Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Zaoqu-Liu/ScienceClaw --skill medge-feishu-rich-cardgit clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zaoqu-liu/scienceclaw/medge-feishu-rich-card)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/medge-feishu-rich-card"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/medge-feishu-rich-card/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/medge-feishu-rich-card"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/medge-feishu-rich-card.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.01124 |
| Opus 5 | $0.00031 | $0.00562 |
| Sonnet 5 | $0.00012 | $0.00225 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
feishu-rich-card scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-templatesskill 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:
# 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)
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 119 lines · 62 tokens per session scan A 54a936ad5017
feishu-rich-card is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 62 tokens to every session and 1,124 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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