OpenClaw/clawhub-security-signals huggingface dataset:
Skill: emersonbraun/eb-prd-to-issues (version 1.0.0)

--- name: prd-to-issues description: Break a PRD into independently-grabbable GitHub issues using tracer-bullet vertical slices. Use when user wants to convert a PRD to issues, create implementation tickets, or break down a PRD into work items. metadata: author: EmersonBraun version: "1.0.0" --- # PRD to Issues Break a PRD into independently-grabbable GitHub issues using vertical slices (tracer bullets). ## Process ### 1. Locate the PRD Ask the user for the PRD GitHub issue number (or URL). If the PRD is not already in your context window, fetch it with `gh issue view <number>` (with comments). ### 2. Explore the codebase (optional) If you have not already explored the codebase, do so to understand the current state of the code. ### 3. Draft vertical slices Break the PRD into **tracer bullet** issues. Each issue is a thin vertical slice that cuts through ALL integration layers end-to-end, NOT a horizontal slice of one layer. Slices may be 'HITL' or 'AFK'. HITL slices require human interaction, such as an architectural decision or a design review. AFK slices can be implemented and merged without human interaction. Prefer AFK over HITL where possible. <vertical-slice-rules> - Each slice delivers a narrow but COMPLETE path through every layer (schema, API, UI, tests) - A completed slice is demoable or verifiable on its own - Prefer many thin slices over few thick ones </vertical-slice-rules> ### 4. Quiz the user Present the proposed breakdown as a numbered list. For each slice, show: - **Title**: short descriptive name - **Type**: HITL / AFK - **Blocked by**: which other slices (if any) must complete first - **User stories covered**: which user stories from the PRD this addresses Ask the user: - Does the granularity feel right? (too coarse / too fine) - Are the dependency relationships correct? - Should any slices be merged or split further? - Are the correct slices marked as HITL and AFK? Iterate until the user approves the breakdown. ### 5. Create the GitHub issues For each approved slice, create a GitHub issue using `gh issue create`. Use the issue body template below. Create issues in dependency order (blockers first) so you can reference real issue numbers in the "Blocked by" field. <issue-template> ## Parent PRD #<prd-issue-number> ## What to build A concise description of this vertical slice. Describe the end-to-end behavior, not layer-by-layer implementation. Reference specific sections of the parent PRD rather than duplicating content. ## Acceptance criteria - [ ] Criterion 1 - [ ] Criterion 2 - [ ] Criterion 3 ## Blocked by - Blocked by #<issue-number> (if any) Or "None - can start immediately" if no blockers. ## User stories addressed Reference by number from the parent PRD: - User story 3 - User story 7 </issue-template> Do NOT close or modify the parent PRD issue.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gratefulheart/english-vocabulary-helper (version 1.0.1)

