lgtm/liepin-ai-loop-recruiting/pxb-liepin)<a href="https://agentmods.dev/skills/dizhouid-lgtm/liepin-ai-loop-recruiting/pxb-liepin"><img src="https://agentmods.dev/badge/skills/dizhouid-lgtm/liepin-ai-loop-recruiting/pxb-liepin/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/dizhouid-lgtm/liepin-ai-loop-recruiting/pxb-liepin"><img src="https://agentmods.dev/badge/skills/dizhouid-lgtm/liepin-ai-loop-recruiting/pxb-liepin.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 | $0.00152 | $0.07602 |
| Opus 5 | $0.00076 | $0.03801 |
| Sonnet 5 | $0.00030 | $0.01520 |
| Haiku 4.5 | $0.00015 | $0.00760 |
Grade A, and why
pxb-liepin 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pixboom 搜人筛人作业流程
⚠️ 改本 Skill 前先想:这里只放所有岗通用、可复用的作业流程。单一案例/某轮/某岗经验 → 进该岗
JD.md,不进这里。 每岗四件:JD.md(对外 JD + 内部备注表,判断只看它)·去重台账.csv(resume_id,status,仅去重)·候选人池.md(确认候选人)·待定/(简历 PDF 中转)。 路径:命令里<岗位>\…相对工作区根(本CLAUDE.md所在);脚本全在.claude/skills/pxb-liepin/scripts/,命令都写全路径,原样可跑。机械活全在脚本、跨 Win/mac;判断在本文 + agent。
全流程:两层循环,主 agent 串行自跑
P. 聊全 JD → 0. 体检+抢锁 → 0.5 登录 → 1. 粗筛 → 2. 精筛 → 3. 够一批没(没够回 1)→ 4. 出 PDF 交用户 → 5. 入池 → 6. 迭代回 0;招满/用户喊停 → 7. 结项复盘。
- 不派 subagent:单账号单锁串行,subagent 无并行收益、反而启动慢易卡死;主 agent 从头跑到尾。
- 小循环(1-3,自跑、不打扰用户):搜一页 → 粗筛挑 ~10 → 精筛 → 对口标
待定→ 没攒够回步骤 1,连轮跑到攒够 ~5 个待定。绝不跑一轮就停等用户。 - 大循环(4-6,用户校准 JD):出一批 PDF → 用户复核 → 入池 → 迭代回写 JD(重点是进化关键词)→ 下一批。JD 越调越准、小循环越搜越准。
- 上下文:脚本只输出窄表。看完做完判断后记住结论即可(
待定的 id=标签、方向感、哪个词有效),别把整页窄表一直留着;轮数多了丢旧轮明细。
状态机(去重台账只此 5 值,全程走 dedup.mjs,绝不手改 CSV)
未精筛(捞回来没细看的干净库存)→ 粗筛不合适(卡片就否)/精筛不合适(细看后否,含用户毙掉的)/待定(细看够格、出了 PDF 等复核)→ 入选(用户拍板要了)。
心跳 + 输出纪律(治"闷头静默"和"过程废话/散问打断")
- 边跑边报:每跑完一轮先发一行进度再继续,别让用户 10-20 分钟看不到动静。格式:
第N轮(词:X):搜A/精筛B/新增待定C,累计 D/目标 E,继续…。心跳是单向,不算打扰。 - 心跳就那一行:逐人淘汰理由、本轮发现、推理全留你脑子里 + JD 表,别讲给用户。
- 交付干净:出批消息 = 几份可点 PDF + 问为什么,别夹每轮过程、复盘、SKILL 自测。
- 决策别打断、给推荐、能不问就别问:破例/放宽约束/停搜这类才需用户拍的,优先带推荐先做可逆一步(照推荐先提报、复核可撤),决策点在交付里一句话点出;真要问就攒一处、一次问清、每条附推荐,别过程里东一句西一句、也别没问完就干等。照 JD 能自己判的,别问。
全程红线
① 单账号串行,绝不并发调 liepin。② 反爬宁慢勿封:脚本非零退出即停、别密集连刷(脚本内已带抖动)。③ 原始 JSON 不进上下文(脚本只输出窄表)。④ 隐私:简历只存本地、不外发、不传外部服务。⑤ 只 search/resume,不 greet(沟通归 HR)。⑥ 只走脚本(search/fetch/pdf/jd-round,已强制无头),除用户手动 liepin login 外绝不裸敲 liepin(裸调发布版默认有头会弹窗)。全程不该弹任何浏览器窗口;弹了 = 有人裸调或没登录 → 停手排查、别继续刷(可选安全网:装完跑一次 patch-headless.mjs)。
脚本无参运行会打印用法;任一脚本退出码 ≠ 0 = 停手,按报错处置(多半反爬/被踢/未登录)。
P. 开搜前:把 JD + 人员画像聊全(主 agent+用户,不碰 liepin、不抢锁)
搜任何人之前,先把该岗 JD + 人员画像补到能搜的状态——判断全程只看它,补不全 = 瞎搜、多轮返工。在对话里完成。
🟢 怎么聊(最重要):像懂行的猎头跟用人方对齐需求,用人话把"要什么样的人、这种人在哪、什么样的看着像其实不是"聊明白。下面的"三锚/锚一二三"是给你对照的内部清单,不是台词——别把这些词、别"锚一锚二"这样列条目念给客户。自然地问、一次一两点。
What ships with it
11 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.
- scripts/_csv.mjs 1.7 KB runs code
- scripts/_liepin.mjs 4.5 KB runs code
- scripts/dedup.mjs 2.7 KB runs code
- scripts/doctor.mjs 4.3 KB runs code
- scripts/fetch.mjs 3.0 KB runs code
- scripts/init-role.mjs 1.4 KB runs code
- scripts/jd-round.mjs 1.6 KB runs code
- scripts/lock.mjs 3.1 KB runs code
- scripts/patch-headless.mjs 2.0 KB runs code
- scripts/pdf.mjs 7.7 KB runs code
- scripts/search.mjs 4.0 KB runs code
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 · 211 lines · 152 tokens per session scan A 06f5bd5f0b3f
pxb-liepin is a skill published in the GitHub repository dizhouid-lgtm/liepin-ai-loop-recruiting (3 stars, last pushed 2mo ago), licensed MIT. It adds 152 tokens to every session and 7,602 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 2026-08-31.
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