[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1780":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":9,"pushedAt":9,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":15,"starSnapshotCount":15,"syncStatus":28,"lastSyncTime":29,"discoverSource":30},1780,"SenseNova-Skills","OpenSenseNova\u002FSenseNova-Skills","OpenSenseNova","Modular SenseNova skills for building AI-powered office assistants and productivity workflows",null,"Python",4045,284,94,7,0,409,1091,1834,1227,29.36,"MIT License",false,"main",[],"2026-06-12 02:00:32","# SenseNova-Skills\n\n**English | [简体中文](README_CN.md)**\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"docs\u002Fimages\u002Fteasers\u002Fteaser_v2.webp\" width=\"100%\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fplatform.sensenova.cn\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWebsite-Platform-1f6feb?style=flat-square&logo=googlechrome&logoColor=white\" alt=\"Website\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Foffice.xiaohuanxiong.com\u002Fhome\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A6%9D_Raccoon-Try%20it%20free-f29415?style=flat-square\" alt=\"Raccoon\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fplatform.sensenova.cn\u002Ftoken-plan\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FToken_Plan-Free-2ea44f?style=flat-square&logo=opensea&logoColor=white\" alt=\"Token Plan\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FOpenSenseNova\u002FSenseNova-U1\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSenseNova-U1-8957e5?style=flat-square&logo=github&logoColor=white\" alt=\"SenseNova U1\">\u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FOpenSenseNova\u002FSenseNova6.7\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FSenseNova-6.7-cf222e?style=flat-square&logo=github&logoColor=white\" alt=\"SenseNova 6.7\">\u003C\u002Fa>\n\u003C\u002Fp>\n\nThe SenseNova model family plugs directly into agent runtimes such as [OpenClaw](https:\u002F\u002Fopenclaw.ai\u002F) and [hermes-agent](https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent), with the skills in this repository extending the models with concrete, end-to-end office capabilities.\n\nIn 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](https:\u002F\u002Fagentskills.io\u002F) convention.\n\nThe skills cover **image generation & visualization**, **slide-deck (PPT) generation**, **Excel data analysis**, and **deep research** — usable standalone or composed into end-to-end workflows.\n\n> 🎨 **Want to see what it can do?** Check out our   [**sn-infographic Gallery**](docs\u002Fsn-infographic-examples.md) to explore nearly 100 stunning generation cases and steal their **prompt designs**  !\n\n## 🦝 Available out-of-the-box in Raccoon\n\nThe latest SenseNova models and the full Cowork-Skill suite in this repo are bundled into [**Raccoon**](https:\u002F\u002Foffice.xiaohuanxiong.com\u002Fhome), 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.\n\nRaccoon now ships a full upgrade across product capability and client experience:\n\n- **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\u002Fanalysis to formal report decks, industry\u002Fcompetitive research, and investment memos.\n- **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.\n- **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.\n- **Proven at scale**: chosen by 15M+ individual users and thousands of enterprise customers.\n\n> 👉 Try it: [xiaohuanxiong.com](https:\u002F\u002Foffice.xiaohuanxiong.com\u002Fhome)\n\n## How to Use\n\nThese skills are designed to run inside an [Agent Skills](https:\u002F\u002Fagentskills.io\u002F)-compatible agent.\n\n- **Recommended runtime**: pair them with **[OpenClaw](https:\u002F\u002Fopenclaw.ai\u002F)** or **[hermes-agent](https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent)**.\n- **Recommended LLM**: pair them with the **[SenseNova Platform API](https:\u002F\u002Fplatform.sensenova.cn\u002Ftoken-plan)** — a free token plan is available.\n- **Install & configure**: follow the full walkthrough in **[`INSTALL.md`](INSTALL.md)**.\n\n**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:\n\n> *\"Please install SenseNova-Skills from https:\u002F\u002Fgithub.com\u002FOpenSenseNova\u002FSenseNova-Skills into your skills directory.\"*\n\nAfter it finishes, **you may need to manually restart the agent service** before the new skills are picked up.