快速判断
- 01这个 Skill 是干嘛的
- 把 Excel 分析、演示、图像和研究等办公任务封装成可执行的端到端 skills。
- 02它能解决什么问题
- 解决办公任务步骤分散、工具调用不稳定和结果难以形成完整交付物的问题。
- 03它适合谁来用
- 适合需要 Agent 处理表格、PPT、研究和视觉内容的个人与企业用户。
预览 · README.md
中文为 README 译文；English 保留 GitHub README 原文。
SenseNova-Skills
简体中文 | English
<p align="center"> <img src="docs/images/teasers/teaser_v2.webp" width="100%"> </p>
<p align="center"> <a href="https://platform.sensenova.cn"><img src="https://img.shields.io/badge/%E5%AE%98%E7%BD%91-Platform-1f6feb?style=flat-square&logo=googlechrome&logoColor=white" alt="官网"></a> <a href="https://office.xiaohuanxiong.com/home"><img src="https://img.shields.io/badge/%F0%9F%A6%9D_%E5%B0%8F%E6%B5%A3%E7%86%8A-%E5%85%8D%E8%B4%B9%E4%BD%93%E9%AA%8C-f29415?style=flat-square" alt="小浣熊"></a> <a href="https://platform.sensenova.cn/token-plan"><img src="https://img.shields.io/badge/Token_%E5%A5%97%E9%A4%90-%E5%85%8D%E8%B4%B9-2ea44f?style=flat-square&logo=opensea&logoColor=white" alt="Token 套餐"></a> <a href="https://github.com/OpenSenseNova/SenseNova-U1"><img src="https://img.shields.io/badge/SenseNova-U1-8957e5?style=flat-square&logo=github&logoColor=white" alt="SenseNova U1"></a> <a href="https://github.com/OpenSenseNova/SenseNova6.7"><img src="https://img.shields.io/badge/SenseNova-6.7-cf222e?style=flat-square&logo=github&logoColor=white" alt="SenseNova 6.7"></a> </p>
SenseNova 系列模型可直接接入 OpenClaw、hermes-agent 等智能体；本仓库的 skills 则把这些模型扩展为可直接落地的端到端办公能力。
本项目每个技能位于独立目录中，通过 SKILL.md 声明触发条件、能力边界和执行方式，遵循 Agent Skills 规范。
技能覆盖 图像生成与可视化、演示文稿生成、Excel 数据分析、深度研究 等场景，可独立使用，也可组合成端到端工作流。
🎨 想看它到底能干啥？ 点击逛 sn-infographic 案例画廊，探索近 100 个有趣生成案例，顺便 “ 偷师 ”一下 Prompt 应该怎么写！
🦝 在小浣熊中开箱即用
本仓库的最新模型与全系 Cowork-Skill，已整体集成进 小浣熊，提供企业级安全防护与开箱即用的丝滑体验——如果你不想自己搭环境、配 API key，可以直接通过小浣熊使用这些能力。支持免费试用，无需付费即可上手体验。
小浣熊本次迎来产品能力与客户端体验的全面升级：
- 三大核心办公能力全面增强：依托 SenseNova 6.7 Flash 与 Cowork-Skill，数据分析、PPT 生成、任务规划进一步强化，覆盖多文件清洗分析、正式汇报 PPT、行业研究 / 竞品分析 / 投研报告等复杂知识工作的完整闭环。
- 新增信息图生成功能：基于 SenseNova U1 模型，将复杂数据、长篇报告与业务洞察压缩为高密度、结构化、视觉化的信息图，让复杂内容更易理解、更适合传播。
- 全新客户端 + 本地 Agent OS：云端模型负责复杂推理与多模态理解，本地 Agent OS 围绕本地文件、工作上下文与个人使用习惯，带来更个性化、本地化、安全化的 AI 原生办公体验。
- 规模化验证：1500 万个人用户、数千家企业用户的共同选择。
👉 立即体验：xiaohuanxiong.com
如何使用
本仓库的 skill 需要配合支持 Agent Skills 规范的智能体使用。
- 推荐运行时：OpenClaw 或 hermes-agent。
- 推荐 LLM：配合使用 SenseNova 平台 API（提供免费 token 套餐）。
