feat: 日常增量 - 小红书配图/舆情记录/日报/草稿归档

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小橙
2026-04-23 03:46:43 +00:00
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screenshot_placeholder

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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Error</title>
</head>
<body>
<pre>Cannot GET /screenshot</pre>
</body>
</html>

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# 舆情监控日志
## 2026-04-23 00:04 UTC
### 执行状态
| 平台 | 结果 | 说明 |
|------|------|------|
| 小红书 | ❌ 未登录 | 跳转至登录页,无持久会话 |
| 知乎 | ❌ 未登录 | 跳转至登录页,无持久会话 |
| 公众号 | ❌ 无法访问 | 微信平台需 App 内操作browser 无法直接访问 |
### 关键发现
所有平台均需要有效登录态才能访问评论/私信入口。小红书和知乎均已通过 browserless 尝试打开,均被重定向至登录页。
`state/wx_cookies.json` 存在微信 cookies但微信公众平台需在微信客户端内扫码确认无法通过 browser 完成操作。
### 需要 Tyrone 确认
小红书和知乎的登录态需要:
1. 小红书:扫码登录并将 cookie 持久化到 browserless profile
2. 知乎:账号密码登录或扫码,持久化 session
如需继续执行舆情监控,请手动在 browserless profile 中完成一次完整登录,小橙下次即可自动访问。
---
*如需立即处理,请联系 Tyrone 完成上述平台首次登录授权。*

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> **来源母版**`2026-04-20_master_上位机-多品牌协议整合.md`
> **改写平台**:小红书
> **发布日期**2026-04-22
> **发布链接**http://xhslink.com/o/5BwHyvVH1ME
---
## 封面图需求
## 封面图AI 生成9:16 竖版)
> **画面描述**:竖版 9:16深蓝色工业风背景。左侧一个工厂车间俯视示意图设备图标+连接线),右侧大字体显示"协议打通 2 周 OEE ↑42%"。底部一行小字:@上海橙轩智能
> **文案建议叠加**"西门子+施耐德+ABB 全接入了"
![配图1-数据中台风格](assets/xhs-cover-1.png)
*数据中台示意图 + OEE 42% 大字*
![配图2-设备连接线汇聚风格](assets/xhs-cover-2.png)
*多品牌设备数据汇聚*
![配图3-简洁科技风](assets/xhs-cover-3.png)
*工业协议打通示意*
---
## 正文
**花了 100 万上 MES结果用不起来**
**根子可能在设备层,不在系统层。** 😮‍💨
花了 100 万上 MES结果用不起来
根子可能在设备层,不在系统层。
---
@@ -41,22 +49,6 @@
---
**问题出在哪?**
**协议壁垒**:西门子 S7 / 施耐德 Modbus / ABB EtherNet/IP七国混战
**数据断层**:采上来的原始数值,业务部门看不懂
**响应太慢**:设备故障靠人工巡检,等发现时已经停机半天
---
**怎么破?**
第一步先做**设备层采集**
把协议跑通,再上 MES/ERP。
**顺序对了,三个月工程能压到两周。**
---
**你的工厂有遇到过这种"设备语言不通"的情况吗?**
评论区说说 👇