--- name: english-vocabulary-helper description: 中英文双语教育专家，帮助中文母语用户理解和记忆英语单词。当用户需要学习英语单词、查询单词含义、分析词根词缀、记忆英语词汇时使用此技能。触发关键词：学英语、英语单词、单词学习、词汇学习、记单词、词根分析、词缀分析、英语学习。 --- # 英语词汇学习助手 中英文双语教育专家，帮助中文母语用户系统学习、理解和记忆英语单词。 ## ⚠️ 重要：自动保存 **每次学习单词后，必须保存两种格式的文件：** ### Markdown 文件 ``` 保存路径：~/.qclaw/workspace/vocabulary/words/ 文件命名：{单词}.md 示例：context.md, proceed.md ``` ### HTML 互动学习卡片 ``` 保存路径：~/.qclaw/workspace/vocabulary/words/ 文件命名：{单词}.html 示例：context.html, proceed.html ``` **保存流程：** 1. 生成完整的单词学习内容 2. 保存 Markdown 文件到 `vocabulary/words/` 文件夹 3. 生成互动式 HTML 学习卡片，保存到 `vocabulary/words/` 文件夹 4. 自动打开 HTML 文件供用户学习 5. 告知用户保存路径 **文件结构：** ``` ~/.qclaw/workspace/vocabulary/words/ ├── {单词}.md # Markdown 文档 └── {单词}.html # HTML 互动卡片 ``` --- ## 核心任务流程 当用户提供一个英语单词时，按以下顺序完成分析： ### 1. 词义分析 - 用简单易懂的方式解释单词的核心含义 - 区分不同词性下的含义差异 - 标注音标（英式/美式） ### 2. 例句展示 提供 3 个不同场景的例句： - 日常生活场景 - 学术/工作场景 - 技术/专业场景 每个例句附带中文翻译。 ### 3. 词根分析 - 识别并解释单词的词根来源 - 列出由该词根衍生的其他常用单词 - 使用表格展示衍生词 ### 4. 词缀分析 - 前缀：含义及作用 - 后缀：词性变化及含义 - 列出相同词缀的其他单词 ### 5. 发展历史与文化背景 - 单词的造词来源 - 词义演变历史 - 文化内涵与应用场景 ### 6. 单词变形 使用表格展示： - 名词、动词、形容词、副词等形式 - 固定搭配与短语 ### 7. 记忆辅助 提供多种记忆技巧： - 词根词缀拆解记忆 - 场景联想记忆 - 对比记忆（形近词辨析） ### 8. 小故事 用英文撰写一个有画面感的场景故事（100词内），附带中文翻译。 --- ## HTML 互动学习卡片生成 **必须生成包含以下功能的互动 HTML 文件：** ### 必备功能 1. **发音功能** 🔊 - 点击单词听发音 - 点击例句听朗读 - 点击搭配短语听发音 - 使用 Web Speech API 2. **翻转卡片** 🔄 - 3-4 张翻转卡片测试记忆 - 点击翻转查看答案 - "记住了/忘记了" 按钮记录学习效果 - CSS 3D 翻转动画 3. **互动测验** 🎯 - 5 道选择题（放在页面最后） - 实时显示正确/错误/总题数 - 进度条显示答题进度 - 最终得分和评价 - 答题即时反馈（绿色正确/红色错误） 4. **学习统计** 📊 - 正确/错误计数 - 本地存储学习记录（localStorage） ### 页面结构（按顺序） 1. 单词标题 + 音标 2. 核心含义 3. 常用搭配 4. 例句 5. 单词变形 6. 记忆技巧 7. 趣味知识 8. 学习总结 9. 翻转卡片（倒数第三，学完后自测） 10. 词根分析（倒数第二） 11. 互动测验（最后） ### 翻转卡片样式要求 - 使用 `.flip-cards-grid` 包裹，`flex-direction: column; gap: 16px` - `.flip-card-inner` 设置固定高度 `height: 130px`，`position: relative` - `.flip-card-front` 和 `.flip-card-back` 使用 `position: absolute; inset: 0` - 避免使用 `min-height` 导致卡片叠加 - 背面包含：答案标题 + 说明文字 + 记住了/忘记了按钮 ### 设计要求 - 渐变背景，视觉舒适 - 卡片式布局，内容分区清晰 - 响应式设计，适配不同屏幕 - 交互动效，hover 效果 - 重点突出，颜色区分 --- ## 输出格式规范 ### Markdown 文件格式 ```markdown # {单词} - 英语词汇学习 ## 基本信息 | 项目 | 内容 | |------|------| | 单词 | {word} | | 音标 | 英 /xxx/ 美 /xxx/ | | 词性 | n. / v. / adj. | | 含义 | 中文释义 | ## 词根词缀分析 ### 词根 ... ### 词缀 ... ## 核心含义 1. 含义一 2. 含义二 3. 含义三 ## 例句 ### 场景一：日常生活 > 英文例句 > 中文翻译 ### 场景二：学术工作 > 英文例句 > 中文翻译 ### 场景三：技术专业 > 英文例句 > 中文翻译 ## 单词变形 | 词性 | 形式 | 中文 | |------|------|------| | 名词 | ... | ... | | 形容词 | ... | ... | ## 常用搭配 | 搭配 | 含义 | |------|------| | ... | ... | ## 记忆技巧 ... ## 文化背景 ... ## 小故事 **英文：** ... **中文：** ... ## 学习总结 | 要点 | 内容 | |------|------| | 核心含义 | ... | | 词根记忆 | ... | | 常用搭配 | ... | ``` --- ## 注意事项 - 输出格式必须整齐、规范 - 表格列对齐 - 每个部分之间用空行分隔 - 生成后保存 Markdown 和 HTML 两种格式 - 自动打开 HTML 文件 - 告知用户文件保存路径 --- ## 测验题目模板 根据单词内容生成 5 道测验题： 1. 词根相关问题 2. 词性/含义问题 3. 同根词问题 4. 搭配用法问题 5. 文化/趣味知识问题


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: gechengling/finance-content-factory (version 3.0.1)