- 安装与配置：完整流程请参考
INSTALL_CN.md。
推荐做法：直接让 agent 帮你装好这些 skill。 把仓库地址交给它，让它自己克隆并把内容拷贝到目标目录，例如：
"请帮我把 https://github.com/OpenSenseNova/SenseNova-Skills 安装到你的 skills 目录。"
安装完成后，可能需要手动重启 agent 服务，新 skill 才会被加载。
| 智能体 | 目标目录 |
|---|---|
| OpenClaw | ~/.openclaw/skills/ |
| hermes-agent | ~/.hermes/skills/ |
<details> <summary>想手动安装？</summary>
克隆本仓库，然后把 skills/ 下的子目录自行复制（或软链接）到目标目录：
git clone https://github.com/OpenSenseNova/SenseNova-Skills.git --depth=1
mkdir -p ~/.openclaw/skills
cp -r SenseNova-Skills/skills/* ~/.openclaw/skills/Hermes 把目录换成 ~/.hermes/skills/ 即可。
</details>
各分类技能的 Python 依赖、API key 与调用示例同样请参考对应分类的 📖 详细使用指南。
技能列表
🎨 图像与可视化
📖 详细使用指南：docs/sn-image-generate.md（环境要求、Quick Start、API 配置与调用样例）。
| 名称 | 标签 | 描述 |
|---|---|---|
sn-image-doctor | 环境诊断 | 检查 SenseNova-Skills 环境，验证 sn-image-base 安装、Python 依赖与必填环境变量；交互式补齐缺失项并写入 .env。 |
sn-image-base | 图像基础层（Tier 0） | 提供文生图（sn-image-generate）、图像识别（sn-image-recognize）与文本优化（sn-text-optimize）三个底层工具，统一通过 sn_agent_runner.py 调用，供上层技能复用。 |
sn-infographic | 信息图生成（Tier 1） | 自动评估提示词、从 87 种布局 / 66 种风格中选型，多轮生成 + VLM 评审 + 质量排序，输出专业级信息图。 |
sn-image-imitate | 图像风格模仿（Tier 1） | 给定一张参考图像和目标内容描述，模仿其风格生成新图像。 |
sn-image-resume | 简历图片生成（Tier 1） | 给定一份简历信息，生成简历图片。 |
📊 演示文稿（PPT）
📖 详细使用指南：docs/sn-ppt-generate_cn.md（环境要求、Quick Start、API 配置与调用样例）。
| 名称 | 标签 | 描述 |
|---|---|---|
sn-ppt-entry | PPT 入口 | PPT 生成功能的统一入口，先请用户选择快速 / 标准 / 创意模式，再收集角色 / 受众 / 场景 / 页数。标准模式下进一步询问图像来源（AI 生成 / 网络搜索 / 纯文字）和图表渲染方式（U1 信息图 / ECharts）。解析 pdf / docx / md / txt 输入，产出 task_pack.json + info_pack.json 并分派到下游模式。快速模式跳过可选提问，直接进入构建。 |
sn-ppt-doctor | PPT 环境诊断 | PPT 流水线的环境检查，验证 sn-image-base、API key、Node 运行时与可选依赖；按需写入 .env。 |
sn-ppt-creative | PPT 创意模式 | 每页一张 16:9 全图（PNG），按页面构图 prompt 走 sn-image-generate 一次性出图。T2I 生成失败时自动回退到网络图片搜索。 |
sn-ppt-standard | PPT 标准与快速 | style_spec → 大纲 → 资产规划 + 分槽位图像 + VLM 质检 → 分页 HTML → 分页评审 → 导出 PPTX。快速模式自主决策、即刻构建完整草稿，完成后提供结构化修改建议。标准模式包含风格预览确认环节。支持 U1 智能生成流程图与数据图表，支持 Serper 网络图片搜索。 |
📈 数据分析（DA）
📖 详细使用指南：docs/sn-data-analysis_cn.md（环境要求、Quick Start、API 配置与调用样例）。
| 名称 | 标签 | 描述 |
|---|---|---|
sn-da-excel-workflow | Excel 分析编排 | Excel 多表读取、大文件检测（≥10k 行触发 Parquet 优化）、清洗、条件过滤、跨表聚合、Excel/CSV 导出的全流程编排。 |
sn-da-image-caption | 图像理解与数据提取 | 图像类输入做表格 OCR / 图表解读 / 截图描述 / UI 描述；可解析为 DataFrame、复绘可视化、导出 Excel/CSV。 |
