Clowder AI is a self-hosted workspace where AI agents from different model families work together as a persistent team, retaining identities, shared evidence, and memory across tasks. It is for people who want to coordinate multiple AI agents without repeatedly rebuilding their context.
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 zts212653/clowder-ai --skill enterprise-workflowgit clone --depth 1 https://github.com/zts212653/clowder-aiWrote 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/zts212653/clowder-ai/enterprise-workflow)<a href="https://agentmods.dev/skills/zts212653/clowder-ai/enterprise-workflow"><img src="https://agentmods.dev/badge/skills/zts212653/clowder-ai/enterprise-workflow.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00114 | $0.02317 |
| Opus 5 | $0.00057 | $0.01158 |
| Sonnet 5 | $0.00023 | $0.00463 |
| Haiku 4.5 | $0.00011 | $0.00232 |
Grade A, and why
enterprise-workflow 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 8d 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Enterprise Workflow — 企业 IM 工作流自动化
F162:通过厂商官方 CLI 驱动企业操作。 架构决策:ADR-029 — ActionService + CliExecutor + callback route。
两条腿并行:
- Phase A(企业微信):
wecom-cli(Rust)→ callback/api/callbacks/wecom-action - Phase B(飞书/Lark):
lark-cli(Go,@larksuite/cli)→ callback/api/callbacks/lark-action
如何选平台
| operator提到 | 用哪边 |
|---|---|
| 企微 / wecom / 企业微信 / 腾讯 IM | WeCom (/api/callbacks/wecom-action) |
| 飞书 / feishu / lark / 字节 IM | Lark (/api/callbacks/lark-action) |
| 只说"企业 IM"没指定 | 先问一句哪个平台;demo 场景默认 WeCom |
所有操作都必须走 callback route,不要裸调 CLI(ADR-029 Decision 2)。
POST /api/callbacks/{wecom|lark}-action
Content-Type: application/json
{
"invocationId": "<your invocationId>",
"callbackToken": "<your callbackToken>",
"action": "<action_name>",
...action-specific params
}
🟩 WeCom(企业微信)能力
| 操作 | action | 说明 |
|---|---|---|
| 创建文档 | create_doc |
Markdown 文档 |
| 创建智能表格 | create_smart_table |
自定义字段 + 数据行 |
| 创建待办 | create_todo |
分发给指定人员 |
| 创建会议 | create_meeting |
预约会议,自动邀请 |
| 黄金链路 | golden_chain |
一句话 → 文档 + 表格 + 待办 + 会议 |
WeCom golden_chain 示例
{
"action": "golden_chain",
"docName": "Q2 产品 PRD",
"docContent": "# Q2 产品规划\n...",
"tableName": "Q2 任务跟踪表",
"tasks": [
{ "content": "完成 API 设计", "assigneeUserId": "zhangsan", "remindTime": "2026-04-20 09:00:00" }
],
"meetingTitle": "Q2 PRD 评审会",
"meetingStart": "2026-04-20 14:00",
"meetingDurationSeconds": 3600,
"meetingInviteeUserIds": ["zhangsan", "lisi"]
}
WeCom 单独操作
// create_doc
{ "action": "create_doc", "docName": "会议纪要", "content": "..." }
// create_smart_table
{
"action": "create_smart_table",
"tableName": "Bug 跟踪表",
"fields": [
{ "fieldTitle": "Bug", "fieldType": "FIELD_TYPE_TEXT" },
{ "fieldTitle": "优先级", "fieldType": "FIELD_TYPE_SINGLE_SELECT" }
],
"records": [{ "Bug": "登录超时", "优先级": "P1" }]
}
// create_todo
{ "action": "create_todo", "content": "Review PRD", "followerUserIds": ["zhangsan"], "remindTime": "2026-04-20 09:00:00" }
// create_meeting
{ "action": "create_meeting", "title": "评审", "startDatetime": "2026-04-20 14:00", "durationSeconds": 3600, "inviteeUserIds": ["zhangsan"] }
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.
- 8d ago First seen · 229 lines · 114 tokens per session scan A 4d9d8ec2112e
enterprise-workflow is a skill published in the GitHub repository zts212653/clowder-ai (2,924 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 2,317 once invoked, about $0.0006 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 2026-08-30.
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