Creator · yanliudesign
Last updated · Sep 5, 2026
行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。
Creator · yanliudesign
Last updated · Sep 5, 2026
行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。
Creator · yanliudesign
Last updated · Sep 5, 2026
行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。
Creator · yanliudesign
Last updated · Sep 5, 2026
行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。
Sandbox only
Install targets
Codex install prompt
Install the "bq-skill" agent skill from https://github.com/yanliudesign/offer-toolkit-skill/tree/main/bq-skill. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。 After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"yanliudesign-bq-skill","task":"Install bq-skill","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Maintenance
fresh
5d since push
Risk
Safe to try
Quality score needs review
GitHub quality
385
73/100 Quality · 81/100 Trust
Coverage tags
Review notes
Quality score needs review · Stars/forks activity: 385 stars, 39 forks; issue activity unavailable in current metadata
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
385 GitHub stars
Repo activity
385 stars, 39 forks
Maintenance
5d since push
License
MIT
Install
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/yanliudesign-bq-skill/install
Agent should check
Copy prompt
Task: Use bq-skill in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install
Install command: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/yanliudesign-bq-skill/install
LLM text format
/api/skills/yanliudesign-bq-skill/install?format=text
Find alternatives
/api/skills/search?q=bq-skill&limit=3
Agent prompt
Use bq-skill for this task. Review https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install, then install with: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/yanliudesign-bq-skill
LLM text
/api/registry/manifest/yanliudesign-bq-skill?format=text
Install alias
/api/registry/install/yanliudesign-bq-skill
Recommend
/api/registry/recommend?task=Use%20bq-skill%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Workflow automation
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO385 GitHub stars
Stars/forks activity
CHECK385 stars, 39 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: bq-skill description: "行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。" ---
# 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：
1. **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。 2. **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）**。 3. **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`）。 4. **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
1. **解析题目**：这道题在考什么能力？（参考 `frameworks/competency-tags.md` 反查） 2. **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。 3. **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。 4. **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
1. 问目标公司/岗位，加载 `frameworks/company-profiles.md` 对应风格。 2. 按该公司常考维度出 1 道题，**一次一道**。 3. 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。 4. 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：
``` ① 上传 JD → ② 上传简历 → ③ 生成 Top 20 选题（对着 JD）→ ④ 基于你的经历给 STAR 准备模板 → ⑤ 输出 HTML 报告 ```
1. **拿 JD** — 优先复用 `../job-description-skill/jd-bank/` 里已解码的 Must Have + Hidden Signals；没有就让用户贴 JD，先过一遍解码。 2. **拿简历全文** — 给每段经历打能力标签，为配对做准备。简历太薄 → 转「挖新故事」 先挖几个故事。 3. **交叉分析 → Top 20 选题** — 四类（Behavior 通用 / 公司价值观定制 / 岗位专业向 / Level 阶段定制）。每题必带「考什么 + **为什么这家会问**（指向 JD 某条 Must Have / Hidden Signal）+ 配哪个故事」。 4. **逐题准备模板** — 默认对 **Top 5 必练**写完整 STAR 模板：S/T 用简历事实填实，**A/R 留占位符**引导用户填真实动作和数字，**绝不杜撰**。标注一稿多用。 5. **缺口清单** — JD 要、简历没素材的题如实列出，导流到「挖新故事」 现场挖。 6. **HTML 报告** — 默认输出到 `~/Desktop/Claude skills/bq-prep-<company>-<role>-<YYYYMM>.html`（视觉规范见 `assets/bq-prep-report.md`）。 7. **回写** — 把 JD↔故事映射写回 job-description-skill 的 jd-bank 文件「Predicted Questions」，形成完整求职链路。
> 这是 job-description-skill 面试预测流程的下游深化：那边给「会问什么」，这里给「**用你的真实经历怎么答 + 模板**」。
---
## 故事库（Story Bank）
- 位置：本 skill 目录下 `story-bank/`，每个故事一个 `.md`。 - 元数据靠 frontmatter（tags / competencies / company_fit / metrics / framework / status）。 - `_index.md` 是反查表：**能力标签 → 故事文件**，是「答题」 复用的入口。 - 每次新增或修改故事，**务必同步更新 `_index.md`**。
---
## 参考文件（按需读取，别一次全加载）
- `prompts/story-mining.md` — 四层追问引擎（「挖新故事」 核心） - `prompts/structuring.md` — 结构化诊断与改写（「打磨已有故事」） - `prompts/jd-driven-prep.md` — JD × 简历 → Top 20 选题 + 准备模板（「JD 驱动准备」，挂钩 job-description-skill） - `assets/bq-prep-report.md` — 「JD 驱动准备」 的 HTML 报告视觉规范 - `frameworks/star-car.md` — STAR / CAR / SOAR 何时用哪个、同一故事多角度复用 - `frameworks/competency-tags.md` — 能力标签词典 + BQ 题型反查 - `frameworks/company-profiles.md` — Amazon LP / Meta / Anthropic / OpenAI 等评价风格 - `story-bank/_story-template.md` — 故事文件模板 - `story-bank/_index.md` — 故事索引
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for bq-skill, ready for a manual X post.