--- name: Financial Content Factory slug: finance-content-factory description: AI-powered financial content factory — covers content planning, article writing, social media posts, video scripts, and multi-format content generation for financial marketing. Built for financial marketing teams. Keywords: content marketing, article writing, social media, video scripts, financial marketing, 内容工厂, 内容营销, 文章写作, 社媒内容, 视频脚本, 内容创作, 金融文案, 小红书文案, 抖音脚本, 公众号文章. version: "3.0.1" --- # Financial Content Factory / 金融内容工厂 > **English:** AI-powered content factory — covers articles, social media, video scripts. > > **中文:** 内容工厂——覆盖文章、社媒、直播脚本。 --- ### 金融监管最新动态 [2026-05-25更新] | 动态类型 | 内容摘要 | 影响范围 | |---------|---------|---------| | 金融监管 | 2026年Q1：金融内容合规要求提升 | 内容模板库需增加合规标签和风险提示模块 | | 金融监管 | 营销内容需满足消费者保护和合规留存要求 | 内容模板库需增加合规标签和风险提示模块 | | 金融监管 | 产品分级告知和风险提示需纳入内容模板 | 内容模板库需增加合规标签和风险提示模块 | > **数据截止**: 2026-05-25 | 来源：证监会、NFRA、中证协、安永Q1分析 > **声明**: 以上动态供参考，具体以官方最新发布为准 ## Industry Pain Points / 行业痛点 | Pain Point / 痛点 | Impact / 影响 | Solution / 本Skill解决方案 | |------------------|-------------|------------------------| | **内容产出慢** | 营销内容需求大，团队小 | AI批量生成 | | **质量不稳定** | 兼职写作水平参差 | 质量标准化 | | **合规风险** | 金融内容监管严 | 合规检查 | | **形式单一** | 只有图文 | 多格式覆盖 | --- ## Trigger Keywords / 触发关键词 **English Triggers:** content marketing, article writing, social media, video scripts, financial marketing **中文触发词（优先）：** 内容创作 / 内容工厂 / 文章写作 / 社媒内容 / 视频脚本 / 营销文案 / 金融科普 / 内容规划 --- ## Core Capabilities / 核心能力 ### 1. Content Templates / 内容模板 ```python CONTENT_TEMPLATES = { "产品种草文": { "结构": "痛点引入 → 产品介绍 → 核心卖点 → 案例/数据 → CTA", "合规要点": ["风险提示", "收益说明", "产品条款链接"] }, "科普文章": { "结构": "热门话题 → 概念解释 → 深度分析 → 实用建议", "长度": "1500-2000字" }, "社媒短文": { "微博": "观点 + 数据/案例 + 互动引导（150字）", "小红书": "标题 + 干货 + 种草（500字+图）", "抖音": "3秒开场 + 干货 + 互动（60秒内）" } } ``` ### 2. Content Calendar / 内容日历 ```python CONTENT_CALENDAR = { "banking": { "周频率": 5, "内容类型分布": { "产品推广": "20%", "知识科普": "40%", "活动宣传": "20%", "品牌故事": "20%" } }, "insurance": { "周频率": 7, "内容类型分布": { "保障科普": "30%", "产品介绍": "20%", "案例分享": "20%", "热点借势": "15%", "团队/品牌": "15%" } } } ``` --- ## Disclaimer This skill provides content creation tools for educational purposes.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tangweigang-jpg/instructor-structured-output (version 0.1.0)

--- name: instructor-structured-output description: |- Instructor：声明 Pydantic BaseModel 即可从 20 个 LLM provider 拿到类型化实例。核心是 monkey-patch（instructor.patch / from_*）拦截 create()，注入 schema-aware kwargs，tenacity 重试 + Instructor: declare a Pydantic BaseModel and receive a typed instance back from any of 20 LLM providers. Core mechanism is a monkey-patch (instructor.patch / from_*) that intercepts create(), injects schema-aware kwargs, runs the call inside a tenacity retry loop, and rewrites fa license: MIT-0 compatibility: AI engineering knowledge skill — host AI consumes seed.yaml directly. No installation required. metadata: version: "v0.1.0" blueprint_id: "finance-bp-139" blueprint_source: "jxnl/instructor" blueprint_commit: [REDACTED_SECRET] category: ai-engineering doramagic_url: "https://doramagic.ai/zh/crystal/instructor-structured-output" openclaw: skillKey: instructor-structured-output category: ai-engineering primaryEnv: knowledge --- # 这个 skill 适合什么用户？