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…
Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is
$ npx -y skills add OpenSenseNova/SenseNova-Skills --skill sn-image-base --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/sn-image-baseContext preview
The summary Claude sees to decide when to auto-load this skill.
Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is
name: sn-image-base description: | Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only. triggers: - "SenseNova-Skills Image Generation" - "SenseNova-Skills 图像基础工具" - "sn 图像基础工具" - "SenseNova 图像基础工具" - "SenseNova Image Generation" - "sn-image-base" metadata: project: SenseNova-Skills tier: 0 category: infrastructure user_visible: false
pip install -r requirements.txt
`sn-image-base` is the base-layer skill (tier 0) of the SenseNova-Skills project and provides four low-level tools:
This skill **does not perform any input preprocessing** and only calls backend services to return results.
Image generation tool that calls the text-to-image-no-enhance API.
`--prompt` is required; all other parameters are optional:
| Parameter | Type | Default | Description | |------|------|--------|------| | `--prompt` | string | **Required** | Prompt text for image generation | | `--negative-prompt` | string | `""` | Negative prompt | | `--image-size` | string | `2k` | Image size preset (case-insensitive). Recommended: `2k`. `4k` is supported by `sensenova-u1.5-lite`; other SenseNova image models may reject it. Other values → `status=failed`. | | `--aspect-ratio` | string | `16:9` | Aspect ratio, e.g. `1:1`, `16:9`, `9:16` | | `--seed` | int | `None` | Random seed for reproducible generation | | `--unet-name` | string | `None` | Specify a UNet model name | | `--api-key` | string | `SN_IMAGE_GEN_API_KEY` -> `SN_API_KEY` | API key (CLI argument has priority; `MissingApiKeyError` is raised when all are empty) | | `--base-url` | string | `SN_IMAGE_GEN_BASE_URL` -> `SN_BASE_URL` | API base URL (CLI argument has priority) | | `--poll-interval` | float | `5.0` | Polling interval (seconds) | | `--timeout` | float | `300.0` | Timeout (seconds) | | `--insecure` | flag | `False` | Disable TLS verification | | `--save-path` | Path | Auto-generated | Save path |
SenseNova image requests explicitly send `watermark=false` by default. Both `sensenova-u1-fast` and `sensenova-u1.5-lite` are supported; U1.5 Lite additionally supports native 4K output. This no-watermark feature is currently in free public beta and may become paid.
Edits one or more reference images with SenseNova U1.5 Lite through the `/images/edits` endpoint. Local paths are converted to Data URLs; HTTP(S) URLs and Data URLs are passed through.
python scripts/sn_agent_runner.py sn-image-edit \
--prompt "Change the background to a snowy mountain" \
--images source.png reference.png \
--save-path edited.pngThe edit request uses the official defaults `n=1`, `size=auto`, `watermark=false`, `prompt_extend=true`, and `response_format=url`.
Image recognition tool that uses VLM (Vision Language Model) to analyze image content. Supports multiple image inputs.
`--images` and `--user-prompt` (or `--user-prompt-path`) are required. All other parameters use three-level defaults (CLI > env var > built-in default):
| Parameter | Type | Built-in Default | Env Var | Description | |------|------|-----------|---------|------| | `--api-key` | string | No hardcoded default | `SN_VISION_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY` | Chat runtime API key; raises `MissingApiKeyError` when all are unset | | `--base-url` | string | `SN_CHAT_BASE_URL` default | `SN_VISION_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL` | Vision provider base URL; falls back to shared chat/global provider | | `--model` | string | `sensenova-6.8-flash-lite` | `SN_VISION_MODEL` -> `SN_CHAT_MODEL` | Vision-capable model name | | `--vlm-type` | string | `openai-completions` | `SN_VISION_TYPE` -> `SN_CHAT_TYPE` | Chat protocol type override | | `--user-prompt-path` | string | `None` | - | Local file path, mutually exclusive with `--user-prompt` | | `--system-prompt-path` | string | `None` | - | Local file path, mutually exclusive with `--system-prompt` |
Available values for `--vlm-type`:
Text optimization tool that uses LLM (Language Model) to optimize text content. Does not accept image inputs.
`--user-prompt` (or `--user-prompt-path`) is required. All other parameters use three-level defaults (CLI > env var > built-in default):
| Parameter | Type | Built-in Default | Env Var | Description | |------|------|-----------|---------|------| | `--api-key` | string | No hardcoded default | `SN_TEXT_API_KEY` -> `SN_CHAT_API_KEY` -> `SN_API_KEY` | Chat runtime API key; raises `MissingApiKeyError` when all are unset | | `--base-url` | string | `SN_CHAT_BASE_URL` default | `SN_TEXT_BASE_URL` -> `SN_CHAT_BASE_URL` -> `SN_BASE_URL` | Text provider base URL; falls back to shared chat/global provider | | `--model` | string | `sensenova-6.8-flash-lite` | `SN_TEXT_MODEL` -> `SN_CHAT_MODEL` | Text model name | | `--llm-type` | string | `openai-completions` | `SN_TEXT_TYPE` -> `SN_CHAT_TYPE` | Chat protocol type override | | `--user-prompt-path` | string | `None` | - | Local file path, mutually exclusive with `--user-prompt` | | `--system-prompt-path` | string | `None` | - | Local file path, mutually exclusive with `--system-prompt` |
Available values for `--llm-type`:
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。