# SenseNova-Skills
**English | [简体中文](README_CN.md)**
The SenseNova model family plugs directly into agent runtimes such as [OpenClaw](https://openclaw.ai/) and [hermes-agent](https://github.com/NousResearch/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](https://agentskills.io/) 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**](docs/sn-infographic-examples.md) 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**](https://office.xiaohuanxiong.com/home), 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](https://office.xiaohuanxiong.com/home)
## How to Use
These skills are designed to run inside an [Agent Skills](https://agentskills.io/)-compatible agent.
- **Recommended runtime**: pair them with **[OpenClaw](https://openclaw.ai/)** or **[hermes-agent](https://github.com/NousResearch/hermes-agent)**.
- **Recommended LLM**: pair them with the **SenseNova Platform API**.
International: [platform.sensenova.ai/docs](https://platform.sensenova.ai/docs), Base URL `https://token.sensenova.ai/v1`
Mainland China: [platform.sensenova.cn/token-plan](https://platform.sensenova.cn/token-plan), Base URL `https://token.sensenova.cn/v1`
- **Install & configure**: follow the full walkthrough in **[`INSTALL.md`](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](https://openclaw.ai/) | `~/.openclaw/skills/` |
| [hermes-agent](https://github.com/NousResearch/hermes-agent) | `~/.hermes/skills/` |
Prefer to install manually?
Clone this repository, then copy the subdirectories under `skills/` into the target directory yourself:
```bash
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`](docs/sn-image-generate_en.md) (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
| -------------------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`sn-image-doctor`](skills/sn-image-doctor/SKILL.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`. |
| [`sn-image-base`](skills/sn-image-base/SKILL.md) | 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`](skills/sn-infographic/SKILL.md) | 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`](skills/sn-image-imitate/SKILL.md) | Image Imitation (Tier 1) | Given one reference image and a target content prompt, generates a new image that imitates the reference. |
| [`sn-image-resume`](skills/sn-image-resume/SKILL.md) | Resume Image Generation (Tier 1) | Given resume information, generates a resume image. |
### 📊 Presentations (PPT)
📖 Full guide: [`docs/sn-ppt-generate.md`](docs/sn-ppt-generate.md) (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
| ---------------------------------------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [`sn-ppt-entry`](skills/sn-ppt-entry/SKILL.md) | 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`](skills/sn-ppt-story/SKILL.md) | 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`](skills/sn-ppt-standard/SKILL.md) | 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`](skills/sn-ppt-dazzle/SKILL.md) | 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`](skills/sn-ppt-creative/SKILL.md) | PPT Creative Mode | One full-page 16:9 PNG per slide generated from a per-page composed prompt; exports PPTX. |
| [`sn-ppt-doctor`](skills/sn-ppt-doctor/SKILL.md) | 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`](skills/sn-ppt-tools/SKILL.md) | 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`](skills/sn-ppt-workbench/SKILL.md) | 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`](docs/sn-data-analysis.md) (prerequisites, Quick Start, API config, and invocation samples).
| Name | Label | Description |
| ------------------------------------------------------------------ | ------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`sn-da-excel-workflow`](skills/sn-da-excel-workflow/SKILL.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/CSV export. |
| [`sn-da-image-caption`](skills/sn-da-image-caption/SKILL.md) | 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`](skills/sn-da-large-file-analysis/SKILL.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. |
### 🔬 Deep Research
📖 Full guides: [`docs/sn-deep-research.md`](docs/sn-deep-research.md) and [`docs/sn-deepresearch-cli.md`](docs/sn-deepresearch-cli.md) (prerequisites, Quick Start, CLI setup, and per-stage invocation).
| Name | Label | Description |
| -------------------------------------------------------------------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`sn-deep-research`](skills/sn-deep-research/SKILL.md) | **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`](skills/sn-deepresearch-cli/SKILL.md) | 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`](skills/sn-research-report/SKILL.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. |
| [`sn-report-format-discovery`](skills/sn-report-format-discovery/SKILL.md) | Presentation-Format Discovery | Optional standalone format recommendation; `sn-deep-research` uses a single request-level `format` string instead of format artifacts. |
| [`sn-prepare-citations`](skills/sn-prepare-citations/SKILL.md) | 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`](docs/sn-deep-research.md) (includes per-platform API keys, invocation, and unified JSON output).
| Name | Label | Description |
| ------------------------------------------------------ | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| [`sn-search-academic`](skills/sn-search-academic/SKILL.md) | 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`](skills/sn-search-code/SKILL.md) | Developer Search | GitHub (repo / code / issue) / Stack Overflow / Hacker News / HuggingFace (models / datasets / spaces), aggregated. |
| [`sn-search-social-cn`](skills/sn-search-social-cn/SKILL.md) | Chinese Social Search | Bilibili / Zhihu / Douyin search; some platforms require cookie auth. |
| [`sn-search-social-en`](skills/sn-search-social-en/SKILL.md) | English Social Search | Reddit / Twitter (X) / YouTube search. |
### 🎬 Code-driven Video Creation
📖 Full guide: [`docs/sn-code-video.md`](docs/sn-code-video.md) (requirements, setup, rendering, sound, and verification).
| Name | Label | Description |
| --- | --- | --- |
| [`sn-code-video`](skills/sn-code-video/SKILL.md) | 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`](docs/sn-motion-html.md) (continuous-shot stories, media generation, project setup, and browser QA).
| Name | Label | Description |
| --- | --- | --- |
| [`sn-motion-html`](skills/sn-motion-html/SKILL.md) | 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`](skills/sn-md-to-html-report/SKILL.md) | 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`](docs/sn-team-harness.md) (self-hosted setup, core concepts, local execution, and security boundaries).
| Name | Label | Description |
| --- | --- | --- |
| [`sn-team-harness`](skills/sn-team-harness/SKILL.md) | 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`](docs/sn-proactive-agent.md) (installation, Hermes / OpenClaw integration, Web workbench, data layout, and acceptance checks).
| Name | Label | Description |
| --- | --- | --- |
| [`sn-proactive-agent`](skills/sn-proactive-agent/SKILL.md) | 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`](docs/sn-infographic-examples.md)).
### 🧩 Memory price analysis — insight → analysis → presentation → end-to-end workflow
[`examples/memory-price-end2end-analysis`](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`](skills/sn-da-excel-workflow/SKILL.md), [`sn-deep-research`](skills/sn-deep-research/SKILL.md), [`sn-ppt-entry`](skills/sn-ppt-entry/SKILL.md), [`sn-ppt-standard`](skills/sn-ppt-standard/SKILL.md), [`sn-md-to-html-report`](skills/sn-md-to-html-report/SKILL.md)
### 📊 Employee performance analysis — data analysis
[`examples/employee-performance-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`](skills/sn-da-excel-workflow/SKILL.md)
### 🔬 Embodied AI industry research — deep research
[`examples/embodied-ai-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`](skills/sn-deep-research/SKILL.md)
### 🎯 Property fee pricing — PPT generation
[`examples/property-fee-pricing-ppt`](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`](skills/sn-ppt-entry/SKILL.md), [`sn-ppt-standard`](skills/sn-ppt-standard/SKILL.md)
## 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-infographic` on ClawMama (Telegram / WhatsApp)](https://app.clawmama.run/skills/3k6s9d/hermes?utm_source=github&utm_medium=issue&utm_campaign=skill_outreach_opensensenova_sensenova_skills_sn_infographic) — 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`](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!
## License
MIT — see [LICENSE](LICENSE).
