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SenseNova-Skills
OpenSenseNova/SenseNova-Skills
Modular SenseNova skills for building AI-powered office assistants and productivity workflows
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Install the skills
npx skills add OpenSenseNova/SenseNova-SkillsInstalls into Claude Code, Codex, Cursor, OpenCode and the other agents it finds on your machine.
git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git /tmp/sensenova-skills
cp -r /tmp/sensenova-skills ~/.claude/skills/sensenova-skillsClaude Code reads ~/.claude/skills/ (or .claude/skills/ in a project). Other agents read the same SKILL.md format from their own skills folder.
Inside this repo
48 of 84 skills
skills/sn-code-videosn-da-excel-workflowExcel 数据分析多步编排器。覆盖:(1) 读取多 Sheet Excel 文件并统计行数,(2) 大文件检测(≥10k 行自动 Parquet 优化),(3) 数据清洗(缺失值、文本标准化、无效字符),(4) 条件筛选与分类提取,(5) 跨 Sheet 统计聚合,(6) 导出 Excel/CSV 并提供下载链接。覆盖从数据读取到报告生成全流程,按步骤编排 capability 子 skill。**遇到以下任一情况就主动使用本 skill,不要自行写几行 pandas 就回答**:①用户出现触发词:Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数skills/sn-da-excel-workflowlarge-file-parquet-analysis-and-highlight当Excel文件总行数超过1万行时,通过转换为Parquet格式提升读取性能,提取目标指标并计算最大值,最后将结果输出为Excel并对特定行进行高亮标注。skills/sn-da-excel-workflow/capability/excel-cell-coloring/category-coloringexcel-conditional-comparison-and-large-file-processing对比Excel多表中的特定系数并对异常值进行颜色标记。skills/sn-da-excel-workflow/capability/excel-cell-coloring/duplicate-value-coloringexcel-outlier-detection-and-highlighting识别 Excel 中的超限数值与错误单元格并进行高亮标注。skills/sn-da-excel-workflow/capability/excel-cell-coloring/outlier-coloringlarge-file-conditional-formatting根据Excel总行数自动切换Parquet加速读取,计算特定维度的时间序列平均值,并使用openpyxl输出带有条件格式(如低于均值标绿)和自定义样式的分析报告。skills/sn-da-excel-workflow/capability/excel-cell-coloring/threshold-cell-coloringtop-value-coloring根据数据规模动态选择处理策略,对多表数据进行合并、统计筛选,并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。skills/sn-da-excel-workflow/capability/excel-cell-coloring/top-value-coloringnumeric-extraction-and-distribution-analysis从带单位的字符串列中提取数值并清洗,生成包含直方图、饼图、条形图和累积分布图的多维度综合分布可视化图表,用于展示数据的集中趋势与分布特征。skills/sn-da-excel-workflow/capability/excel-conditional-formatting/data-bar-formattingcategorical-comparison-analysis对两类分类数据进行对比分析,统计数量差异与比例关系并生成可视化图表。skills/sn-da-excel-workflow/capability/excel-data-analysis/comparison-analysisgroup-by-analysis对多 Sheet 的 Excel 文件进行行数统计、大文件 Parquet 转换预处理、数据清洗及分组聚合分析,并生成带样式标记的统计表与可视化图表。skills/sn-da-excel-workflow/capability/excel-data-analysis/group-by-analysislarge-file-kpi-analysis根据数据量自动选择读取策略(大文件转Parquet),提取关键指标进行单位一致性验证与排序分析,并输出可下载的结果表格。skills/sn-da-excel-workflow/capability/excel-data-analysis/kpi-metric-analysispivot-table-cross-analysis利用交叉表与热力图对分类数据进行多维度占比分析,适用于奖项分布、绩效评估或市场占有率等结构化数据的清洗与可视化。skills/sn-da-excel-workflow/capability/excel-data-analysis/pivot-table-analysistime-series-and-categorical-analysis对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测,并生成高分辨率的可视化综合报告,适用于业务指标监控与预测场景。skills/sn-da-excel-workflow/capability/excel-data-analysis/time-series-analysistrend-analysis基于多维度数据进行分级评估与趋势预测,通过设定差异化增长率计算预测值,并生成对比可视化图表,适用于绩效评估、目标设定等场景。skills/sn-da-excel-workflow/capability/excel-data-analysis/trend-analysisexcel-multi-sheet-threshold-analysis统计多Sheet Excel总行数并根据规模选择处理策略,提取特定维度信息进行去重统计,并生成摘要与明细报表。skills/sn-da-excel-workflow/capability/excel-data-cleaning/duplicate-removalinvalid-data-cleaning用于大规模Excel数据的预处理,通过统计总行数判断是否转换为Parquet格式以提升读写效率,并使用正则表达式清洗指定文本列(如仅保留中文字符),最后导出清洗后的文件并提供下载链接。skills/sn-da-excel-workflow/capability/excel-data-cleaning/invalid-data-cleaningexcel-smart-analysis-and-cleaning对多 Sheet Excel 进行智能清洗、跨表核对与可视化分析。。skills/sn-da-excel-workflow/capability/excel-data-cleaning/missing-value-handlingnumeric-format-normalization对 Excel 数据进行数值格式标准化与清洗,支持大规模数据的 Parquet 转换流程,并完成关键指标的合计核对与结果文件导出。skills/sn-da-excel-workflow/capability/excel-data-cleaning/numeric-format-normalizationoutlier-detection-and-quality-assessment执行全面的异常值检测与数据质量评估,利用 IQR 方法识别异常值并结合偏度、峰度分析数据分布特征,适用于非正态分布数据的预处理阶段。skills/sn-da-excel-workflow/capability/excel-data-cleaning/outlier-detectiontext-normalization-and-large-file-processing对Excel文件进行文本标准化清洗(如去除异常前缀、提取纯中文字符等),并,最终输出清洗后的Excel文件并提供下载链接。skills/sn-da-excel-workflow/capability/excel-data-cleaning/text-normalizationcategory-filtering-and-difficulty-analysis对Excel数据进行自定义分类统计、交叉分析与可视化,并基于多维度指标(如文本长度、术语密度、正则匹配等)进行综合评分与分级,适用于多类别数据分布统计及文本内容难度/质量评估场景。skills/sn-da-excel-workflow/capability/excel-data-filtering/category-filteringcondition-filtering-and-large-file-optimization根据数据规模动态选择处理策略。skills/sn-da-excel-workflow/capability/excel-data-filtering/condition-filteringexcel-conditional-filtering-optimization根据多维数值条件筛选 Excel 数据并导出结果,支持大规模数据的自动性能优化处理。skills/sn-da-excel-workflow/capability/excel-data-filtering/range-filteringexcel-threshold-analysis-and-styling根据 Excel 数据量级自动判断处理策略,执行数值列清洗、条件过滤,并使用 openpyxl 对符合条件的单元格进行样式标记与导出。skills/sn-da-excel-workflow/capability/excel-data-filtering/threshold-filteringexcel-basic-statistics-and-routing对多Sheet Excel文件进行基础统计与,支持按条件筛选计算均值,以及从指定行区间提取数据去重求和,并生成结果文件与下载链接。skills/sn-da-excel-workflow/capability/excel-data-statistics/basic-statisticscategory-statistics提取指定类别列并统计各类别数量与占比,生成高分辨率的柱状图、饼图等组合可视化报告,适用于分类数据的分布情况分析。skills/sn-da-excel-workflow/capability/excel-data-statistics/category-statisticsgrouped-statistics对多 Sheet 的 Excel 文件进行行数统计、数据合并与前向填充。skills/sn-da-excel-workflow/capability/excel-data-statistics/grouped-statisticsdynamic-percentage-and-large-file-analysis根据文件行数动态切换大文件处理策略(Parquet转换),通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量,最终输出结构化Excel报告及可视化图表。skills/sn-da-excel-workflow/capability/excel-data-statistics/percentage-calculationexcel-bar-chart-visualization读取多工作表Excel文件,自动处理合并单元格与数据清洗,进行交叉分组统计并生成带总计行的结果表,最后绘制支持中英文字体的美化柱状图,适用于多维度数据汇总与可视化分析。skills/sn-da-excel-workflow/capability/excel-data-visualization/bar-chart-visualizationstatistical-distribution-and-outlier-analysis执行数值型数据的分布分析与异常值检测,支持通过正则表达式从文本中提取误差项并生成高分辨率的箱线图与直方图报告。skills/sn-da-excel-workflow/capability/excel-data-visualization/histogram-visualizationline-chart-visualization提取结构化数据并进行特征清洗与聚类分析,生成包含趋势对比、分布特征与参数敏感性的多维度综合可视化图表,适用于各类趋势预测与多维对比场景。skills/sn-da-excel-workflow/capability/excel-data-visualization/line-chart-visualizationpie-chart-data-analysis对多Sheet Excel或CSV数据进行分类汇总统计,自动识别关键字段并生成包含占比、数值及美化饼图的可下载分析报告。