bq-skill: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 T... 385 stars https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x
Listing + install path for bq-skill: https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x Install: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to yanliudesign but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill/audit)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)yanliudesign
@yanliudesign
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsSandbox only
Install targets
Codex install prompt
Install the "bq-skill" agent skill from https://github.com/yanliudesign/offer-toolkit-skill/tree/main/bq-skill. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。 After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"yanliudesign-bq-skill","task":"Install bq-skill","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Maintenance
fresh
5d since push
Risk
Safe to try
Quality score needs review
GitHub quality
385
73/100 Quality · 81/100 Trust
Coverage tags
Review notes
Quality score needs review · Stars/forks activity: 385 stars, 39 forks; issue activity unavailable in current metadata
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
385 GitHub stars
Repo activity
385 stars, 39 forks
Maintenance
5d since push
License
MIT
Install
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/yanliudesign-bq-skill/install
Agent should check
Copy prompt
Task: Use bq-skill in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install
Install command: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/yanliudesign-bq-skill/install
LLM text format
/api/skills/yanliudesign-bq-skill/install?format=text
Find alternatives
/api/skills/search?q=bq-skill&limit=3
Agent prompt
Use bq-skill for this task. Review https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install, then install with: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/yanliudesign-bq-skill
LLM text
/api/registry/manifest/yanliudesign-bq-skill?format=text
Install alias
/api/registry/install/yanliudesign-bq-skill
Recommend
/api/registry/recommend?task=Use%20bq-skill%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Workflow automation
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO385 GitHub stars
Stars/forks activity
CHECK385 stars, 39 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: bq-skill description: "行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。" ---
# 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：
1. **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。 2. **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）**。 3. **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`）。 4. **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
1. **解析题目**：这道题在考什么能力？（参考 `frameworks/competency-tags.md` 反查） 2. **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。 3. **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。 4. **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
1. 问目标公司/岗位，加载 `frameworks/company-profiles.md` 对应风格。 2. 按该公司常考维度出 1 道题，**一次一道**。 3. 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。 4. 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：
``` ① 上传 JD → ② 上传简历 → ③ 生成 Top 20 选题（对着 JD）→ ④ 基于你的经历给 STAR 准备模板 → ⑤ 输出 HTML 报告 ```
1. **拿 JD** — 优先复用 `../job-description-skill/jd-bank/` 里已解码的 Must Have + Hidden Signals；没有就让用户贴 JD，先过一遍解码。 2. **拿简历全文** — 给每段经历打能力标签，为配对做准备。简历太薄 → 转「挖新故事」 先挖几个故事。 3. **交叉分析 → Top 20 选题** — 四类（Behavior 通用 / 公司价值观定制 / 岗位专业向 / Level 阶段定制）。每题必带「考什么 + **为什么这家会问**（指向 JD 某条 Must Have / Hidden Signal）+ 配哪个故事」。 4. **逐题准备模板** — 默认对 **Top 5 必练**写完整 STAR 模板：S/T 用简历事实填实，**A/R 留占位符**引导用户填真实动作和数字，**绝不杜撰**。标注一稿多用。 5. **缺口清单** — JD 要、简历没素材的题如实列出，导流到「挖新故事」 现场挖。 6. **HTML 报告** — 默认输出到 `~/Desktop/Claude skills/bq-prep-<company>-<role>-<YYYYMM>.html`（视觉规范见 `assets/bq-prep-report.md`）。 7. **回写** — 把 JD↔故事映射写回 job-description-skill 的 jd-bank 文件「Predicted Questions」，形成完整求职链路。