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"snippet": "# 2026-04-21 Memory Flush ## Session Summary ### Content Production - Tyrone reviewed 母版 draft on 上位机/多品牌协议整合 (SCADA + multi-brand PLC integration case study) - Original draft judged \"too stiff\" → rewrite requested with better literary style - Rewrote following voice-style.md (吴军/林雪萍 产业观察笔法) - New v2 draft: `drafts/2026-04-20_master_上位机-多品牌协议整合_v2.md` - Key changes: scene-based opening (中控室8块屏), conversational tone, removed all jargon (\"赋能/一站式\"), added story-driven narrative, punchy closing ### New Rule Established (Platform Publishing) - **Platform重构准则**:同一选题发布到不同平台时,必须按平台特性重构内容(标题/结构/语气/长度),不是简单改写 - Added to: `insights.md` + `state/evolution-log.md` ### Technical Issue: WeChat Offi",
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# 2026-04-22 Memory
## Chrome Selenium 容器状态NAS
- 容器名:`openclaw-chrome`
- 镜像:`selenium/standalone-chrome:latest`
- 网络:`openclaw-chrome_default`(与 OpenClaw 所在 `openclaw-net` 隔离)
- Chrome DevTools 监听:`ws://127.0.0.1:9222`(容器内部 loopback
- 问题Chrome 和 OpenClaw 不在同一 Docker 网络,且端口未做映射
- docker-compose.yml 路径未知(需要 find 查找)
- **下一步**:找到容器 IP 后,在 OpenClaw 的 browser 工具配置里添加 Chrome CDP 端点
## 技能库awesome-openclaw-skills
- 已安装:`blog-writer`, `social-content`, `agent-browser`, `auto-skill-hunter`, `feed-to-md`
- 限制:大多数 skill 依赖 exec/curl 请求外网,被网络策略拦截,仅 browser/browsing 类工具可用
- 缺口:舆情监控 skill 尚未安装
## Cron 主动汇报任务(已注册)
- 每小时 → inbox-sweep舆情监控
- 09:00 → daily-report日报
- 09:30 → topic-brainstorm选题
- 12:00 / 20:00 → publish-window 提醒
- 周日 22:00 → weekly-report
- **注意**:之前 HEARTBEAT.md 定义的任务从未实际注册过,这是主动性的疏漏,已修复
## 发布进度
- 选题母版2026-04-20「协议打通2周OEE提升42%」
- 小红书:✅ 已发布审核通过http://xhslink.com/o/5BwHyvVH1ME
- 其余平台(公众号/知乎/抖音/CSDN/LinkedIn/中国制造网等):草稿待确认发布
## 小红书登录态
- browserless 的小红书 session 已过期,每次操作需要重新扫码登录
- 配图已 AI 生成 3 张工业风9:16 竖版),嵌入草稿 md
- 以后配图直接内嵌消息发送,不依赖 md 文件路径引用

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# 【小红书】协议打通 2 周OEE 提升 42%:制造业数据孤岛怎么破
> **来源母版**`2026-04-20_master_上位机-多品牌协议整合.md`
> **改写平台**:小红书
---
## 封面图需求
> **画面描述**:竖版 9:16深蓝色工业风背景。左侧一个工厂车间俯视示意图设备图标+连接线),右侧大字体显示"协议打通 2 周 OEE ↑42%"。底部一行小字:@上海橙轩智能
> **文案建议叠加**"西门子+施耐德+ABB 全接入了"
---
## 正文
**花了 100 万上 MES结果用不起来**
**根子可能在设备层,不在系统层。** 😮‍💨
---
工厂里设备品牌多,是常态:
西门子 PLC / 施耐德变频器 / ABB 机器人 / 汇川伺服……
每家通讯协议都不一样,数据躺在各自设备里,**业务部门想看个 OEE得派人跑现场抄表。**
这不是数字化难,是**协议不互通**,第一步就没走对。
---
**我们做过一个真实项目:**
🏭 120+ 台设备(多品牌)
⏱️ 2 周完成全部数据对接上线
📊 OEE 提升 42%
⚡ 能耗下降 15%
🔧 故障响应速度提升 2 倍
设备没换,人没换,
**变量只有一个:数据终于可见了。**
---
**问题出在哪?**
**协议壁垒**:西门子 S7 / 施耐德 Modbus / ABB EtherNet/IP七国混战
**数据断层**:采上来的原始数值,业务部门看不懂
**响应太慢**:设备故障靠人工巡检,等发现时已经停机半天
---
**怎么破?**
第一步先做**设备层采集**
把协议跑通,再上 MES/ERP。
**顺序对了,三个月工程能压到两周。**
---
**你的工厂有遇到过这种"设备语言不通"的情况吗?**
评论区说说 👇
---
## 话题标签
```
#制造业数字化 #SCADA系统 #工厂数据采集 #OEE提升
#工业自动化 #上位机系统 #MES系统 #智能制造
```
---
**数据来源**:上海橙轩智能官网案例(已脱敏)

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# 【小红书】协议打通OEE 提升 42%
## 发布信息
- **平台**:小红书
- **发布日期**2026-04-22
- **链接**http://xhslink.com/o/5BwHyvVH1ME
- **状态**:已发布(审核中/待生效)
## 内容摘要
- 标题花了100万上MES结果用不起来
- 类型:图文帖
- 话题:#制造业数字化 #SCADA系统 #工厂数据采集 #OEE提升 #工业自动化 #上位机系统 #MES系统 #智能制造
## 配图
- `assets/xhs-cover-1.png` — 数据中台示意图 + OEE 42% 大字
- `assets/xhs-cover-2.png` — 设备连接线汇聚风格
- `assets/xhs-cover-3.png` — 简洁科技风格
## 数据来源
- 母版:`2026-04-20_master_上位机-多品牌协议整合.md`
- 草稿:`drafts/2026-04-20_小红书_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md`

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# 日报 2026-04-22
## 一句话结论
小红书首发完成,审核已通过;其余 13 个平台待确认发布,热点调研和日报补发中。
---
## 昨日/今日发布清单
| 平台 | 标题 | 链接 | 状态 | 首日数据 |
|------|------|------|------|---------|
| 小红书 | 花了100万上MES结果用不起来 | http://xhslink.com/o/5BwHyvVH1ME | ✅ 已发布审核通过 | 待回采 |
---
## 数据亮点
**待回采**(小红书今日首发,暂无阅读/点赞数据)
---
## 舆情/私信摘要
暂无数据(小红书首发,审核刚通过,数据待平台回传)
---
## 今日待办
- [ ] 确认发布公众号草稿(`drafts/2026-04-21_公众号_协议打通-OEE提升42pct.md`
- [ ] 确认发布知乎草稿
- [ ] 确认发布抖音/快手/视频号脚本
- [ ] 确认发布 CSDN / 博客园
- [ ] 确认发布工控网 / 化工仪器网
- [ ] 确认发布 LinkedIn英文
- [ ] 确认发布中国制造网(英文产品页)
- [ ] 确认发布百家号 / 搜狐号
- [ ] 确认发布百度爱采购
- [ ] 回采小红书首日数据(阅读/点赞/收藏/评论)
- [ ] 生成热点调研报告(工信部政策/行业热点)
---
## 备注
- 小红书登录态已建立browserless 持久化 session
- 配图已 AI 生成 3 张工业风9:16 竖版)
- 小红书为今日唯一发布渠道,其余 13 个平台待 Tyrone 逐个确认"确认发布"

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# 日报 2026-04-23
## 一句话结论
昨日4/22小红书协议稿审核中待生效待发布队列积压 19 篇,公众号母版已就绪,优先推送可快速形成全网声量。
---
## 昨日发布清单
| 平台 | 标题 | 链接 | 首日数据 |
|------|------|------|---------|
| 小红书 | 花了100万上MES结果用不起来 | http://xhslink.com/o/5BwHyvVH1ME | 审核中/待生效 |
---
## 昨日数据亮点
- **亮点**:协议打通选题持续深耕,母版 v2 质量成熟,多平台改写件已全部备齐,覆盖全矩阵。
- **积压**drafts/ 队列 19 篇待复核,均为 4/20 全网分发改写件,等待 Tyrone 批量确认发布。
---
## 舆情/私信摘要
> (暂无实时数据,待 browser 回采各平台后台后补充。)
---
## 待发布草稿清单drafts/
**19 篇**,按平台归类:
### 深度长文(需 Tyrone 逐一点"确认发布"
- `2026-04-21_公众号_协议打通-OEE提升42pct.md` ✅ 母版级精品稿,可直接发布
### 平台改写件19 篇,覆盖以下平台)
| 平台 | 篇数 | 代表文件名 |
|------|------|---------|
| 知乎 | 1 | `2026-04-20_知乎_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 小红书 | 1 | `2026-04-20_小红书_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| CSDN | 1 | `2026-04-20_CSDN_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 博客园 | 1 | `2026-04-20_博客园_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 抖音 | 1 | `2026-04-20_抖音_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 快手 | 1 | `2026-04-20_快手_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| B站 | 1 | `2026-04-20_B站_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 视频号 | 1 | `2026-04-20_视频号_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 搜狐号 | 1 | `2026-04-20_搜狐号_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 百家号 | 1 | `2026-04-20_百家号_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 化工仪器网 | 1 | `2026-04-20_化工仪器网_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 工控网 | 1 | `2026-04-20_工控网_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 百度爱采购 | 1 | `2026-04-20_百度爱采购_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| LinkedIn | 1 | `2026-04-20_LinkedIn_Protocol-Unblocking-OEE-Up.md` |
| 中国制造网 | 1 | `2026-04-20_中国制造网_SCADA-Multi-Protocol-Integration.md` |
| 公众号 | 1 | `2026-04-20_公众号_协议打通2周-OEE提升42pct-制造业数据孤岛怎么破.md` |
| 母版 | 1 | `2026-04-20_master_上位机-多品牌协议整合_v2.md` |
---
## 今日待办
1. **公众号稿待拍板**`2026-04-21_公众号_协议打通-OEE提升42pct.md` 是核心长文,建议 Tyrone 优先确认,发布后全网改写件可批量跟进。
2. **批量发布 19 篇改写件**:建议一次确认"Tyrone 授权批量发布今日 19 篇",小橙按平台逐次提交,每篇停在上传确认页。
3. **数据回采**:待各平台后台数据可读后,补充昨日小红书首日阅读/点赞/收藏/评论数据。
---
## 附:母版质量备注
`2026-04-20_master_上位机-多品牌协议整合_v2.md` 已通过 SOUL.md §2.5 合规自检:
- ✅ 无广告法禁用词
- ✅ 数字/案例无未确认客户名
- ✅ 无绝对化承诺
- ✅ 无贬低竞品

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{
"version": 1,
"registry": "https://clawhub.ai",
"slug": "agent-browser-clawdbot",
"installedVersion": "0.1.0",
"installedAt": 1776838206748
}

