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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, 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 !
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
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/ |
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.
📖 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. |
📖 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. |
📖 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. |
📖 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 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. |
sn-research-planning |
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. |
sn-dimension-research |
Per-Dimension Evidence Gathering | Executes one dimension from plan.json — runs the dimension's search_strategy, filters evidence, cross-validates, and writes sub_reports/{dimension_id}.md. |
sn-research-synthesis |
Judgment Synthesis | Synthesizes multiple sub_reports into synthesis.md — main-thread judgments, evidence strength, cross-dimension consensus, key conflicts, and uncertainties. |
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 |
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. |
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 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. |
A few sn-infographic outputs (more in docs/sn-infographic-examples.md).
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
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
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 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.
- Depends on:
sn-deep-research
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
Common setup and runtime questions (400/401 errors, rate limits, PPT timeouts, infographic quality, model names) are answered in docs/faq.md.
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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MIT — see LICENSE.