> 这是 job-description-skill 面试预测流程的下游深化：那边给「会问什么」，这里给「**用你的真实经历怎么答 + 模板**」。
---
## 故事库（Story Bank）
- 位置：本 skill 目录下 `story-bank/`，每个故事一个 `.md`。 - 元数据靠 frontmatter（tags / competencies / company_fit / metrics / framework / status）。 - `_index.md` 是反查表：**能力标签 → 故事文件**，是「答题」 复用的入口。 - 每次新增或修改故事，**务必同步更新 `_index.md`**。
---
## 参考文件（按需读取，别一次全加载）
- `prompts/story-mining.md` — 四层追问引擎（「挖新故事」 核心） - `prompts/structuring.md` — 结构化诊断与改写（「打磨已有故事」） - `prompts/jd-driven-prep.md` — JD × 简历 → Top 20 选题 + 准备模板（「JD 驱动准备」，挂钩 job-description-skill） - `assets/bq-prep-report.md` — 「JD 驱动准备」 的 HTML 报告视觉规范 - `frameworks/star-car.md` — STAR / CAR / SOAR 何时用哪个、同一故事多角度复用 - `frameworks/competency-tags.md` — 能力标签词典 + BQ 题型反查 - `frameworks/company-profiles.md` — Amazon LP / Meta / Anthropic / OpenAI 等评价风格 - `story-bank/_story-template.md` — 故事文件模板 - `story-bank/_index.md` — 故事索引
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for bq-skill, ready for a manual X post.
bq-skill: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 T... 385 stars https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x
Listing + install path for bq-skill: https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x Install: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to yanliudesign but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill/audit)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)yanliudesign
@yanliudesign
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsSandbox only
Install targets
Codex install prompt
Install the "bq-skill" agent skill from https://github.com/yanliudesign/offer-toolkit-skill/tree/main/bq-skill. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。 After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"yanliudesign-bq-skill","task":"Install bq-skill","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Maintenance
fresh
5d since push
Risk
Safe to try
Quality score needs review
GitHub quality
385
73/100 Quality · 81/100 Trust
Coverage tags
Review notes
Quality score needs review · Stars/forks activity: 385 stars, 39 forks; issue activity unavailable in current metadata
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
385 GitHub stars
Repo activity
385 stars, 39 forks
Maintenance
5d since push
License
MIT
Install
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/yanliudesign-bq-skill/install
Agent should check
Copy prompt
Task: Use bq-skill in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install
Install command: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/yanliudesign-bq-skill/install
LLM text format
/api/skills/yanliudesign-bq-skill/install?format=text
Find alternatives
/api/skills/search?q=bq-skill&limit=3
Agent prompt
Use bq-skill for this task. Review https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install, then install with: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/yanliudesign-bq-skill
LLM text
/api/registry/manifest/yanliudesign-bq-skill?format=text
Install alias
/api/registry/install/yanliudesign-bq-skill
Recommend
/api/registry/recommend?task=Use%20bq-skill%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Workflow automation
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO385 GitHub stars
Stars/forks activity
CHECK385 stars, 39 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: bq-skill description: "行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。" ---
# 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：
1. **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。 2. **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）**。 3. **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`）。 4. **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
1. **解析题目**：这道题在考什么能力？（参考 `frameworks/competency-tags.md` 反查） 2. **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。 3. **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。 4. **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