sn-da-large-file-analysis | 大文件高性能分析 | ≥10k 行 Excel 的流式读取（openpyxl read_only + iter_rows）、Parquet 转换、内存优化、分块处理与大文件写入模式。 |
🔬 深度研究
📖 详细使用指南：docs/sn-deep-research_cn.md（环境要求、web_search 硬检查、Quick Start 与各阶段调用）。
| 名称 | 标签 | 描述 |
|---|---|---|
sn-deep-research | 深度研究入口 | 深度研究功能的统一入口，支持真实依赖 DAG、来源快照复用和 evidence-informed content units，最终产出 report.md。 |
sn-research-report | 终稿写作 / 改写 | 把判断层落成最终 report.md；也可对已有报告做重写、润色、重组结构、补充表格等定向编辑。 |
sn-report-format-discovery | 最终呈现形式发现 | 比较研究报告、学术论文、表格优先报表、决策备忘录或自定义 Markdown 形式；由 scout 在研究前调用并交给用户确认。 |
sn-prepare-citations | 引用渲染 | 将 [^source_id] 脚注后处理为编号引用，并基于 evidence sources 追加参考文献。 |
sn-md-to-html-report | Markdown → HTML 报告 | 把研究产出的 report.md（或任意 Markdown 文档）转换成单文件、可离线打开的 HTML 阅读视图——内嵌图片、侧栏目录、自适应表格，并自动修复表格分隔符。 |
🔍 搜索
📖 搜索技能与深度研究合并在同一份文档：docs/sn-deep-research_cn.md（含各平台 API key、调用方式与统一 JSON 输出）。
| 名称 | 标签 | 描述 |
|---|---|---|
sn-search-academic | 学术搜索 | ArXiv（含 HTML 全文按章节读）/ Semantic Scholar（含引用数）/ PubMed（含 PMC 开放获取全文）/ Wikipedia 四平台聚合。 |
sn-search-code | 开发者搜索 | GitHub（仓库 / 代码 / Issue）/ Stack Overflow / Hacker News / HuggingFace（模型 / 数据集 / Space）四平台聚合。 |
sn-search-social-cn | 中文社交搜索 | B 站 / 知乎 / 抖音 三个中文社交平台搜索；部分平台需 cookie 认证。 |
sn-search-social-en | 英文社交搜索 | Reddit / Twitter (X) / YouTube 三个英文社交平台搜索。 |
输出样例
🎨 信息图（sn-infographic）
sn-infographic 的部分生成效果（更多样例见 docs/sn-infographic-examples_CN.md）。
<img src="docs/images/teasers/cases_merge.webp" alt="sn-infographic sample outputs">
🧩 内存价格分析 — 洞察-分析-汇报-全链路
examples/memory-price-end2end-analysis。智能体先对原始报价 CSV 做字段刻画、品类与时间戳标准化，然后从「整体走势」「分品类涨幅 Top」「服务器级 vs 消费级背离」三个角度刻画本轮上涨，沿途定位 2 月下旬的拐点。把数据结论作为新的研究问题，转入深度调研：按维度规划检索（供给收缩、AI 服务器需求、原厂控产），并在不同来源之间交叉验证证据后再写入报告。数据 + 研究结论一并交给 PPT 生成：先排 16 页大纲、规划每页素材，再生成分页 HTML、做 VLM 评审、最后把分页截图合成 PPTX。最终是一条清晰的三段叙事：价格在涨 → 为什么涨 → 怎么应对。这是仓库里唯一一个完整跑过 数据分析 → 深度调研 → PPT 的端到端样例。
📊 员工绩效分析 — 数据分析
examples/employee-performance-analysis。智能体先把 10 份分散的月度考核 xlsx 读入，对齐各月列结构，纵向拼成一张长表。在这张表上分别做总体视角（月度均值趋势、得分分布箱线图、等级占比变化、38 个岗位排名）和个体视角（优秀 / 待提升 / 持续进步三类员工，配合个人年度走势）的分析。结论部分把改进建议落到具体岗位和具体员工，并用 8 张图表佐证。同样的内容产出 Word 版（适合下发）和可视化 HTML 版（适合浏览）两种形态。这个样例展示了 sn-da-excel-workflow 如何把「一堆零散的小表」当成一次完整分析来处理。
- 依赖技能：
sn-da-excel-workflow
🔬 具身智能行业调研 — 深度调研
