feishu-rich-card
Send rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.
Best use case
feishu-rich-card is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Send rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.
Teams using feishu-rich-card should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/medge-feishu-rich-card/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How feishu-rich-card Compares
| Feature / Agent | feishu-rich-card | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Send rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Feishu Rich Card — 飞书图文混排卡片
在飞书群聊中发送**图文并茂**的交互式卡片，用于汇报进展、展示分析结果、项目状态等。
## When to Use
- 汇报任务进展、项目状态
- 展示数据分析结果（图表 + 解读）
- 发送研究简报、阶段性成果
- 任何需要图文混排的飞书消息
## Architecture
```
生成图片(SVG→PNG / matplotlib / PIL)
↓
上传图片到飞书 → 获取 image_key
↓
构造 Card JSON (schema 2.0) → 嵌入 img 元素 + markdown 元素
↓
调用飞书 API 发送 interactive 消息
```
## Workflow
### Step 1: Prepare Images
图片来源可以是：
- **SVG UI 模板** → 用 `svg-ui-templates` skill 生成 SVG → cairosvg 转 PNG
- **matplotlib/seaborn** → 直接 savefig 为 PNG
- **PIL/Pillow** → 程序化生成图片
- **已有文件** → 直接使用本地 PNG/JPG
### Step 2: Upload & Send
使用 `references/send_card.py` 中的辅助函数：
```python
# 完整用法参见 references/send_card.py
from send_card import FeishuCardSender
sender = FeishuCardSender() # 自动读取 openclaw.json 凭证
# 发送图文卡片
sender.send_rich_card(
chat_id="oc_xxx",
title="📊 分析报告",
elements=[
{"type": "markdown", "content": "## 结果摘要\n\n发现 **3 个**显著差异基因"},
{"type": "image", "path": "/tmp/volcano_plot.png", "alt": "火山图"},
{"type": "markdown", "content": "> Gene X: FC=2.5, p<0.001"},
{"type": "hr"},
{"type": "image", "path": "/tmp/heatmap.png", "alt": "热图"},
{"type": "markdown", "content": "**结论：** 样本间差异显著，建议进一步验证。"},
],
header_template="blue" # blue/indigo/green/red/purple/violet/wathet/turquoise/yellow/grey
)
```
### Step 3: Quick One-liner (for simple cases)
```python
sender.send_image_report(
chat_id="oc_xxx",
title="🧬 单细胞分析完成",
intro="UMAP 降维完成，共识别 12 个细胞群：",
image_path="/tmp/umap.png",
conclusion="Cluster 5 为目标细胞群，marker: CD8A, GZMB, PRF1",
header_template="indigo"
)
```
## Card Elements Reference
| Element | Tag | 说明 |
|---------|-----|------|
| **Markdown** | `markdown` | 支持加粗、斜体、链接、列表、引用块、代码块 |
| **Image** | `img` | 需要 `image_key`（上传后获取） |
| **Divider** | `hr` | 水平分割线 |
| **Column Set** | `column_set` | 多列并排布局 |
| **Note** | `note` | 底部灰色备注 |
## Header Templates (颜色)
`blue` `wathet` `turquoise` `green` `yellow` `orange` `red` `carmine` `violet` `purple` `indigo` `grey`
## Key Rules
1. **图片必须先上传**到飞书获取 `image_key`，不能用 URL
2. **Card schema 必须是 `"2.0"`**
3. **每张卡片最多 50 个元素**
4. 图片建议宽度 600-1200px，飞书会自动缩放
5. markdown 中**不能嵌入图片**，图片必须是独立的 `img` 元素
6. 发送后 OpenClaw 的正常回复会重复，用 `NO_REPLY` 避免
## Integration with SVG UI Templates
当需要专业级可视化时，结合 `svg-ui-templates` skill：
```bash
# 1. 生成 SVG（用模板或自定义）
# 2. 转 PNG
python3 -c "import cairosvg; cairosvg.svg2png(url='report.svg', write_to='report.png', output_width=2400)"
# 3. 用本 skill 上传并发送卡片
```
## Default Chat ID
通过环境变量配置：`FEISHU_DEFAULT_CHAT_ID`（在 `.env` 中设置）Related Skills
tooluniverse-gene-enrichment
Perform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, functional annotation, or gene set analysis.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Academic Writing
## Overview
scientific-visualization
## Overview
venue-templates
Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.
vaex
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
uspto-database
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
uniprot-database
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
umap-learn
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
treatment-plans
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
transformers
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.