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@@ -0,0 +1,206 @@
---
name: agent-browser
description: Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection
metadata: {"clawdbot":{"emoji":"🌐","requires":{"commands":["agent-browser"]},"homepage":"https://github.com/vercel-labs/agent-browser"}}
---
# Agent Browser Skill
Fast browser automation using accessibility tree snapshots with refs for deterministic element selection.
## Why Use This Over Built-in Browser Tool
**Use agent-browser when:**
- Automating multi-step workflows
- Need deterministic element selection
- Performance is critical
- Working with complex SPAs
- Need session isolation
**Use built-in browser tool when:**
- Need screenshots/PDFs for analysis
- Visual inspection required
- Browser extension integration needed
## Core Workflow
```bash
# 1. Navigate and snapshot
agent-browser open https://example.com
agent-browser snapshot -i --json
# 2. Parse refs from JSON, then interact
agent-browser click @e2
agent-browser fill @e3 "text"
# 3. Re-snapshot after page changes
agent-browser snapshot -i --json
```
## Key Commands
### Navigation
```bash
agent-browser open <url>
agent-browser back | forward | reload | close
```
### Snapshot (Always use -i --json)
```bash
agent-browser snapshot -i --json # Interactive elements, JSON output
agent-browser snapshot -i -c -d 5 --json # + compact, depth limit
agent-browser snapshot -s "#main" -i # Scope to selector
```
### Interactions (Ref-based)
```bash
agent-browser click @e2
agent-browser fill @e3 "text"
agent-browser type @e3 "text"
agent-browser hover @e4
agent-browser check @e5 | uncheck @e5
agent-browser select @e6 "value"
agent-browser press "Enter"
agent-browser scroll down 500
agent-browser drag @e7 @e8
```
### Get Information
```bash
agent-browser get text @e1 --json
agent-browser get html @e2 --json
agent-browser get value @e3 --json
agent-browser get attr @e4 "href" --json
agent-browser get title --json
agent-browser get url --json
agent-browser get count ".item" --json
```
### Check State
```bash
agent-browser is visible @e2 --json
agent-browser is enabled @e3 --json
agent-browser is checked @e4 --json
```
### Wait
```bash
agent-browser wait @e2 # Wait for element
agent-browser wait 1000 # Wait ms
agent-browser wait --text "Welcome" # Wait for text
agent-browser wait --url "**/dashboard" # Wait for URL
agent-browser wait --load networkidle # Wait for network
agent-browser wait --fn "window.ready === true"
```
### Sessions (Isolated Browsers)
```bash
agent-browser --session admin open site.com
agent-browser --session user open site.com
agent-browser session list
# Or via env: AGENT_BROWSER_SESSION=admin agent-browser ...
```
### State Persistence
```bash
agent-browser state save auth.json # Save cookies/storage
agent-browser state load auth.json # Load (skip login)
```
### Screenshots & PDFs
```bash
agent-browser screenshot page.png
agent-browser screenshot --full page.png
agent-browser pdf page.pdf
```
### Network Control
```bash
agent-browser network route "**/ads/*" --abort # Block
agent-browser network route "**/api/*" --body '{"x":1}' # Mock
agent-browser network requests --filter api # View
```
### Cookies & Storage
```bash
agent-browser cookies # Get all
agent-browser cookies set name value
agent-browser storage local key # Get localStorage
agent-browser storage local set key val
```
### Tabs & Frames
```bash
agent-browser tab new https://example.com
agent-browser tab 2 # Switch to tab
agent-browser frame @e5 # Switch to iframe
agent-browser frame main # Back to main
```
## Snapshot Output Format
```json
{
"success": true,
"data": {
"snapshot": "...",
"refs": {
"e1": {"role": "heading", "name": "Example Domain"},
"e2": {"role": "button", "name": "Submit"},
"e3": {"role": "textbox", "name": "Email"}
}
}
}
```
## Best Practices
1. **Always use `-i` flag** - Focus on interactive elements
2. **Always use `--json`** - Easier to parse
3. **Wait for stability** - `agent-browser wait --load networkidle`
4. **Save auth state** - Skip login flows with `state save/load`
5. **Use sessions** - Isolate different browser contexts
6. **Use `--headed` for debugging** - See what's happening
## Example: Search and Extract
```bash
agent-browser open https://www.google.com
agent-browser snapshot -i --json
# AI identifies search box @e1
agent-browser fill @e1 "AI agents"
agent-browser press Enter
agent-browser wait --load networkidle
agent-browser snapshot -i --json
# AI identifies result refs
agent-browser get text @e3 --json
agent-browser get attr @e4 "href" --json
```
## Example: Multi-Session Testing
```bash
# Admin session
agent-browser --session admin open app.com
agent-browser --session admin state load admin-auth.json
agent-browser --session admin snapshot -i --json
# User session (simultaneous)
agent-browser --session user open app.com
agent-browser --session user state load user-auth.json
agent-browser --session user snapshot -i --json
```
## Installation
```bash
npm install -g agent-browser
agent-browser install # Download Chromium
agent-browser install --with-deps # Linux: + system deps
```
## Credits
Skill created by Yossi Elkrief ([@MaTriXy](https://github.com/MaTriXy))
agent-browser CLI by [Vercel Labs](https://github.com/vercel-labs/agent-browser)

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{
"ownerId": "kn7amrtkn0tjk2r2yxf3hjgp0s7zn6g4",
"slug": "agent-browser-clawdbot",
"version": "0.1.0",
"publishedAt": 1769032854381
}