examples/embodied-ai-deep-research。给定一个行业关键词后，智能体先列出研究维度（市场规模、玩家份额、融资、成本结构、发展路线），而不是直接撒网搜索。每个维度按计划做定向检索、抓取并阅读原始页面，提取数值与定性证据；不同来源之间出现冲突的数字会先做 reconcile 再落到报告里。综合阶段按读者任务把各维度证据组织成可追溯的信息结构，而不是一堆互不连接的要点。最终产出是一份图文并茂的报告（Markdown + 可视化 HTML），含 5 张分维度的配图。这个样例展示了 sn-deep-research 如何把一句「调研 X」变成「先规划再执行、证据可追溯」的结构化闭环。
- 依赖技能：
sn-deep-research
🎯 物业费定价体系 — PPT 生成
examples/property-fee-pricing-ppt。智能体读到一份开放式输入（主题：物业费定价；受众：物业管理人员 + 物业委员会；26 页；黑白温馨风），先确定大纲，再产出符合风格规范的逐页素材计划。每一页以语义化的 HTML 方式构造，而不是直接出整页大图：文案、版式、配图、图标、需要的数据图表都是分槽位规划的。素材按槽位生成或选型，并由 VLM 对照页面意图做质检；每页 HTML 渲染出来后再走一轮评审与按需改写，保证用语和视觉一致性。最后把分页截图合成 PPTX，分页 HTML 也保留下来，便于直接在浏览器里预览或继续修改。这个样例展示了 sn-ppt-standard 在一份偏文本、长篇幅的方案稿上如何在每一页都遵守同一套受众和配色约束。
常见问题
接入与运行中的常见问题（400/401 报错、限流、PPT 超时、信息图质量、模型名等）解答见 docs/faq_CN.md。
贡献
欢迎以本仓库的技能为模板创建你自己的 OpenClaw 技能。一个好技能的核心要素：
- 清晰的触发条件：在
description中写明"什么时候用 / 什么时候不用"，让智能体准确识别 - 聚焦的能力边界：每个技能只把一件事做好，复杂工作流通过多个技能编排实现
- 完善的文档：包含示例、产物约定、边界情况与失败处理
- 必要的支撑资源：通过
references/、scripts/、prompts/提供补充上下文
加入社区
欢迎加入我们的交流群，分享反馈、获取支持，并第一时间了解最新进展。扫描下方二维码即可加入——期待你的声音！
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许可证
MIT — 详见 LICENSE。
SenseNova-Skills
English | 简体中文
<p align="center"> <img src="docs/images/teasers/teaser_v2.webp" width="100%"> </p>
<p align="center"> <a href="https://platform.sensenova.cn"><img src="https://img.shields.io/badge/Website-Platform-1f6feb?style=flat-square&logo=googlechrome&logoColor=white" alt="Website"></a> <a href="https://office.xiaohuanxiong.com/home"><img src="https://img.shields.io/badge/%F0%9F%A6%9D_Raccoon-Try%20it%20free-f29415?style=flat-square" alt="Raccoon"></a> <a href="https://platform.sensenova.cn/token-plan"><img src="https://img.shields.io/badge/Token_Plan-Free-2ea44f?style=flat-square&logo=opensea&logoColor=white" alt="Token Plan"></a> <a href="https://github.com/OpenSenseNova/SenseNova-U1"><img src="https://img.shields.io/badge/SenseNova-U1-8957e5?style=flat-square&logo=github&logoColor=white" alt="SenseNova U1"></a> <a href="https://github.com/OpenSenseNova/SenseNova6.7"><img src="https://img.shields.io/badge/SenseNova-6.7-cf222e?style=flat-square&logo=github&logoColor=white" alt="SenseNova 6.7"></a> </p>
The SenseNova model family plugs directly into agent runtimes such as OpenClaw and hermes-agent, with the skills in this repository extending the models with concrete, end-to-end office capabilities.
