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README

SenseNova-Skills

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Website Raccoon Token Plan SenseNova U1 SenseNova 6.7

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 !

🦝 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: 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 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.

📊 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. 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.

📈 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 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-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-prepare-citations Citation Rendering Post-processes [^source_id] footnotes into numbered citations and appends references from evidence sources.
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

📖 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 /

Core symbols most depended-on inside this repo

err
called by 117
skills/sn-deep-research/scripts/validate_outline.py
resolve
called by 65
skills/sn-image-base/scripts/sn_image_base/configs.py
pxToInch
called by 52
skills/sn-ppt-standard/scripts/export_pptx/lib/style_parser.mjs
print_json
called by 39
skills/sn-search-academic/scripts/search_utils.py
err
called by 39
skills/sn-deep-research/scripts/validate_evidence.py
_fail
called by 31
skills/sn-ppt-standard/scripts/run_stage.py
_value
called by 27
skills/sn-search-academic/scripts/semantic_scholar_search.py
cssColorToHex
called by 26
skills/sn-ppt-standard/scripts/export_pptx/lib/style_parser.mjs

Shape

Function 823
Method 77
Class 45

Languages

Python86%
TypeScript14%

Modules by API surface

skills/sn-search-finance/scripts/finance_search.py43 symbols
skills/sn-search-academic/scripts/refTree.py41 symbols
skills/sn-ppt-standard/scripts/run_stage.py41 symbols
skills/sn-ppt-standard/scripts/export_pptx/lib/pptx_builder.mjs40 symbols
skills/sn-search-academic/scripts/semantic_scholar_crawler_refTree.py33 symbols
skills/sn-ppt-standard/scripts/export_pptx/lib/echarts_to_pptx.mjs28 symbols
skills/sn-search-academic/scripts/paper.py25 symbols
skills/sn-search-academic/scripts/arxiv_crawler_search.py25 symbols
skills/sn-image-base/scripts/sn_image_base/utils/error_utils.py22 symbols
skills/sn-search-academic/scripts/search.py21 symbols
skills/sn-ppt-doctor/ppt_doctor/check_environment.py20 symbols
skills/sn-ppt-standard/scripts/export_pptx/lib/dom_extractor.mjs18 symbols

Dependencies from manifests, versioned

echarts5.4.3 · 1×
playwright1.50.0 · 1×
pptxgenjs3.12.0 · 1×
arxiv2.0.0 · 1×
beautifulsoup44.12.3 · 1×
httpx0.27 · 1×
pillow10.0.0 · 1×
playwright1.40.0 · 1×
pypdf4.0.0 · 1×
python-docx1.1 · 1×
python-dotenv1.0.0 · 1×
python-pptx0.6.21 · 1×

For agents

$ claude mcp add SenseNova-Skills \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact