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 nongjun/feishu-cursor-claw --skill data-exportgit clone --depth 1 https://github.com/nongjun/feishu-cursor-clawWrote 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/nongjun/feishu-cursor-claw/data-export)<a href="https://agentmods.dev/skills/nongjun/feishu-cursor-claw/data-export"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/data-export/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/nongjun/feishu-cursor-claw/data-export"><img src="https://agentmods.dev/badge/skills/nongjun/feishu-cursor-claw/data-export.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 | $0.00046 | $0.00448 |
| Opus 5.5 | $0.00018 | $0.00179 |
| Sonnet 5.5 | $0.00009 | $0.00090 |
| Haiku 4.5 | $0.00005 | $0.00045 |
Grade A, and why
data-export 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 1mo 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.
What it actually says
数据导出
核心能力
| 功能 | 说明 |
|---|---|
| 异步导出 | 后台线程处理，不阻塞请求 |
| 多格式 | Excel (.xlsx) / CSV / JSON |
| 多维度 | 按员工 / 客户 / 群导出 |
| 进度追踪 | progress 字段实时更新 |
| 任务管理 | 列表、取消、删除 |
| Excel 美化 | 表头样式、自适应列宽、边框 |
关键函数
| 函数 | 用途 |
|---|---|
| run_export_in_background() | 启动后台导出线程 |
| process_export_task() | 处理导出任务主逻辑 |
| _export_to_excel() | 导出 Excel |
| _export_to_csv() | 导出 CSV |
| _export_to_json() | 导出 JSON |
| get_export_list() | 获取任务列表（分页） |
| cancel_export_task() | 取消进行中的任务 |
| delete_export_task() | 删除任务及文件 |
SOP：新增导出类型
- 在 export_service.py 中新增导出处理逻辑
- 创建 ExportTask 记录（task_id, export_type, format, status=pending）
- 调用 run_export_in_background(task_id) 启动后台处理
- 前端轮询任务状态或用 WebSocket 推送完成通知
- 完成后通过 /download/{task_id} 下载文件
重点关注
- 依赖 openpyxl 库处理 Excel
- 大数据量导出需分批查询，避免内存溢出
- 导出文件存放在容器内，需挂载卷持久化
参考文件
- 会话存档/后端服务/app/services/export_service.py
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.
- 1mo ago First seen · 49 lines · 46 tokens per session scan A 55f3ab76613e
data-export is a skill published in the GitHub repository nongjun/feishu-cursor-claw (14 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 448 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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