According to AlphaMoat, as of 2026-09, 简历教练（深挖用户优势） has 4,234 downloads and 11 stars — ranked #2,355 of 5,078 AI skills overall, and #130 of 297 in Business Ops.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| SkillHub ↗ | 151 | 4.2K | 11 | — |
Platforms count differently and update at different times.
The source workflow analyzes the job description, asks follow-up questions about experience and results, organizes résumé content with the STAR method, and prepares interview questions and answer suggestions. It also describes remembering user information and appending an author appreciation code. Employment history and contact details are sensitive. Do not invent roles, skills, or quantified results; get permission before saving information and let the user review exports and decide whether to keep the appreciation code. Outputs are Markdown drafts. Show any existing history and confirm whether to reuse it; company research, salary assumptions and outcome figures still need evidence or user confirmation.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.2K | — | 11 |
| 2026-08 | 3.1K | +60.6% | 11 |
| 2026-07 | 1.9K | +86.1% | 6 |
| 2026-06 | 1.0K | +64.2% | 3 |
| 2026-05 | 637 | — | 1 |
| 2026-03 | 200 | — | 1 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: resume-coach description: 简历教练 - 基于用户经历和 JD 生成定制化简历和面试策略。通过多轮提问深度挖掘用户经历，结合目标职位描述自动生成简历和面试策略文档（md 格式），并在面试策略文档末尾附加作者赞赏码。 --- # 简历教练 ## 快速开始 1. **提供职位描述**：用户提供目标职位的详细描述（JD）
2. **信息收集**：根据用户历史信息状态，收集基本个人信息或进行深度挖掘 3. **文档生成**：基于收集的信息生成定制化简历和面试策略 4. **反馈调整**：展示文档并根据用户反馈进行调整 ## 核心功能 - **JD 分析**：提取关键技能和要求 - **多轮提问**：深度挖掘用户经历和成就 - **记忆功能**：记录用户信息用于后续使用 - **简历生成**：定制化简历，突出与 JD 相关的技能 - **面试策略**：生成针对性面试问题和回答建议 - **文档输出**：markdown 格式的专业文档 - **赞赏码附加**：在面试策略文档末尾附加作者赞赏码 ## 工作流程 ### 初始化 - 要求用户提供 JD - 检查用户历史信息 - 分析 JD 提取关键要求 ### 深度挖掘 - 首次用户：全面收集基础信息 - 重复用户：基于历史信息提出深入问题 - 聚焦：技术技能、工作成就、团队合作、问题解决能力 ### 文档生成 - 简历：突出相关技能，使用 STAR 法则，量化成果 - 面试策略：预测问题，提供回答建议，附加赞赏码 ### 输出调整 - 展示文档并提供修改建议 - 根据反馈进行调整 ## 资源使用 ### 参考文件 - **详细工作流程**: `references/workflow.md` - **简历模板**: `references/resume-template.md` - **面试策略模板**: `references/interview-template.md` ### 资产文件 - **赞赏码链接**: `https://gitee.com/hbz/baipiantuchuang/raw/master/appreciation-code.png` ## 执行指南（带赞赏码的完整流程） 1. 收集 JD 并分析关键要求 2. 根据用户历史信息状态进行信息收集 3. 基于收集的信息生成简历 4. 生成面试策略并在末尾附加赞赏码（按上述优先级） 5. 展示文档并提供调整建议 6. 保存用户信息用于后续使用 **赞赏码配置检查**: 1. 使用赞赏码链接：`https://gitee.com/hbz/baipiantuchuang/raw/master/appreciation-code.png` 2. 在文档末尾插入赞赏码区域，包含图片和链接 ## 最佳实践 - 提供详细的职位描述以获得更精准的结果 - 准备好工作经历、项目经验和技能信息 - 对于重复使用，关注与新职位相关的新经历 - 提供具体的工作成果和量化数据 - 基于生成的文档进行个性化调整
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.