能做哪些任务？ ## 概览 Instructor 是把 Pydantic BaseModel 直接绑到 LLM 输出的 Python 框架（github.com/jxnl/instructor）。核心机制：monkey-patch（instructor.patch / instructor.from_*）拦截 provider client 的 create() 调用，注入 schema-aware kwargs，在 tenacity 重试循环里跑，验证 JSON 响应到模型，ValidationError 时把 failed_attempts 作为 XML 重写 prompt 再试。 支持 20 个 provider... **Doramagic 晶体页**: https://doramagic.ai/zh/crystal/instructor-structured-output ## 知识规模 - **47 条约束** (4 fatal + 43 non-fatal) - 上游源码: `jxnl/instructor` @ commit `3f1d6ddb` - 蓝图 ID: `finance-bp-139` ## 用法 Host AI（Claude Code / Cursor / OpenClaw）读 `references/seed.yaml`，按其中的： - `intent_router` 匹配用户意图 - `architecture` 理解项目架构 - `constraints` 应用 anti-pattern 约束 - `business_decisions` 参考核心设计决策 ## FAQ 摘要 ### 这个 skill 适合什么用户？能做哪些任务？ 适合需要从 LLM 拿到强类型结构化输出的工程师：信息抽取、表单解析、JSON API 直接返回 Pydantic 模型、agent 工具调用参数解析等。20 个 provider 一致 API。访问 doramagic.ai/r/instructor 查看完整用例。 ### 需要准备什么环境？依赖什么？ Python 3.9+（instructor 在 pyproject 中声明 >=3.9）。Pydantic v2 事实上必须（function_calls.py 用 model_validate_json + TypeAdapter，都是 v2-only，v1 在 Partial 路径上 AttributeError）。 ### 会踩哪些坑？这个 skill 怎么防护？ 本 skill 内置 47 条约束（4 条 fatal）。典型踩坑：(1) failed_attempts XML 每次重试线性增长，max_retries=5 可超 context window；(2) from_openai 的 mode 验证用 assert，python -O 下静默剥离； --- 完整文档: 见 `references/seed.yaml` (v6.1 schema). 浏览页: https://doramagic.ai/zh/crystal/instructor-structured-output


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: aipoch-ai/mnemonic-generator (version 1.0.0)

--- name: mnemonic-generator description: Create memory aids for anatomy and pharmacology version: 1.0.0 category: Education tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # Mnemonic Generator Medical memory aid creator. ## Use Cases - Cranial nerve memorization - Drug side effects - Anatomy structures - Biochemistry pathways ## Parameters - `topic`: Subject matter - `items_to_remember`: List - `style`: Acronym/story/visual ## Returns - Custom mnemonics - Explanation of connection - Alternative suggestions - Usage tips ## Example Cranial nerves: "On Old Olympus Towering Tops..." ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ms-yun/excel-report-msy-v1 (version 1.0.0)

--- name: excel-report description: Create formatted Excel report workbooks from CSV files using openpyxl. Use when the user provides or references a .csv file and asks to convert it to .xlsx, make an Excel report, add header styling, comma number formats, numeric total rows, or a simple chart. --- # Excel Report Convert CSV files into polished Excel `.xlsx` reports with the bundled `openpyxl` script. ## Workflow 1. Locate the input CSV file and decide an output `.xlsx` path. Default to the same basename beside the CSV unless the user specifies otherwise. 2. Use the bundled script: ```bash python3 /home/ubuntu/.openclaw/workspace/skills/excel-report/scripts/csv_to_excel_report.py input.csv output.xlsx ``` 3. Verify the workbook was created. For important outputs, inspect the workbook with `openpyxl` or run a quick file check. 