tensei-skills

generate production-grade interview question banks from a role, jd, technology direction, project, or business system. use when the user asks for 面试题库, 高频题, 面经题, 项目深挖题, 系统设计题, or interview preparation for backend, distributed systems, java, redis, mq, database, payment, flash sale, inventory, order, membership, or other business systems. prioritize real production problems, business constraints, technical decision points, failure modes, follow-up chains, answer anchors, evaluation rubrics, and evidence from current jd/interview sources instead of shallow question lists.

analyze current job market demand for a user-specified career direction using real job descriptions from the last 30 days. use when the user asks to evaluate a role, technology direction, career path, market demand, salary value, job requirements, or skill gap based on boss直聘, lagou, liepin, linkedin, indeed, company career pages, or user-provided jd text. must collect and analyze at least 6 valid recent jds before making strong conclusions; every conclusion must be grounded in jd evidence.

orchestrate a personal ai-era growth operating system for daily judgment training, weekly theme planning, information intake, scenario analysis, language output, market calibration, and social practice. use when the user asks to run, design, review, or maintain a daily mustdo system; build a personal ability operating system; plan weekly/monthly learning themes; coordinate hacker news, github trending, track awesome list, scenario harness, boss/job-market review, or workplace/social-practice reflection; or convert scattered self-improvement tasks into a closed-loop training workflow.

analyze technical decision points in a business scenario or system design. use when the user asks to identify, compare, review, or document key technical decisions for systems such as flash sale, payment, order fulfillment, inventory, messaging, recommendations, workflows, risk control, or other business systems. especially useful for turning a scenario into decision points, trade-off matrices, failure modes, recommended choices, architecture constraints, and coding-agent-ready implementation guidance.

以“解决方案架构师 + 人类判断力教练 + harness 工程师”的复合视角，把秒杀、IM、清结算、订单履约、推荐、内容社区等业务场景拆成结构化架构分析、人类判断文档、难点深拆和可执行 prompt 包。Use when: 用户要求系统设计、业务场景拆解、架构方案、harness prompt、让 Claude Code/Codex/Cursor 实现，或希望用 AI 作为工程杠杆但不外包判断力。输出强调人类必须亲自拥有的问题定义、不变量、取舍、失败模式、验收标准和复盘问题，再把确认后的判断压成 agent 可执行契约。

将一段口语化、碎片化的每日热点串讲整理成结构化中文分析文档，重点识别热点叙事与关键事实之间的信息差、背景缺口、误读点和后续观察指标。 Use when: 用户要做每日热点分析、信息差早报、晨会简报、舆情复盘、风险雷达，或提供一段热点口播/碎片文本并要求结构化分析。

输入一个项目或子模块，输出基于DDD的“系统业务架构与技术规划分析文档”。文档必须足够详尽、不可过度精简；最终总行数必须 >1000 行；内容过长时必须按“分片追加协议”多次输出并保持行号连续。

对 GitHub Trending 仓库或单个 GitHub repo 输出逐仓库同等深度的中文分析，覆盖详细 TL;DR、核心特性、痛点到方案到效果、证据来源和快速试用路径。 Use when: 用户提到 GitHub Trending、热门仓库、想快速读懂某个 repo、想知道它解决什么问题、为什么火、有哪些核心特性，或要求做榜单速报。

抓取 Hacker News 的 top/best/new/ask/show/jobs 榜单，针对 Top N 帖子输出结构化中文深读文档，解释帖子讲了什么、为什么上榜、讨论焦点和可执行结论。 Use when: 用户提到 Hacker News、HN、热榜、Top N、今日 HN、上热搜、为什么火，或要求把 HN 榜单做成中文简报/讲解文档。

对单个产品做立项前分析，输出结构化中文报告，覆盖产品定义、需求强度、创新点、竞品矩阵、商业价值、付费意愿、风险、批判性锐评和两周验证计划。 Use when: 用户要做产品立项分析、竞品矩阵、商业价值判断、付费意愿分析、PRD 前置调研、内部立项备忘录，或要求“像评审会一样锐评一个产品”。

将文章、研报、论文或白皮书重写为可教学、可转发、可落地的中文讲义，要求高覆盖率、高保真和教学化扩展，并在需要时输出原子信息点 Ledger 与 Claim-Evidence 映射。 Use when: 用户要求精读文章、研报解读、论文讲解、内部培训讲义、读书会材料，或强调“不要过度总结”“按讲义方式讲清楚”。

将短篇洞察、社交媒体短帖、经验片段或一句话忠告扩展成高信息密度的中文深度文档，要求覆盖全部核心观点，并补认知模型、批判性分析、方法论逆向和行动清单。 Use when: 用户提供短篇文章、短帖、行业洞察、经验总结、领导短评或金句，希望把短内容扩展成可教学、可落地、可转发的深度文档。

analyze yesterday's updates on trackawesomelist by starting from https://www.trackawesomelist.com/, retrieving the actual updated awesome lists and their newly added items, then producing a long-form structured chinese report. use when the user asks to analyze yesterday's track awesome list, yesterday's awesome list updates, recent trackawesomelist changes, or wants a deep report that first covers all updates and then selects the most important ones for deeper interpretation.