sn-da-excel-workflow
Excel 数据分析多步编排器。覆盖:(1) 读取多 Sheet Excel 文件并统计行数,(2) 大文件检测(≥10k 行自动 Parquet 优化),(3) 数据清洗(缺失值、文本标准化、无效字符),(4) 条件筛选与分类提取,(5) 跨 Sheet 统计聚合,(6) 导出 Excel/CSV…
Build continuous-shot motion-driven HTML stories from a subject or narrative, including research, story structure, consistent AI stills, Seedance scene and connector clips, interface styling, media normalization, and browser QA. Use for immersive journeys, timelines, product
$ npx -y skills add OpenSenseNova/SenseNova-Skills --skill sn-motion-html --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/sn-motion-htmlContext preview
The summary Claude sees to decide when to auto-load this skill.
Build continuous-shot motion-driven HTML stories from a subject or narrative, including research, story structure, consistent AI stills, Seedance scene and connector clips, interface styling, media normalization, and browser QA. Use for immersive journeys, timelines, product
name: sn-motion-html description: Build continuous-shot motion-driven HTML stories from a subject or narrative, including research, story structure, consistent AI stills, Seedance scene and connector clips, interface styling, media normalization, and browser QA. Use for immersive journeys, timelines, product stories, fictional worlds, or other scene-based visual narratives whose camera should flow from beginning to end; not for ordinary autoplay video pages or general web apps.
Create a reusable project, not a one-off HTML file. Keep subject matter, chapter count, visual style, interface treatment, copy placement, language, image model, video model, clip duration, and hosting path configurable.
The defining experience is one continuous camera journey controlled by page progress. Preserve a single fixed media stage and one ordered timeline from the first scene through every connector to the final scene. Differentiate projects through transparent copy composition, header treatment, type, navigation, and scene-specific copy placement—not by replacing the continuous stage with separate article media blocks.
1. Resolve the story goal, audience, factual fidelity, chapter scale, visual language, copy language, target devices, sound policy, and available API credentials. Make reasonable defaults only for low-risk implementation details. 2. For factual or source-based stories, research before outlining and record sources separately from display copy. 3. Produce a chapter blueprint, shot/connector plan, and character/object bible before generating the full media set. 4. Detect the image-generation capability available in the current environment before generating any stills. The capability may be a callable image tool, a skill that exposes or instructs an image workflow, or both. Use the available capability when present; if no suitable capability is available, report that blocker to the user before continuing past the still-generation stage. 5. Prepare a review package containing the proposed visual style and the content/shot arrangement. When an image capability is available, generate only the small reference/gate set needed for review. 6. Obtain the user's explicit approval at both approval gates below. After approval, generate the complete still set and inspect it before any paid video batch. 7. Dry-run the video manifest and representative clip. Obtain explicit authorization immediately before the first paid video API request, then generate scene clips and connector clips in dependency-aware parallel phases. 8. Choose or design an interface treatment around the continuous stage, assemble from structured content, then verify media and browser behavior.
Do not silently infer approval from an unanswered message or from a prior general instruction. If the user requests changes, revise the package and present the affected gate again.
1. **Visual style approval:** present the style preamble and identity blocks, including medium, palette, materials, lighting, camera grammar, composition, negative constraints, and recurring character/product treatment. When image generation is available, include a small reference/gate set (normally opening, middle, and ending stills). Do not generate the complete still set until the user explicitly approves the visual direction. 2. **Content and shot-plan approval:** present the chapter order, each chapter's dominant visual event and display copy, scene framing/camera intent, connector start and destination, and per-scene copy position. Do not generate the complete still set or write the final video manifest until the user explicitly approves this arrangement.
These are separate decisions: style approval determines how the world looks; content and shot-plan approval determines what is shown and how the camera moves through it. The later paid-video approval is an authorization gate, not a substitute for either creative approval.
Read [references/workflow.md](references/workflow.md) for the full production sequence. Read only the additional references needed for the current stage:
Run:
python scripts/init_project.py /absolute/output/path --title "Story title" --style cinematic --ui folio
The initializer copies `assets/template/` and creates the media directories. Replace the example story and manifests; do not leave placeholder subject matter in the final project.
Choose `--style anime`, `--style cinematic`, or `--style cgi`. Separately choose `--ui folio`, `--ui caption`, or `--ui graphic`. Style controls the generated world; UI controls header, transparent copy treatment, navigation, and type composition. Per-chapter `copyPosition` may be `left`, `right`, `bottom`, or `center` when the focal subject requires it.
All UI presets use the same continuous-shot runtime. Adapt the visual chrome materially for the subject, but do not interrupt the camera chain or turn chapters into separate media cards.
It also copies the reusable scripts into the new project. From the project root, the common commands are:
python scripts/seedance_pipeline.py . plan python scripts/seedance_pipeline.py . all --workers 4 python scripts/verify_media.py . python scripts/contact_sheet.py . python scripts/serve_project.py . --port 8080
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.
Repo: OpenSenseNova/SenseNova-Skills
Excel 数据分析多步编排器。覆盖:(1) 读取多 Sheet Excel 文件并统计行数,(2) 大文件检测(≥10k 行自动 Parquet 优化),(3) 数据清洗(缺失值、文本标准化、无效字符),(4) 条件筛选与分类提取,(5) 跨 Sheet 统计聚合,(6) 导出 Excel/CSV…
当Excel文件总行数超过1万行时,通过转换为Parquet格式提升读取性能,提取目标指标并计算最大值,最后将结果输出为Excel并对特定行进行高亮标注。
根据Excel总行数自动切换Parquet加速读取,计算特定维度的时间序列平均值,并使用openpyxl输出带有条件格式(如低于均值标绿)和自定义样式的分析报告。
根据数据规模动态选择处理策略,对多表数据进行合并、统计筛选,并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。