4. Return the generated `.xlsx` file path, and attach it with `MEDIA:<path>` when replying in a channel that supports file delivery. ## What the script does - Reads CSV with UTF-8 BOM support and delimiter sniffing. - Creates an Excel workbook with a `Report` sheet. - Styles the header row with a dark blue fill and white bold text. - Converts numeric-looking cells to numbers and applies thousands separators. - Adds a bold total row with `SUM` formulas for numeric columns. - Adds a simple bar chart for the first numeric column using the first column as categories, limited to the first 10 data rows. - Adds an Excel table, filters, frozen header row, and autosized columns. ## Options - Omit the output path to create `input.xlsx` beside the CSV. - Use `--sheet-name "Name"` to set the worksheet title. ## Dependency The script requires `openpyxl`. If it is missing, install it in the active Python environment before running: ```bash python3 -m pip install openpyxl ``` Do not use pandas for the core conversion unless the user explicitly asks; this skill is designed around `openpyxl`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shawn92/ftshare-index-data (version 1.0.0)

--- name: FTShare-index-data description: A 股指数数据技能集（market.ft.tech）。覆盖单只指数详情、指数分页列表（排序/筛选）、指数 K 线（日/周/月/年线）、指数分钟级分时。用户询问某只指数行情、指数列表、指数 K 线或分时时使用。 --- # FT 指数数据 Skills 本 skill 是 `FTShare-index-data` 的**统一路由入口**。 根据用户问题，从下方「能力总览」或「询问方式与子 skill 对应表」匹配对应子 skill，然后通过 `run.py` 执行并解析响应。 > 所有接口均以 `https://market.ft.tech` 为基础域名，请求头需携带 `X-Client-Name: ft-web`（各子 skill 脚本已内置）。 --- ## 调用方式（唯一规则） `run.py` 与本文件（`SKILL.md`）位于同一目录。执行时： 1. 取本文件的绝对路径，将末尾 `/SKILL.md` 替换为 `/run.py`，得到 `<RUN_PY>` 。 2. 调用：`python <RUN_PY> <子skill名> [参数...]` ```bash # 示例（<RUN_PY> 为实际绝对路径） python <RUN_PY> index-description-all python <RUN_PY> index-detail --index 000001.XSHG python <RUN_PY> index-list-paginated --order_by "change_rate desc" --page_size 20 --page_no 1 python <RUN_PY> index-ohlcs --index 000001.XSHG --span DAY1 --limit 50 python <RUN_PY> index-prices --index 000001.XSHG --since TODAY python <RUN_PY> get-nth-trade-date --n 5 ``` > `run.py` 内部通过 `__file__` 自定位，无论安装在何处都能正确找到各子 skill 的脚本。 --- ## 指数 — 询问方式与子 skill 对应表 | 询问方式（用户常说的词） | 子 skill | |------------------------|----------| | **全部指数基础信息**、**指数列表（PB/PE）**、**有哪些指数**、指数 **简称/全称**、**市净率/市盈率 TTM** | `index-description-all` | | 某只 **指数详情**、**上证指数行情**、**沪深300** 点位/涨跌幅、指数名称/成交 | `index-detail` | | **指数列表**、**全市场指数**、**按涨跌幅排序的指数**、**筛选某类指数** | `index-list-paginated` | | 某只指数的 **K 线**、**上证指数日线/周线/月线/年线**、指数 **开高低收**、**MA5/MA10/MA20** | `index-ohlcs` | | 某只指数 **分时**、**上证指数当日分时**、指数 **一分钟行情**、**多日分时走势** | `index-prices` | | **前 N 个交易日**、**近 N 天交易日**、**往前推 N 个交易日**（查近几天 K 线时先调此接口再转时间戳） | `get-nth-trade-date` | --- ## 能力总览 - **`get-nth-trade-date`**：获取当前日期的前 N 个交易日。必填：`--n`（≥1）。查「近 N 天」K 线时先调本接口得到 `nth_trade_date`，再按东八区转为毫秒时间戳用于 index-ohlcs 等。 - **`index-description-all`**：查询全部指数基础信息（symbol、全称、简称、pb、pe_ttm）。无需参数；`GET /data/api/v1/market/data/index-description-all`。 - **`index-detail`**：查询单只指数详情（名称、行情点位、成交、涨跌幅、多周期涨跌幅等）。必填：`--index`；可选 `--masks`。若用户仅给名称，先通过 `index-description-all` 或 `index-list-paginated` 确认代码再查。 - **`index-list-paginated`**：指数分页列表，支持分页、排序、筛选。可选：`--order_by`/`--ob`、`--filter`、`--masks`、`--page_size`、`--page_no`。 - **`index-ohlcs`**：查询单只指数 OHLC K 线（开高低收、成交量、成交额），附带 MA5/MA10/MA20。必填：`--index`、`--span`（DAY1/WEEK1/MONTH1/YEAR1）；可选 `--limit`、`--until_ts_ms`。建议先完成名称到代码映射后再调用。 - **`index-prices`**：查询单只指数分钟级分时价格。必填：`--index`；时间范围二选一：`--since`（TODAY、FIVE_DAYS_AGO、TRADE_DAYS_AGO(n)）或 `--since_ts_ms`。建议先完成名称到代码映射后再调用。 --- ## 使用流程 1. **记录本文件绝对路径**，将 `/SKILL.md` 替换为 `/run.py` 得到 `<RUN_PY>`。 2. **理解用户意图**，从「询问方式与子 skill 对应表」或「能力总览」匹配子 skill 名称。 3. **若用户给的是指数名称/简称**：先调用 `index-description-all` 或 `index-list-paginated` 获取候选，确定标准代码（如 `000001.XSHG`）。 4. （可选）读取 `sub-skills/<子skill名>/SKILL.md` 了解接口与参数。 5. **执行**：`python <RUN_PY> <子skill名> [参数...]`，获取 JSON 输出（详情/K 线/分时统一使用代码参数 `--index`）。 6. **解析并输出**：以表格或要点形式展示给用户；若候选代码不唯一，先让用户确认再查询指标。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tanis90/doc2md (version 1.0.0)

--- name: doc2md description: "Document to Markdown converter - convert DOCX, PPTX, Excel files to Markdown. Use when extracting content from Word documents, PowerPoint presentations, or Excel spreadsheets." homepage: https://mineru.net metadata: {"openclaw":{"emoji":"📑","requires":{"bins":["mineru-open-api"]},"install":[{"id":"npm","kind":"node","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via npm"},{"id":"uv","kind":"uv","package":"mineru-open-api","bins":["mineru-open-api"],"label":"Install via uv"},{"id":"go","kind":"go","package":"github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api","bins":["mineru-open-api"],"label":"Install via go install","os":["darwin","linux"]}]}} --- # Doc2MD - Quick Office Parser Convert Word, PowerPoint, and Excel files to Markdown using MinerU Open API. No API key required. ## Quick Start ```bash # Doc2MD - Quick Office Parser mineru-open-api flash-extract report.docx # Doc2MD - Quick Office Parser mineru-open-api flash-extract slides.pptx # Doc2MD - Quick Office Parser mineru-open-api flash-extract data.xlsx # Doc2MD - Quick Office Parser mineru-open-api flash-extract https://example.com/report.docx # Doc2MD - Quick Office Parser mineru-open-api flash-extract report.docx -o ./output/ ``` ## Language Rule You MUST reply to the user in the SAME language they use. This is non-negotiable. ## Capabilities - Converts DOCX, PPTX, XLS, XLSX to Markdown - Supports both local files and URLs directly - Preserves text, tables, and document structure - No API key, no signup, no authentication - Max 10MB / 20 pages per document ## When to Use - User asks to "read", "extract", or "convert" a Word/PowerPoint/Excel file - User shares a .docx, .pptx, or .xlsx and asks for its content - User wants to summarize or analyze an office document - User needs document content in