\n\n| Agent | Target directory |\n|-------|------------------|\n| [OpenClaw](https:\u002F\u002Fopenclaw.ai\u002F) | `~\u002F.openclaw\u002Fskills\u002F` |\n| [hermes-agent](https:\u002F\u002Fgithub.com\u002FNousResearch\u002Fhermes-agent) | `~\u002F.hermes\u002Fskills\u002F` |\n\n\u003Cdetails>\n\u003Csummary>Prefer to install manually?\u003C\u002Fsummary>\n\nClone this repository, then copy the subdirectories under `skills\u002F` into the target directory yourself:\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FOpenSenseNova\u002FSenseNova-Skills.git --depth=1\nmkdir -p ~\u002F.openclaw\u002Fskills\ncp -r SenseNova-Skills\u002Fskills\u002F* ~\u002F.openclaw\u002Fskills\u002F\n```\n\nFor Hermes, swap the target to `~\u002F.hermes\u002Fskills\u002F`.\n\n\u003C\u002Fdetails>\n\nPer-category Python dependencies, API keys, and invocation examples are documented in the 📖 Full guide for each section.\n\n## Skills List\n\n### 🎨 Image & Visualization\n\n📖 Full guide: [`docs\u002Fsn-image-generate_en.md`](docs\u002Fsn-image-generate_en.md) (prerequisites, Quick Start, API config, and invocation samples).\n\n\n| Name                                               | Label                          | Description                                                                                                                                                       |\n| -------------------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [`sn-image-doctor`](skills\u002Fsn-image-doctor\u002FSKILL.md)           | Environment Doctor             | Validates the SenseNova-Skills environment — checks `sn-image-base` install, Python deps, and required env vars; interactively fills missing values into `.env`. |\n| [`sn-image-base`](skills\u002Fsn-image-base\u002FSKILL.md)   | 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. |\n| [`sn-infographic`](skills\u002Fsn-infographic\u002FSKILL.md) | Infographic Generation (Tier 1) | Auto prompt-quality scoring, layout\u002Fstyle selection (87 layouts \u002F 66 styles), multi-round generation with VLM review and quality ranking, producing publication-ready infographics. |\n| [`sn-image-imitate`](skills\u002Fsn-image-imitate\u002FSKILL.md) | Image Imitation (Tier 1) | Given one reference image and a target content prompt, generates a new image that imitates the reference. |\n| [`sn-image-resume`](skills\u002Fsn-image-resume\u002FSKILL.md) | Resume Image Generation (Tier 1) | Given resume information, generates a resume image. |\n\n\n### 📊 Presentations (PPT)\n\n📖 Full guide: [`docs\u002Fsn-ppt-generate.md`](docs\u002Fsn-ppt-generate.md) (prerequisites, Quick Start, API config, and invocation samples).\n\n\n| Name                                           | Label                  | Description                                                                                                                                                                                                              |\n| ---------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |\n| [`sn-ppt-entry`](skills\u002Fsn-ppt-entry\u002FSKILL.md)       | **PPT Entry Point**    | **Unified entry point for PPT generation.** Collects role \u002F audience \u002F scenario \u002F page count \u002F mode (creative or standard), parses uploaded pdf \u002F docx \u002F md \u002F txt, emits `task_pack.json` + `info_pack.json`, and dispatches to the chosen mode. |\n| [`sn-ppt-doctor`](skills\u002Fsn-ppt-doctor\u002FSKILL.md)     | 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`.                                                             |\n| [`sn-ppt-creative`](skills\u002Fsn-ppt-creative\u002FSKILL.md) | PPT Creative Mode      | One full-page 16:9 PNG per slide, generated via `sn-image-generate` with a per-page composed prompt.                                                                                                                     |\n| [`sn-ppt-standard`](skills\u002Fsn-ppt-standard\u002FSKILL.md) | PPT Standard Mode      | `style_spec` → outline → asset plan + per-slot images + VLM QC → per-page HTML → per-page review (with optional rewrite) → aggregated `review.md` → PPTX export.                                                         |\n\n\n### 📈 Data Analysis (DA)\n\n📖 Full guide: [`docs\u002Fsn-data-analysis.md`](docs\u002Fsn-data-analysis.md) (prerequisites, Quick Start, API config, and invocation samples).