skills/sn-da-excel-workflow/capability/excel-data-visualization/pie-chart-visualizationexcel-statistical-viz-large-file对 Excel 数据进行多维度统计分析与可视化。skills/sn-da-excel-workflow/capability/excel-data-visualization/scatter-plot-visualizationstacked-chart-visualization处理包含百分比字符串的分类占比数据,通过补全缺失维度并生成堆叠柱状图,直观展示多维度构成随时间或分类的变化趋势。skills/sn-da-excel-workflow/capability/excel-data-visualization/stacked-chart-visualizationlarge-excel-analysis-and-formatting用于处理多Sheet大型Excel文件,支持大文件Parquet格式转换提速,并使用openpyxl生成带条件高亮和自定义样式的格式化Excel报告及下载链接。skills/sn-da-excel-workflow/capability/excel-reading/large-excel-readingmulti-file-excel-parquet-analysis读取多 Sheet Excel 文件并统计规模,支持大文件向 Parquet 格式转换、分类数据统计及可视化报告生成。skills/sn-da-excel-workflow/capability/excel-reading/multi-file-readingmulti-sheet-reading-and-analysis用于读取多工作表Excel文件,动态评估数据量以启用Parquet大文件优化,并执行正则清洗、分类汇总、线性拟合及生成带格式的图表与结果文件。skills/sn-da-excel-workflow/capability/excel-reading/multi-sheet-readingrange-reading-and-large-file-analysis读取多 Sheet Excel 文件,根据数据量动态选择处理策略,支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。skills/sn-da-excel-workflow/capability/excel-reading/range-readingsingle-sheet-reading-and-analysis读取并解析单个Excel工作表数据,支持合并单元格处理、数据清洗、交叉分析及多维度可视化,适用于需要从单表中提取关键指标并进行趋势模拟与图表生成的场景。skills/sn-da-excel-workflow/capability/excel-reading/single-sheet-readingexcel-multi-sheet-dynamic-analysis用于分析包含多个Sheet的Excel文件,动态判断数据量级以决定是否转换为Parquet进行大文件处理,并支持跨Sheet的特定字段统计、数据清洗、交叉分析与可视化,最终生成带下载链接的汇总报告。skills/sn-da-excel-workflow/capability/excel-reading/specific-sheet-readingexcel-large-file-processing-and-cleaning读取多 sheet Excel 文件,动态识别目标列进行统计,并使用正则清洗文本字段提取中文字符,最终输出标准化 Excel 文件。skills/sn-da-excel-workflow/capability/excel-reading/structured-header-readingchart-embedded-export从结构化数据中提取分类分布执行清洗与统计,生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告,适用于大文件处理与嵌入式可视化场景。skills/sn-da-excel-workflow/capability/excel-result-export/chart-embedded-exportformatted-export-with-parquet从多Sheet Excel文件中识别指定条件的记录,并将筛选结果以整行标红格式导出为Excel文件,适用于数据清洗、条件筛选与可视化标记场景。skills/sn-da-excel-workflow/capability/excel-result-export/formatted-exportexcel-data-analysis-and-report-generation从Excel提取多类型数据,并生成包含可视化图表与下载链接的综合分析报告。skills/sn-da-excel-workflow/capability/excel-result-export/report-generation-exportexcel-sheet-filter-export动态统计多Sheet Excel文件行数以判断大文件处理逻辑,并根据特定条件筛选数据、重命名字段后导出为包含下载链接的新Excel文件,适用于多Sheet数据探查与条件过滤导出场景。skills/sn-da-excel-workflow/capability/excel-result-export/single-sheet-exportdynamic-large-file-parquet-analysis动态统计Excel总行数,当数据量过大(≥10000行)时自动转换为Parquet格式加速读取,并对指定目标列进行条件筛选、分类汇总与结果导出,适用于超大体积Excel文件的快速读取与统计分析。skills/sn-da-excel-workflow/capability/excel-table-styling/table-theme-stylingsn-da-image-caption图片理解与数据提取 skill。当图片文件(.png/.jpg/.jpeg/.gif/.webp/.bmp)是主要输入且用户需要理解、提取数据或分析图片内容时使用。提供预配置的 caption 脚本(scripts/caption.py),通过 vision 模型将图片转为文本描述,无需额外配置 API Key。覆盖:(1) 通过 scripts/caption.py 对图表/表格/截图/流程图进行 caption,(2) 将 caption 文本解析为结构化 DataFrame,(3) 基于提取数据重新生成可视化图表,(4) 导出为 Excel/CSV。**遇到以下任一情况就主动使用本 skskills/sn-da-image-captionsn-da-large-file-analysis万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取(iter_rows 支持 10 万行以上)、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**:①数据行数 ≥ 10k(由 sn-da-excel-workflow 的行数评估步骤触发);②用户出现触发词:大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunkedskills/sn-da-large-file-analysisDocumentation
README
SenseNova-Skills
English | 简体中文
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, deep research, code-driven video creation, HTML experiences, team collaboration, and proactive project tracking — 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.
International: platform.sensenova.ai/docs, Base URL
https://token.sensenova.ai/v1Mainland China: platform.sensenova.cn/token-plan, Base URLhttps://token.sensenova.cn/v1 - Install & configure: follow the full walkthrough in
INSTALL.md.
This repo documents both the international and mainland China SenseNova API flows. Make sure the docs page, API key, base URL, and model name all come from the same region.
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/ |
Prefer to install manually?
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/.
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 editing (sn-image-edit), 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. Supports SenseNova U1.5 Lite, including native 4K output. |
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. Collects role / audience / scene / page count / mode (standard, dazzle, or creative), parses pdf / docx / md / txt inputs, emits task_pack.json + info_pack.json, and dispatches to the downstream mode. |
sn-ppt-story |
PPT Outline (Story) | Mandatory mid-stage between the entry and the exit modes: turns the query, user materials, and completed research into the single editable outline.md; must not be skipped or written by hand. |
sn-ppt-standard |
PPT Standard Mode | style spec → outline → asset plan + per-slot images + VLM QA → per-page HTML → per-page review → build present.html; exports PPTX via its own HTML→PPTX exporter. |
sn-ppt-dazzle |
PPT Dynamic Mode | Turns a prepared outline.md into a single-file 1280×720 dynamic HTML deck (motion, page transitions, keyboard navigation) for animated/interactive presentations. |
sn-ppt-creative |
PPT Creative Mode | One full-page 16:9 PNG per slide generated from a per-page composed prompt; exports PPTX. |
sn-ppt-doctor |
PPT Environment Doctor | Checks local rendering/export dependencies (Python/Node Playwright, Chromium, PPTX exporter) and Bundled media config; reports only — never writes .env or modifies the task directory. |
sn-ppt-tools |
Bundled Tool Fallback | Provides the fallback (web search, image search, image generation, image download) when the host's native search/image tools are absent or fail; reads SN_PPT_* config from .env. |
sn-ppt-workbench |
PPT Edit Workbench | Opens or reuses the AI PPT editing WebUI for an existing HTML deck: preview, inspect, and visually fine-tune in the browser; never regenerates the deck or edits slide files directly. |
📈 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 guides: docs/sn-deep-research.md and docs/sn-deepresearch-cli.md (prerequisites, Quick Start, CLI setup, and per-stage invocation).