1. 问目标公司/岗位，加载 `frameworks/company-profiles.md` 对应风格。 2. 按该公司常考维度出 1 道题，**一次一道**。 3. 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。 4. 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：
``` ① 上传 JD → ② 上传简历 → ③ 生成 Top 20 选题（对着 JD）→ ④ 基于你的经历给 STAR 准备模板 → ⑤ 输出 HTML 报告 ```
1. **拿 JD** — 优先复用 `../job-description-skill/jd-bank/` 里已解码的 Must Have + Hidden Signals；没有就让用户贴 JD，先过一遍解码。 2. **拿简历全文** — 给每段经历打能力标签，为配对做准备。简历太薄 → 转「挖新故事」 先挖几个故事。 3. **交叉分析 → Top 20 选题** — 四类（Behavior 通用 / 公司价值观定制 / 岗位专业向 / Level 阶段定制）。每题必带「考什么 + **为什么这家会问**（指向 JD 某条 Must Have / Hidden Signal）+ 配哪个故事」。 4. **逐题准备模板** — 默认对 **Top 5 必练**写完整 STAR 模板：S/T 用简历事实填实，**A/R 留占位符**引导用户填真实动作和数字，**绝不杜撰**。标注一稿多用。 5. **缺口清单** — JD 要、简历没素材的题如实列出，导流到「挖新故事」 现场挖。 6. **HTML 报告** — 默认输出到 `~/Desktop/Claude skills/bq-prep-<company>-<role>-<YYYYMM>.html`（视觉规范见 `assets/bq-prep-report.md`）。 7. **回写** — 把 JD↔故事映射写回 job-description-skill 的 jd-bank 文件「Predicted Questions」，形成完整求职链路。
> 这是 job-description-skill 面试预测流程的下游深化：那边给「会问什么」，这里给「**用你的真实经历怎么答 + 模板**」。
---
## 故事库（Story Bank）
- 位置：本 skill 目录下 `story-bank/`，每个故事一个 `.md`。 - 元数据靠 frontmatter（tags / competencies / company_fit / metrics / framework / status）。 - `_index.md` 是反查表：**能力标签 → 故事文件**，是「答题」 复用的入口。 - 每次新增或修改故事，**务必同步更新 `_index.md`**。
---
## 参考文件（按需读取，别一次全加载）
- `prompts/story-mining.md` — 四层追问引擎（「挖新故事」 核心） - `prompts/structuring.md` — 结构化诊断与改写（「打磨已有故事」） - `prompts/jd-driven-prep.md` — JD × 简历 → Top 20 选题 + 准备模板（「JD 驱动准备」，挂钩 job-description-skill） - `assets/bq-prep-report.md` — 「JD 驱动准备」 的 HTML 报告视觉规范 - `frameworks/star-car.md` — STAR / CAR / SOAR 何时用哪个、同一故事多角度复用 - `frameworks/competency-tags.md` — 能力标签词典 + BQ 题型反查 - `frameworks/company-profiles.md` — Amazon LP / Meta / Anthropic / OpenAI 等评价风格 - `story-bank/_story-template.md` — 故事文件模板 - `story-bank/_index.md` — 故事索引
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for bq-skill, ready for a manual X post.
bq-skill: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 T... 385 stars https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x
Listing + install path for bq-skill: https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x Install: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to yanliudesign but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill/audit)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)yanliudesign
@yanliudesign
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsSandbox only
Install targets
Codex install prompt
Install the "bq-skill" agent skill from https://github.com/yanliudesign/offer-toolkit-skill/tree/main/bq-skill. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。 After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"yanliudesign-bq-skill","task":"Install bq-skill","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + OpenAI Agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Maintenance
fresh
5d since push
Risk
Safe to try
Quality score needs review
GitHub quality
385
73/100 Quality · 81/100 Trust
Coverage tags
Review notes
Quality score needs review · Stars/forks activity: 385 stars, 39 forks; issue activity unavailable in current metadata
Agent adoption scorecard
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
StrongSolid option that is likely worth shortlisting for production workflows.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Safe to tryA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
385 GitHub stars
Repo activity
385 stars, 39 forks
Maintenance
5d since push
License
MIT
Install
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Install safety
Agent-readable metadata
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
Suited agents
Install decision
Trust and risk
Outcome loop
Install command
npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillDo not use when
Agent safety v2
Good audit and safety signals with no high-risk permission hints in public metadata.