Markdown format ## CLI Reference Run `mineru-open-api flash-extract --help` for all available options. ## Data Privacy - `flash-extract` uploads the document to MinerU's cloud API for processing and returns the result. No account or API key is required. - Documents are processed in real-time and are not stored after extraction. - For details, see https://mineru.net ## Notes - Output is Markdown only; embedded images may be replaced with placeholders - For larger files (up to 200MB/600 pages) or precision extraction, use `mineru-open-api extract` (requires auth via `mineru-open-api auth`) - If the CLI cannot be installed via npm/uv/go, download it from https://mineru.net/ecosystem?tab=cli


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1970168137/promotion-planning (version 1.0.0)

--- name: promotion-planning description: "Develop promotion strategies and discount recommendations based on historical data analysis" --- # Promotion Planning ## Overview This SKILL provides professional Sales Management capabilities to help businesses streamline operations and improve outcomes. ## Capabilities - Develop promotion strategies and discount recommendations based on historical data analysis - Industry best practices and standardized workflows - Data-driven insights and recommendations - Compliance and risk management considerations ## Use Cases ### Primary Applications - Business operations optimization - Strategic decision support - Process automation and standardization - Performance monitoring and improvement ### Industry Applications - Enterprise-level implementations - Small and medium business solutions - Cross-functional team collaboration - Vendor and partner management ## Input Requirements **COPY and paste the following content as input:** ``` [Please provide your specific business context, data, or requirements here] ``` ### Required Information - Business context and objectives - Relevant data or documents - Specific requirements or constraints - Target audience or stakeholders ### Optional Information - Historical data or benchmarks - Competitive landscape - Regulatory requirements - Budget and timeline constraints ## Output Format The SKILL will provide: 1. **Analysis & Insights** - Data-driven observations and recommendations 2. **Actionable Deliverables** - Ready-to-use documents, scripts, or plans 3. **Implementation Guidance** - Step-by-step execution instructions 4. **Best Practices** - Industry standards and proven methodologies ## Best Practices - Always provide complete context for optimal results - Review outputs for business-specific customization - Validate recommendations against your specific industry standards - Iterate based on feedback and performance metrics ## Limitations - Requires accurate and complete input data - Outputs should be reviewed by domain experts - May need customization for highly specialized industries - Does not replace professional legal, financial, or medical advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)