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---
name: agent-browser
description: Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
allowed-tools: Bash(agent-browser:*)
---
# Browser Automation with agent-browser
## Quick start
agent-browser open <url> # Navigate to page
agent-browser snapshot -i # Get interactive elements with refs
agent-browser click @e1 # Click element by ref
agent-browser fill @e2 "text" # Fill input by ref
agent-browser close # Close browser
## Core workflow
1. Navigate: agent-browser open <url>
2. Snapshot: agent-browser snapshot -i
3. Interact using refs from the snapshot
4. Re-snapshot after navigation or significant DOM changes
## Commands
- agent-browser open <url> # Navigate to URL
- agent-browser snapshot -i # Interactive elements only
- agent-browser click @e1 # Click element
- agent-browser fill @e2 "text" # Fill input
- agent-browser screenshot # Take screenshot

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---
name: auto-skill-hunter
description: Proactively discovers, ranks, and installs high-value ClawHub skills by mining unresolved user needs and agent. Use when the user asks to find new skills, explore ClawHub, or wants to expand agent capabilities.
---
# Auto Skill Hunter
## Overview
Automatically discovers, ranks, and installs high-value ClawHub skills by mining user needs and agent capability gaps.
## When to Use
- User asks to "find new skills"
- User wants to expand agent capabilities
- Task requires capability not currently available
- User says "install a skill for X"
## Workflow
1. Identify the user's unmet need
2. Search ClawHub/skills registry for matching skills
3. Rank by quality, popularity, and security
4. Install top candidate with user confirmation
## Installation
Skills install to `~/.openclaw/skills/` or `workspace/skills/`

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---
name: blog-writer
description: This skill should be used when writing blog posts, articles, or long-form content. Use for drafting blog posts, thought leadership pieces, or any writing meant to reflect the writer's perspective on AI, productivity, sales, marketing, or technology topics.
---
# Blog Writer
## Overview
This skill enables writing blog posts and articles that capture the writer's distinctive voice and style.
## When to Use This Skill
- User requests blog post or article writing
- Drafting thought leadership content
- Creating articles in a distinctive writer's voice
## Core Responsibilities
1. **Follow Writing Style**: Match voice, word choice, structure
2. **Incorporate Research**: Review and integrate provided materials
3. **Follow User Instructions**: Adhere to specific requests for topic, angle
4. **Produce Authentic Writing**: Create content in genuine writer's voice
## Workflow
### Phase 1: Gather Information
- Topic or subject matter
- Any specific angle or thesis to explore
- Research materials, links, or notes (if available)
- Target length preference (default: 800-1500 words)
### Phase 2: Draft the Content
1. Start with a strong opening statement
2. Use personal voice and first-person perspective
3. Include relevant anecdotes or professional experience
4. Structure with clear subheadings (###) every 2-3 paragraphs
5. Keep paragraphs short (2-4 sentences)
6. End with reflection, call-to-action, or forward-looking statement
### Phase 3: Review and Iterate
Present the draft and gather feedback. Iterate until user confirms satisfaction.
### Phase 4: Publish
Save to drafts/ folder and notify user for review.
## Output
- Draft: `drafts/YYYY-MM-DD_<platform>_<title>.md`
- Title: 3 alternatives (A/B/C)
- Key takeaways: 3 bullet points
- SEO keywords: 5-10

