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 yanliudesign/offer-toolkit-skill --skill bq-skillgit clone --depth 1 https://github.com/yanliudesign/offer-toolkit-skillWrote 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/yanliudesign/offer-toolkit-skill/bq-skill)<a href="https://agentmods.dev/skills/yanliudesign/offer-toolkit-skill/bq-skill"><img src="https://agentmods.dev/badge/skills/yanliudesign/offer-toolkit-skill/bq-skill/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/yanliudesign/offer-toolkit-skill/bq-skill"><img src="https://agentmods.dev/badge/skills/yanliudesign/offer-toolkit-skill/bq-skill.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 | $0.00166 | $0.02149 |
| Opus 5 | $0.00083 | $0.01074 |
| Sonnet 5 | $0.00033 | $0.00430 |
| Haiku 4.5 | $0.00017 | $0.00215 |
Grade A, and why
bq-skill 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 10d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BQ Skill
把"会回答某道 BQ"升级成"拥有一套可复用的职业故事库"。核心循环：
挖掘 (Mine) → 结构化 (Structure) → 打标 (Map) → 存库 (Save) → 复用 (Reuse)
第一原则：先查库，再开工。 任何 BQ 进来，先看 story-bank/_index.md 有没有能命中的故事；能复用就复用/微调，不能再触发新一轮挖掘。这就是"越用越懂用户"。
第二原则：一次只问一个问题。 挖掘是对话，不是问卷。问一个 → 等回答 → 顺着答案追问。永远不要一次抛一串问题。
第三原则：不替用户编故事。 所有素材必须来自用户真实经历。可以引导、可以追问、可以帮他把模糊的说清楚，但绝不杜撰 Task / Action / Result。量化数字一律向用户求证。
路由：用户进来时先判断意图
| 用户说的话 | 走哪条流程 |
|---|---|
| "帮我准备面试" / "我要建故事库" / 给一段经历 | 挖掘新故事 → prompts/story-mining.md |
| 贴出一道具体 BQ（"Tell me about a time…"） | 回答一道题（先查库，下方流程） |
| "我这个故事讲得好吗" / 贴出已有答案 | 打磨已有故事 → prompts/structuring.md |
| "模拟面试" / "出几道题考我" | 模拟面试（v1 轻量版，下方） |
| 给了 JD + 简历 / "针对这个岗位帮我准备 BQ" / "这家会问什么、我怎么答" | JD 驱动的 BQ 选题 + 准备 → prompts/jd-driven-prep.md |
| "看看我的故事库" / "我有哪些故事" | 读 story-bank/_index.md 汇报 |
判断不了就问一句："你是想挖新故事建库，还是针对某道具体题目准备？"
挖掘新故事
完整执行 prompts/story-mining.md 里的四层追问引擎：
- 破冰层 — 专治"我没什么亮点"。用反事实提问 + 四象限时间锚点扫描，先捞出 3–5 个候选事件。
- 深挖层 — 对选中的事件，逐个补全 STAR，重点逼出最常缺的 T（你具体做了什么，而非团队） 和 R（量化结果）。
- 打标层 — 挖完映射能力标签（
frameworks/competency-tags.md）+ 判断能打哪些公司维度（frameworks/company-profiles.md）。 - 存进故事库 — 按
story-bank/_story-template.md写成一个故事文件，并更新_index.md。
一次会话聚焦挖 挖透 1 个完整故事就够了，挖深比挖多重要。挖完问用户要不要继续下一个。
回答一道具体 BQ
- 解析题目：这道题在考什么能力？（参考
frameworks/competency-tags.md反查） - 查库：读
story-bank/_index.md，找 tags / competencies 命中的故事。- 命中 → 取出故事，按这道题的角度重新组织开场和落点（同一个故事可以打多道题，框架见
frameworks/star-car.md）。 - 未命中 → 转「挖新故事」 现场挖一个，挖完再回答。
- 命中 → 取出故事，按这道题的角度重新组织开场和落点（同一个故事可以打多道题，框架见
- 产出答案：默认中英双语 —— 英文是面试可直接说的版本，附中文要点供复盘。
- 顺手存进故事库：如果是现场新挖的，存进库。
打磨已有故事
执行 prompts/structuring.md：诊断用户现有答案的结构问题（常见：Situation 太长、看不出"我"做了什么、没有量化 Result、能力标签不清晰），给出改写。改完可存库。
模拟面试
- 问目标公司/岗位，加载
frameworks/company-profiles.md对应风格。 - 按该公司常考维度出 1 道题，一次一道。
- 用户作答后给反馈：结构（STAR 是否完整）、能力信号是否清晰、量化是否到位、与该公司维度的契合度。
- 把答得好的故事提示用户存库。
JD 驱动的 BQ 选题 + 准备
针对某个具体岗位做定向 BQ 准备。完整执行 prompts/jd-driven-prep.md，五步流程：
What ships with it
17 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.
- .gitignore 19 B
- assets/bq-prep-report.md 3.9 KB
- assets/demo-report.html 35 KB
- assets/en/.gitkeep 119 B
- assets/preview.png 200 KB
- assets/zh/.gitkeep 103 B
- frameworks/company-profiles.md 2.5 KB
- frameworks/competency-tags.md 2.3 KB
- frameworks/star-car.md 1.9 KB
- prompts/jd-driven-prep.md 6.1 KB
- prompts/story-mining.md 6.7 KB
- prompts/structuring.md 1.5 KB
- README.md 4.7 KB
- README.zh.md 3.5 KB
- story-bank/_index.md 870 B
- story-bank/_story-template.md 1005 B
- story-bank/convince-team-rewrite.md 2.2 KB
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.
- 10d ago First seen · 116 lines · 166 tokens per session scan A 54ab7fa1c29e
bq-skill is a skill published in the GitHub repository yanliudesign/offer-toolkit-skill (424 stars, last pushed 2d ago), licensed MIT. It adds 166 tokens to every session and 2,149 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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