In this repository each skill lives in its own directory and declares triggers, capabilities, and execution flow through a SKILL.md file, following the Agent Skills convention.
The skills cover image generation & visualization, slide-deck (PPT) generation, Excel data analysis, and deep research — usable standalone or composed into end-to-end workflows.
🎨 Want to see what it can do? Check out our sn-infographic Gallery to explore nearly 100 stunning generation cases and steal their prompt designs !
🦝 Available out-of-the-box in Raccoon
The latest SenseNova models and the full Cowork-Skill suite in this repo are bundled into Raccoon, with enterprise-grade security and a zero-setup experience — if you'd rather not provision env, API keys, and runtimes yourself, you can use these capabilities directly through Raccoon. Free trial available — no payment required to get started.
Raccoon now ships a full upgrade across product capability and client experience:
- Three core office capabilities, strengthened: powered by SenseNova 6.7 Flash + Cowork-Skill, data analysis, PPT generation, and task planning each take a step up — covering the full loop from multi-file cleaning/analysis to formal report decks, industry/competitive research, and investment memos.
- New: infographic generation: built on the SenseNova U1 model, compresses complex data, long reports, and business insights into dense, structured, visual infographics that are easier to digest and share.
- New client + local Agent OS: the cloud model handles heavy reasoning and multimodal understanding; the local Agent OS sits next to your files, work context, and personal habits — delivering a more personalized, local, and secure AI-native office experience.
- Proven at scale: chosen by 15M+ individual users and thousands of enterprise customers.
👉 Try it: xiaohuanxiong.com
How to Use
These skills are designed to run inside an Agent Skills-compatible agent.
- Recommended runtime: pair them with OpenClaw or hermes-agent.
- Recommended LLM: pair them with the SenseNova Platform API — a free token plan is available.
- Install & configure: follow the full walkthrough in
INSTALL.md.
Recommended: let the agent install the skills for you. Hand it the repo URL and ask it to clone and drop the skills into the right directory — for example:
"Please install SenseNova-Skills from https://github.com/OpenSenseNova/SenseNova-Skills into your skills directory."
After it finishes, you may need to manually restart the agent service before the new skills are picked up.
| Agent | Target directory |
|---|---|
| OpenClaw | ~/.openclaw/skills/ |
| hermes-agent | ~/.hermes/skills/ |
<details> <summary>Prefer to install manually?</summary>
Clone this repository, then copy the subdirectories under skills/ into the target directory yourself:
git clone https://github.com/OpenSenseNova/SenseNova-Skills.git --depth=1
mkdir -p ~/.openclaw/skills
cp -r SenseNova-Skills/skills/* ~/.openclaw/skills/For Hermes, swap the target to ~/.hermes/skills/.
</details>
Per-category Python dependencies, API keys, and invocation examples are documented in the 📖 Full guide for each section.