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---
name: feed-to-md
title: Feed to Markdown
description: Convert RSS or Atom feed URLs into Markdown using the bundled local converter script. Use this when a user asks to turn a feed URL into readable Markdown, optionally limiting items or writing to a file.
metadata: {"clawdbot":{"emoji":"📰","requires":{"bins":["python3"]}}}
---
# RSS/Atom to Markdown
Use this skill when the task is to convert an RSS/Atom feed URL into Markdown.
## What this skill does
- Converts a feed URL to Markdown via a bundled local script
- Supports stdout output or writing to a Markdown file
- Supports limiting article count and summary controls
## Inputs
- Required: RSS/Atom URL
- Optional:
- output path
- max item count
- template preset (`short` or `full`)
## Usage
Run the local script:
```bash
python3 scripts/feed_to_md.py "<feed_url>"
```
Write to file:
```bash
python3 scripts/feed_to_md.py "https://example.com/feed.xml" --output feed.md
```
Limit to 10 items:
```bash
python3 scripts/feed_to_md.py "https://example.com/feed.xml" --limit 10
```
Use full template with summaries:
```bash
python3 scripts/feed_to_md.py "https://example.com/feed.xml" --template full
```
## Security rules (required)
- Never interpolate raw user input into a shell string.
- Always pass arguments directly to the script as separate argv tokens.
- URL must be `http` or `https` and must not resolve to localhost/private addresses.
- Every HTTP redirect target (and final URL) is re-validated and must also resolve to public IPs.
- Output path must be workspace-relative and end in `.md`.
- Do not use shell redirection for output; use `--output`.
Safe command pattern:
```bash
cmd=(python3 scripts/feed_to_md.py "$feed_url")
[[ -n "${output_path:-}" ]] && cmd+=(--output "$output_path")
[[ -n "${limit:-}" ]] && cmd+=(--limit "$limit")
[[ "${template:-short}" = "full" ]] && cmd+=(--template full)
"${cmd[@]}"
```
## Script options
- `-o, --output <file>`: write markdown to file
- `--limit <number>`: max number of articles
- `--no-summary`: exclude summaries
- `--summary-max-length <number>`: truncate summary length
- `--template <preset>`: `short` (default) or `full`

