SenseNova-Skills

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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, 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/v1 Mainland China: platform.sensenova.cn/token-plan, Base URL https://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.

git clone https://github.com/OpenSenseNova/SenseNova-Skills.git --depth=1
mkdir -p ~/.openclaw/skills
cp -r SenseNova-Skills/skills/* ~/.openclaw/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).

📊 Presentations (PPT)

📖 Full guide: docs/sn-ppt-generate.md (prerequisites, Quick Start, API config, and invocation samples).

📈 Data Analysis (DA)

📖 Full guide: docs/sn-data-analysis.md (prerequisites, Quick Start, API config, and invocation samples).

🔬 Deep Research

📖 Full guides: docs/sn-deep-research.md and docs/sn-deepresearch-cli.md (prerequisites, Quick Start, CLI setup, and per-stage invocation).

🔍 Search

📖 Search skills are documented together with deep research: docs/sn-deep-research.md (includes per-platform API keys, invocation, and unified JSON output).

🌐 HTML & Web Experiences

📖 Full guide: docs/sn-motion-html.md (continuous-shot stories, media generation, project setup, and browser QA).

🤝 Team Collaboration

📖 Full guide: docs/sn-team-harness.md (self-hosted setup, core concepts, local execution, and security boundaries).

🔔 Proactive Project Tracking

📖 Full guide: docs/sn-proactive-agent.md (installation, Hermes integration, Web workbench, data layout, and acceptance checks).

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

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 description exactly 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!

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License

MIT — see LICENSE.