\n\n\n| Name                                                               | Label                                | Description                                                                                                                                                            |\n| ------------------------------------------------------------------ | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [`sn-da-excel-workflow`](skills\u002Fsn-da-excel-workflow\u002FSKILL.md)           | 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\u002FCSV export. |\n| [`sn-da-image-caption`](skills\u002Fsn-da-image-caption\u002FSKILL.md)             | Image Understanding & Data Extraction | For image-first inputs — table OCR, chart understanding, screenshot\u002FUI description; parses captions into DataFrames, recreates visualizations, exports Excel\u002FCSV.    |\n| [`sn-da-large-file-analysis`](skills\u002Fsn-da-large-file-analysis\u002FSKILL.md) | 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.        |\n\n\n### 🔬 Deep Research\n\n📖 Full guide: [`docs\u002Fsn-deep-research.md`](docs\u002Fsn-deep-research.md) (prerequisites, `web_search` precheck, Quick Start, and per-stage invocation).\n\n\n| Name                                                                 | Label                          | Description                                                                                                                                                       |\n| -------------------------------------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| [`sn-deep-research`](skills\u002Fsn-deep-research\u002FSKILL.md)                     | **Deep Research Entry Point**  | **Unified entry point for deep research.** End-to-end orchestrator: planning → per-dimension evidence gathering → synthesis → final `report.md`. Artifacts persist to `report_dir`; supports resumable execution. |\n| [`sn-research-planning`](skills\u002Fsn-research-planning\u002FSKILL.md)             | Research Planning              | Produces `plan.json` from `request.md` in a single pass — scoping, report-shape, dimension breakdown, key questions, search strategy, dependencies, and completion criteria. |\n| [`sn-dimension-research`](skills\u002Fsn-dimension-research\u002FSKILL.md)           | Per-Dimension Evidence Gathering | Executes one dimension from `plan.json` — runs the dimension's `search_strategy`, filters evidence, cross-validates, and writes `sub_reports\u002F{dimension_id}.md`. |\n| [`sn-research-synthesis`](skills\u002Fsn-research-synthesis\u002FSKILL.md)           | Judgment Synthesis             | Synthesizes multiple `sub_reports` into `synthesis.md` — main-thread judgments, evidence strength, cross-dimension consensus, key conflicts, and uncertainties.   |\n| [`sn-research-report`](skills\u002Fsn-research-report\u002FSKILL.md)                 | 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.    |\n| [`sn-report-format-discovery`](skills\u002Fsn-report-format-discovery\u002FSKILL.md) | Report-Format Discovery        | Answers \"what should this kind of report look like\" — derives section structure, required elements, and style constraints. Usable standalone or as the `report_shape` source for sn-deep-research. |\n| [`sn-md-to-html-report`](skills\u002Fsn-md-to-html-report\u002FSKILL.md)             | 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. |\n\n\n### 🔍 Search\n\n📖 Search skills are documented together with deep research: [`docs\u002Fsn-deep-research.md`](docs\u002Fsn-deep-research.md) (includes per-platform API keys, invocation, and unified JSON output).\n\n\n| Name                                                   | Label                  | Description                                                                                                                                |\n| ------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |\n| [`sn-search-academic`](skills\u002Fsn-search-academic\u002FSKILL.md)   | Academic Search        | ArXiv (with section-level HTML reading) \u002F Semantic Scholar (with citation counts) \u002F PubMed (with PMC open-access full text) \u002F Wikipedia, in one aggregated interface. |\n| [`sn-search-code`](skills\u002Fsn-search-code\u002FSKILL.md)           | Developer Search       | GitHub (repo \u002F code \u002F issue) \u002F Stack Overflow \u002F Hacker News \u002F HuggingFace (models \u002F datasets \u002F spaces), aggregated.                        |\n| [`sn-search-social-cn`](skills\u002Fsn-search-social-cn\u002FSKILL.md) | Chinese Social Search  | Bilibili \u002F Zhihu \u002F Douyin search; some platforms require cookie auth.                                                                      |\n| [`sn-search-social-en`](skills\u002Fsn-search-social-en\u002FSKILL.md) | English Social Search  | Reddit \u002F Twitter (X) \u002F YouTube search.                                                                                                     |\n\n\n## Sample Outputs\n\n### 🎨 Infographic (sn-infographic)\n\nA few `sn-infographic` outputs (more in [`docs\u002Fsn-infographic-examples.md`](docs\u002Fsn-infographic-examples.md)).\n\n\u003Cimg src=\"docs\u002Fimages\u002Fteasers\u002Fcases_merge.webp\" alt=\"sn-infographic sample outputs\">\n\n### 🧩 Memory price analysis — insight → analysis → presentation → end-to-end workflow\n\n[`examples\u002Fmemory-price-end2end-analysis`](examples\u002Fmemory-price-end2end-analysis\u002F). 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.\n\n- Depends on: [`sn-da-excel-workflow`](skills\u002Fsn-da-excel-workflow\u002FSKILL.md), [`sn-deep-research`](skills\u002Fsn-deep-research\u002FSKILL.md), [`sn-ppt-entry`](skills\u002Fsn-ppt-entry\u002FSKILL.md), [`sn-ppt-standard`](skills\u002Fsn-ppt-standard\u002FSKILL.md), [`sn-md-to-html-report`](skills\u002Fsn-md-to-html-report\u002FSKILL.md)\n\n### 📊 Employee performance analysis — data analysis\n\n[`examples\u002Femployee-performance-analysis`](examples\u002Femployee-performance-analysis\u002F). 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.\n\n- Depends on: [`sn-da-excel-workflow`](skills\u002Fsn-da-excel-workflow\u002FSKILL.md)\n\n### 🔬 Embodied AI industry research — deep research\n\n[`examples\u002Fembodied-ai-deep-research`](examples\u002Fembodied-ai-deep-research\u002F). 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 stitches the per-dimension evidence into a coherent industry narrative 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.\n\n- Depends on: [`sn-deep-research`](skills\u002Fsn-deep-research\u002FSKILL.md)\n\n### 🎯 Property fee pricing — PPT generation\n\n[`examples\u002Fproperty-fee-pricing-ppt`](examples\u002Fproperty-fee-pricing-ppt\u002F). 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.\n\n- Depends on: [`sn-ppt-entry`](skills\u002Fsn-ppt-entry\u002FSKILL.md), [`sn-ppt-standard`](skills\u002Fsn-ppt-standard\u002FSKILL.md)\n\n## Contributing\n\nFeel free to use the skills here as templates for your own OpenClaw skills. The qualities that make a skill good:\n\n- **Clear triggers**: state in `description` exactly when the skill should and should not run, so the agent recognizes it accurately\n- **Focused scope**: each skill does one thing well; complex workflows compose multiple skills\n- **Solid documentation**: examples, artifact contracts, edge cases, failure handling\n- **Supporting resources**: use `references\u002F`, `scripts\u002F`, `prompts\u002F` to provide additional context\n\n## Join the Community\n\nJoin 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!\n\n\u003Cdiv align=\"center\">\n\u003Ctable>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Cb>\u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FBuTXPHmQub\">Discord\u003C\u002Fa>\u003C\u002Fb>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Cb>WeChat Group\u003C\u002Fb>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd align=\"center\">\u003Ca href=\"https:\u002F\u002Fdiscord.com\u002Finvite\u002FBuTXPHmQub\">\u003Cimg src=\"assets\u002Fdiscord_qr.webp\" width=\"160\"\u002F>\u003C\u002Fa>\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\u003Cimg src=\"assets\u002Fsensenova-skills-chatgroup.jpg\" width=\"160\"\u002F>\u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\u003C\u002Fdiv>\n\n## License\n\nMIT — see [LICENSE](LICENSE).\n","SenseNova-Skills 是一个用于构建基于AI的办公助手和生产力工作流的模块化技能库。该项目使用Python编写，提供图像生成与可视化、幻灯片（PPT）生成、Excel数据分析以及深度研究等功能，支持独立使用或组合成端到端的工作流程。这些技能遵循Agent Skills规范，并可通过简单的配置文件进行扩展。适用于需要提高工作效率、简化复杂数据处理任务的企业和个人场景，特别适合于需要快速生成报告、分析数据、制作演示文稿等办公环境。通过Raccoon平台，用户可以零配置体验所有功能，享受企业级安全保护。",2,"2026-06-11 02:45:59","CREATED_QUERY"]