Skills List
🎨 Image & Visualization
📖 Full guide: docs/sn-image-generate_en.md (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
|---|---|---|
sn-image-doctor | Environment Doctor | Validates the SenseNova-Skills environment — checks sn-image-base install, Python deps, and required env vars; interactively fills missing values into .env. |
sn-image-base | Image Base Layer (Tier 0) | Low-level tools — text-to-image (sn-image-generate), image recognition (sn-image-recognize), and text optimization (sn-text-optimize) — exposed through a unified sn_agent_runner.py, designed to be called by upper-layer skills. |
sn-infographic | Infographic Generation (Tier 1) | Auto prompt-quality scoring, layout/style selection (87 layouts / 66 styles), multi-round generation with VLM review and quality ranking, producing publication-ready infographics. |
sn-image-imitate | Image Imitation (Tier 1) | Given one reference image and a target content prompt, generates a new image that imitates the reference. |
sn-image-resume | Resume Image Generation (Tier 1) | Given resume information, generates a resume image. |
📊 Presentations (PPT)
📖 Full guide: docs/sn-ppt-generate.md (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
|---|---|---|
sn-ppt-entry | PPT Entry Point | Unified entry point for PPT generation. Asks the user to choose fast, standard, or creative mode, then collects role / audience / scenario / page count. For standard mode, also asks about image sourcing (AI, web search, or none) and chart rendering (U1 infographics or ECharts). Parses uploaded pdf / docx / md / txt, emits task_pack.json + info_pack.json, and dispatches to the chosen mode. |
sn-ppt-doctor | PPT Environment Doctor | Environment check for the PPT pipeline — validates sn-image-base, API keys, the Node runtime, and optional deps; writes missing required vars into .env. |
sn-ppt-creative | PPT Creative Mode | One full-page 16:9 PNG per slide, generated via sn-image-generate with a per-page composed prompt. Falls back to web image search when T2I generation fails. |
sn-ppt-standard | PPT Standard & Fast | style_spec → outline → asset plan + per-slot images + VLM QC → per-page HTML → per-page review → PPTX export. Fast mode builds a complete draft immediately with autonomous decisions, then provides structured refinement suggestions. Supports AI-generated infographics (U1) for diagrams and web image search (Serper) for real photos. |
📈 Data Analysis (DA)
📖 Full guide: docs/sn-data-analysis.md (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
|---|---|---|
sn-da-excel-workflow | Excel Analysis Orchestration | End-to-end Excel pipeline — multi-sheet read, large-file detection (≥10k rows triggers Parquet), cleaning, conditional filtering, cross-sheet aggregation, and Excel/CSV export. |
sn-da-image-caption | Image Understanding & Data Extraction | For image-first inputs — table OCR, chart understanding, screenshot/UI description; parses captions into DataFrames, recreates visualizations, exports Excel/CSV. |
sn-da-large-file-analysis | High-Performance Large-File Analysis | Streaming reads for ≥10k-row Excel datasets (openpyxl read_only + iter_rows), Parquet conversion, memory optimization, chunked processing, large-file writes. |
🔬 Deep Research
📖 Full guide: docs/sn-deep-research.md (prerequisites, web_search precheck, Quick Start, and per-stage invocation).
| Name | Label | Description |
|---|---|---|
sn-deep-research | Deep Research Entry Point | Unified deep-research orchestrator with true-dependency DAGs, reusable source snapshots, and evidence-informed content units, producing final report.md. |
sn-research-report | Final Report Writing & Editing | Renders the judgment layer into the final report.md; also handles targeted rewrites — restructuring, polishing, table-augmentation — for an existing draft. |
sn-report-format-discovery | Presentation-Format Discovery | Compares final forms such as a research report, academic paper, table-first analysis, decision memo, or a custom Markdown form; scout uses it before research and user confirmation. |
sn-prepare-citations | Citation Rendering | Post-processes [^source_id] footnotes into numbered citations and appends references from evidence sources. |
sn-md-to-html-report | Markdown → HTML Report | Converts the research report.md (or any Markdown doc) into a clean, single-file HTML reading view that opens offline — embedded images, side-panel TOC, responsive tables, and table-delimiter repair. |
🔍 Search
📖 Search skills are documented together with deep research: docs/sn-deep-research.md (includes per-platform API keys, invocation, and unified JSON output).
| Name | Label | Description |
|---|---|---|
sn-search-academic | Academic Search | ArXiv (with section-level HTML reading) / Semantic Scholar (with citation counts) / PubMed (with PMC open-access full text) / Wikipedia, in one aggregated interface. |
sn-search-code | Developer Search | GitHub (repo / code / issue) / Stack Overflow / Hacker News / HuggingFace (models / datasets / spaces), aggregated. |
sn-search-social-cn | Chinese Social Search | Bilibili / Zhihu / Douyin search; some platforms require cookie auth. |
sn-search-social-en | English Social Search | Reddit / Twitter (X) / YouTube search. |
Sample Outputs
🎨 Infographic (sn-infographic)
A few sn-infographic outputs (more in docs/sn-infographic-examples.md).