Review the audit page, then allow agent install in a sandboxed workflow.
medium
Skill likely fetches remote pages, APIs, repositories, or external services.
Agent resolve plan
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/yanliudesign-bq-skill/install
Agent should check
Copy prompt
Task: Use bq-skill in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20bq-skill%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install
Install command: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/yanliudesign-bq-skill/install
LLM text format
/api/skills/yanliudesign-bq-skill/install?format=text
Find alternatives
/api/skills/search?q=bq-skill&limit=3
Agent prompt
Use bq-skill for this task. Review https://www.openagentskill.com/api/skills/yanliudesign-bq-skill/install, then install with: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skillRegistry metadata
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/yanliudesign-bq-skill
LLM text
/api/registry/manifest/yanliudesign-bq-skill?format=text
Install alias
/api/registry/install/yanliudesign-bq-skill
Recommend
/api/registry/recommend?task=Use%20bq-skill%20in%20an%20agent%20workflow&limit=3
Agent fit
Workflow automation
Use-case tags
Platforms
Claude Code, OpenAI Agents
Audit report
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Shortlist this skill and compare it with close alternatives before production adoption.
Role in stack
Companion skill
Primary fit
Workflow automation
Trust label
Strong shortlist
Install path
Command ready
Use when
Evidence
review first
Implementation path
Trust profile
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
INFO385 GitHub stars
Stars/forks activity
CHECK385 stars, 39 forks; issue activity unavailable in current metadata
Recent maintenance
PASS5d since push
License clarity
PASSMIT
Good signals
Review before install
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Solid option that is likely worth shortlisting for production workflows.
Workflow fit
Automate repeated work
I need my agent to automate a repeated workflow across tools and files.
Operate local tools
I need my agent to operate local files and desktop apps in a repeatable workflow.
Collect structured data
I need my agent to scrape websites and extract structured data from pages.
Workflow fit
Turn skills into distribution
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Scrape, clean, and reuse web data
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
Design, build, test, and ship interfaces
A practical workflow for agents that turn product briefs or Figma designs into polished frontend code, review the result, test it in a browser, and prepare a safe deployment.
Alternative shortlist
Similar skills that may fit this task.
Run multimodal agents that operate desktop interfaces
Connect agents to hundreds of workflow automations
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
--- name: bq-skill description: "行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 Top 20 BQ 选题 + 基于真实经历的 STAR 准备模板（HTML 报告）。不是背答案，而是建立可复用的职业叙事体系，让任何行为面试题都能自然作答。关键词：behavioral question, BQ, 行为面试, STAR, 故事库, Amazon LP, 职业故事, tell me about a time, 面试准备, JD 面试题预测, top 20 题。" ---
# 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：
1. **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。 2. **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）**。 3. **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`）。 4. **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
1. **解析题目**：这道题在考什么能力？（参考 `frameworks/competency-tags.md` 反查） 2. **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。 3. **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。 4. **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