| Name | Label | Description |
|---|---|---|
sn-deep-research |
Deep Research Entry Point | Mode-aware deep-research orchestrator with parallel research work packages, one-pass quick/normal synthesis, and an auditable heavy workflow, producing final report.md. |
sn-deepresearch-cli |
Deep Research CLI | Installs and operates the standalone sensenova-skills-deepresearch CLI, coordinating search, research, monitoring, recovery, and report export through a selected Harness or Agent. |
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 | Optional standalone format recommendation; sn-deep-research uses a single request-level format string instead of format artifacts. |
sn-prepare-citations |
Citation Rendering | Post-processes [^source_id] footnotes into numbered citations and appends references from evidence sources. |
🔍 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. |
🎬 Code-driven Video Creation
📖 Full guide: docs/sn-code-video.md (requirements, setup, rendering, sound, and verification).
| Name | Label | Description |
|---|---|---|
sn-code-video |
Code Video | Creates and revises code-driven MP4 videos with deterministic frame rendering, eight craft guides, optional sound and narration, contact-sheet review, and final-file verification. |
🌐 HTML & Web Experiences
📖 Full guide: docs/sn-motion-html.md (continuous-shot stories, media generation, project setup, and browser QA).
| Name | Label | Description |
|---|---|---|
sn-motion-html |
Motion HTML Storytelling | Builds immersive, scroll-driven web stories with a continuous camera journey, consistent stills, Seedance clips, structured content, and responsive browser delivery. |
sn-md-to-html-report |
Markdown → HTML Report | Reworks a Markdown report into a self-contained HTML feature page with editorial structure, evidence order, responsive layout, and offline-friendly assets. |
🤝 Team Collaboration
📖 Full guide: docs/sn-team-harness.md (self-hosted setup, core concepts, local execution, and security boundaries).
| Name | Label | Description |
|---|---|---|
sn-team-harness |
Team Harness | Explains the self-hosted workspace where people and local Agents share context, projects, work items, resources, and versioned artifacts. |
🔔 Proactive Project Tracking
📖 Full guide: docs/sn-proactive-agent.md (installation, Hermes / OpenClaw integration, Web workbench, data layout, and acceptance checks).
| Name | Label | Description |
|---|---|---|
sn-proactive-agent |
Proactive Agent | Tracks long-running project progress, keeps auditable Project / Item / Event records, and presents next-step suggestions in a Web workbench; accepted suggestions resume the original Hermes or OpenClaw session. |
Both integrations currently require a model-configured Hermes CLI for background processing. OpenClaw uses a Gateway plugin and does not require the Hermes Connector.
Sample Outputs
🎨 Infographic (sn-infographic)
A few sn-infographic outputs (more in docs/sn-infographic-examples.md).

🧩 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
Third-Party Quick Try
If you want to try a single skill before setting up the full local stack, here are some community / third-party entry points.
These links are maintained outside this repository and are not part of the official SenseNova-Skills support surface. Availability, account requirements, and platform terms may vary by provider.
sn-infographicon ClawMama (Telegram / WhatsApp) — a lightweight first run for the infographic workflow in an OpenClaw / Hermes-style agent.
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
Join the Community
Join our growing community to share feedback, get support, and stay updated on the latest developments. Scan the QR code below to hop into the chat — we'd love to hear from you!
| Discord | Lark Group |
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License
MIT — see LICENSE.