<img src="docs/images/teasers/cases_merge.webp" alt="sn-infographic sample outputs">
🧩 Memory price analysis — insight → analysis → presentation → end-to-end workflow
examples/memory-price-end2end-analysis. Starting from a raw quote CSV, the agent profiles fields, normalizes categories and timestamps, then attacks the rally from three angles — overall trend, top movers per category, and the gap between server-grade and consumer-grade SKUs — locating a late-February inflection along the way. Treating those findings as the research question, it switches to deep research: planning per-dimension web searches over supply contraction, AI-server demand, and vendor output discipline, then triaging and cross-checking evidence across sources before committing it to the report. The data and research conclusions are then handed to PPT generation, which lays out a 16-page outline, plans per-slot imagery, renders per-page HTML, runs VLM review, and finally composites screenshots into the PPTX. The result is a clear three-step storyline: prices are rising → here is why → here is what to do. This is the only example that exercises the full data analysis → deep research → PPT chain end-to-end.
- Depends on:
sn-da-excel-workflow,sn-deep-research,sn-ppt-entry,sn-ppt-standard,sn-md-to-html-report
📊 Employee performance analysis — data analysis
examples/employee-performance-analysis. The agent reads 10 separate monthly review xlsx files, aligns column schemas across months and joins them into one longitudinal table. From that table it produces aggregate views — monthly average trend, score-distribution boxplots, grade mix change, and a 38-role ranking — and individual views — top performers, needs-attention, and consistently-improving cohorts plus per-employee year trends. The findings are written up with explicit improvement suggestions tied to specific roles and individuals, backed by 8 supporting charts. The same content is delivered as a Word doc (for distribution) and a visualized HTML report (for browsing). The example shows how sn-da-excel-workflow handles "many small spreadsheets that should be one analysis" rather than a single big file.
- Depends on:
sn-da-excel-workflow
🔬 Embodied AI industry research — deep research
examples/embodied-ai-deep-research. Given only an industry name, the agent first commits to a research plan — market size, vendor share, financing, cost structure, development roadmap — instead of jumping straight into search. For each dimension it runs targeted web searches, fetches and reads source pages, and extracts both numeric and qualitative evidence; conflicting figures across sources are explicitly reconciled before being trusted. A synthesis stage organizes per-dimension evidence into a traceable, reader-oriented information structure rather than a stack of disconnected bullets. The output is an illustrated report (Markdown + visualized HTML) with 5 dimension-specific charts. The example shows how sn-deep-research turns "go research X" into a structured plan-then-execute loop with traceable evidence.
- Depends on:
sn-deep-research
🎯 Property fee pricing — PPT generation
examples/property-fee-pricing-ppt. The agent takes a free-form brief — topic (property fee pricing), audience (property staff + committee), 26 pages, black-and-white warm style — and first commits to an outline plus a per-page asset plan that conforms to the style spec. Each slide is then built as semantic per-page HTML rather than free-form image generation: copy, layout, illustrations, icons, and any data charts are reasoned about per slot. Imagery is produced or selected per slot and VLM-checked against the page's intent; each rendered page goes through a review pass with optional rewrite for coherence and copy quality. Final pages are screenshotted and composited into the PPTX, with the per-page HTML kept alongside for direct browser preview or re-editing. The example demonstrates sn-ppt-standard style consistency on a long, prose-heavy deck where every slide must obey the same audience and palette constraints.
- Depends on:
sn-ppt-entry,sn-ppt-standard
FAQ
Common setup and runtime questions (400/401 errors, rate limits, PPT timeouts, infographic quality, model names) are answered in docs/faq.md.
Contributing
Feel free to use the skills here as templates for your own OpenClaw skills. The qualities that make a skill good:
- Clear triggers: state in
descriptionexactly when the skill should and should not run, so the agent recognizes it accurately - Focused scope: each skill does one thing well; complex workflows compose multiple skills
- Solid documentation: examples, artifact contracts, edge cases, failure handling
- Supporting resources: use
references/,scripts/,prompts/to provide additional context
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License
MIT — see LICENSE.