1. 问目标公司/岗位，加载 `frameworks/company-profiles.md` 对应风格。 2. 按该公司常考维度出 1 道题，**一次一道**。 3. 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。 4. 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：
``` ① 上传 JD → ② 上传简历 → ③ 生成 Top 20 选题（对着 JD）→ ④ 基于你的经历给 STAR 准备模板 → ⑤ 输出 HTML 报告 ```
1. **拿 JD** — 优先复用 `../job-description-skill/jd-bank/` 里已解码的 Must Have + Hidden Signals；没有就让用户贴 JD，先过一遍解码。 2. **拿简历全文** — 给每段经历打能力标签，为配对做准备。简历太薄 → 转「挖新故事」 先挖几个故事。 3. **交叉分析 → Top 20 选题** — 四类（Behavior 通用 / 公司价值观定制 / 岗位专业向 / Level 阶段定制）。每题必带「考什么 + **为什么这家会问**（指向 JD 某条 Must Have / Hidden Signal）+ 配哪个故事」。 4. **逐题准备模板** — 默认对 **Top 5 必练**写完整 STAR 模板：S/T 用简历事实填实，**A/R 留占位符**引导用户填真实动作和数字，**绝不杜撰**。标注一稿多用。 5. **缺口清单** — JD 要、简历没素材的题如实列出，导流到「挖新故事」 现场挖。 6. **HTML 报告** — 默认输出到 `~/Desktop/Claude skills/bq-prep-<company>-<role>-<YYYYMM>.html`（视觉规范见 `assets/bq-prep-report.md`）。 7. **回写** — 把 JD↔故事映射写回 job-description-skill 的 jd-bank 文件「Predicted Questions」，形成完整求职链路。
> 这是 job-description-skill 面试预测流程的下游深化：那边给「会问什么」，这里给「**用你的真实经历怎么答 + 模板**」。
---
## 故事库（Story Bank）
- 位置：本 skill 目录下 `story-bank/`，每个故事一个 `.md`。 - 元数据靠 frontmatter（tags / competencies / company_fit / metrics / framework / status）。 - `_index.md` 是反查表：**能力标签 → 故事文件**，是「答题」 复用的入口。 - 每次新增或修改故事，**务必同步更新 `_index.md`**。
---
## 参考文件（按需读取，别一次全加载）
- `prompts/story-mining.md` — 四层追问引擎（「挖新故事」 核心） - `prompts/structuring.md` — 结构化诊断与改写（「打磨已有故事」） - `prompts/jd-driven-prep.md` — JD × 简历 → Top 20 选题 + 准备模板（「JD 驱动准备」，挂钩 job-description-skill） - `assets/bq-prep-report.md` — 「JD 驱动准备」 的 HTML 报告视觉规范 - `frameworks/star-car.md` — STAR / CAR / SOAR 何时用哪个、同一故事多角度复用 - `frameworks/competency-tags.md` — 能力标签词典 + BQ 题型反查 - `frameworks/company-profiles.md` — Amazon LP / Meta / Anthropic / OpenAI 等评价风格 - `story-bank/_story-template.md` — 故事文件模板 - `story-bank/_index.md` — 故事索引
Source provenance
Decision snapshot
recent repository activity
Audit
Install and adoption review
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Free and open source. Review the report before installing into production agents.
Growth loop
Scenario-led draft for bq-skill, ready for a manual X post.
bq-skill: 行为面试 skill。帮求职者挖掘真实经历、用 STAR/CAR 结构化、映射能力标签、构建可复用的中英双语故事库（Story Bank）；并能接入 JD + 简历，针对具体岗位生成 T... 385 stars https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x
Listing + install path for bq-skill: https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=x Install: npx skills add yanliudesign/offer-toolkit-skill --skill bq-skill
Listing source
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
This Registry indexed listing is attributed to yanliudesign but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill/audit)
[](https://www.openagentskill.com/skills/yanliudesign-bq-skill?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)yanliudesign
@yanliudesign
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Sandbox only
UI-TARS Desktop
Run multimodal agents that operate desktop interfaces
37.0K Starsn8n
Connect agents to hundreds of workflow automations
194.1K StarsMoneyPrinterTurbo
利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.
88.5K StarsTasmota
Alternative firmware for ESP8266 and ESP32 based devices with easy configuration using webUI, OTA updates, automation using timers or rules, expandability and entirely local control over MQTT, HTTP, Serial or KNX. Full documentation at
24.7K StarsPermission surface
no high-risk permission surface in public metadata
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
no high-risk permission surface in public metadata
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
no high-risk permission surface in public metadata
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness
Permission surface
no high-risk permission surface in public metadata
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Install readiness