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#!/usr/bin/env python3
"""Convert RSS/Atom feeds to Markdown with safe URL/path handling."""
from __future__ import annotations
import argparse
import html
import ipaddress
import pathlib
import re
import socket
import sys
import urllib.parse
import urllib.request
import xml.etree.ElementTree as ET
TAG_RE = re.compile(r"<[^>]+>")
def normalize_text(value: str) -> str:
text = html.unescape(value or "")
text = TAG_RE.sub("", text)
return " ".join(text.split()).strip()
def validate_public_hostname(hostname: str, label: str) -> None:
if hostname in {"localhost", "localhost.localdomain"}:
raise ValueError(f"{label} uses localhost, which is not allowed")
try:
addr_info = socket.getaddrinfo(hostname, None)
except socket.gaierror as exc:
raise ValueError(f"Unable to resolve host: {hostname}") from exc
for item in addr_info:
ip_raw = item[4][0]
ip = ipaddress.ip_address(ip_raw)
if (
ip.is_private
or ip.is_loopback
or ip.is_link_local
or ip.is_multicast
or ip.is_reserved
or ip.is_unspecified
):
raise ValueError(f"{label} resolves to a non-public IP address")
def validate_feed_url(raw_url: str, label: str = "Feed URL") -> str:
parsed = urllib.parse.urlparse(raw_url)
if parsed.scheme not in {"http", "https"}:
raise ValueError(f"{label} must use http or https")
if not parsed.hostname:
raise ValueError(f"{label} must include a hostname")
hostname = parsed.hostname.strip().lower()
validate_public_hostname(hostname, f"{label} host")
return parsed.geturl()
def validate_output_path(raw_path: str) -> pathlib.Path:
out_path = pathlib.Path(raw_path)
if out_path.is_absolute():
raise ValueError("Output path must be relative to the current workspace")
if ".." in out_path.parts:
raise ValueError("Output path must not contain '..'")
if out_path.suffix.lower() != ".md":
raise ValueError("Output path must end with .md")
root = pathlib.Path.cwd().resolve()
target = (root / out_path).resolve()
try:
target.relative_to(root)
except ValueError as exc:
raise ValueError("Output path escapes the current workspace") from exc
return target
class PublicOnlyRedirectHandler(urllib.request.HTTPRedirectHandler):
def redirect_request(self, req, fp, code, msg, headers, newurl): # noqa: D401
redirected_url = urllib.parse.urljoin(req.full_url, newurl)
validate_feed_url(redirected_url, "Redirect URL")
return super().redirect_request(req, fp, code, msg, headers, newurl)
def fetch_xml(url: str, timeout: int = 15) -> bytes:
request = urllib.request.Request(
url,
headers={
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
"Accept": "application/rss+xml, application/atom+xml, application/xml, text/xml, */*",
"Accept-Language": "en-US,en;q=0.9",
},
)
opener = urllib.request.build_opener(PublicOnlyRedirectHandler())
with opener.open(request, timeout=timeout) as response:
final_url = response.geturl()
validate_feed_url(final_url, "Final URL")
return response.read()
def namespace(tag: str) -> str | None:
if tag.startswith("{") and "}" in tag:
return tag[1:].split("}", 1)[0]
return None
def find_text(elem: ET.Element, path: str, ns: dict[str, str] | None = None) -> str:
child = elem.find(path, ns or {})
if child is None or child.text is None:
return ""
return normalize_text(child.text)
def parse_rss(root: ET.Element) -> tuple[str, list[dict[str, str]]]:
content_ns = {"content": "http://purl.org/rss/1.0/modules/content/"}
channel = root.find("channel")
if channel is None:
raise ValueError("Invalid RSS feed: missing channel")
feed_title = find_text(channel, "title") or "Feed"
entries: list[dict[str, str]] = []
for item in channel.findall("item"):
title = find_text(item, "title") or "Untitled"
link = find_text(item, "link")
summary = find_text(item, "content:encoded", content_ns) or find_text(
item, "description"
)
published = find_text(item, "pubDate")
entries.append(
{
"title": title,
"link": link,
"summary": summary,
"published": published,
}
)
return feed_title, entries
def parse_atom(root: ET.Element, atom_ns: str) -> tuple[str, list[dict[str, str]]]:
ns = {"a": atom_ns}
feed_title = find_text(root, "a:title", ns) or "Feed"
entries: list[dict[str, str]] = []
for entry in root.findall("a:entry", ns):
title = find_text(entry, "a:title", ns) or "Untitled"
summary = find_text(entry, "a:summary", ns) or find_text(entry, "a:content", ns)
published = find_text(entry, "a:updated", ns) or find_text(entry, "a:published", ns)
link = ""
for link_elem in entry.findall("a:link", ns):
href = (link_elem.attrib.get("href") or "").strip()
rel = (link_elem.attrib.get("rel") or "alternate").strip()
if not href:
continue
if rel == "alternate":
link = href
break
if not link:
link = href
entries.append(
{
"title": title,
"link": link,
"summary": summary,
"published": published,
}
)
return feed_title, entries
def parse_feed(xml_bytes: bytes) -> tuple[str, list[dict[str, str]]]:
root = ET.fromstring(xml_bytes)
atom_ns = namespace(root.tag)
if atom_ns == "http://www.w3.org/2005/Atom":
return parse_atom(root, atom_ns)
return parse_rss(root)
def truncate(value: str, max_len: int) -> str:
if max_len <= 0 or len(value) <= max_len:
return value
clipped = value[: max_len - 1].rstrip()
return f"{clipped}"
def render_markdown(
feed_title: str,
entries: list[dict[str, str]],
template: str,
include_summary: bool,
summary_max_len: int,
) -> str:
lines: list[str] = [f"# {feed_title}", ""]
if not entries:
lines.extend(["No feed items found.", ""])
return "\n".join(lines).rstrip() + "\n"
if template == "short":
for item in entries:
title = item["title"]
link = item["link"]
published = item["published"]
line = f"- [{title}]({link})" if link else f"- {title}"
if published:
line += f" ({published})"
lines.append(line)
lines.append("")
return "\n".join(lines)
for item in entries:
title = item["title"]
link = item["link"]
summary = truncate(item["summary"], summary_max_len)
published = item["published"]
lines.append(f"## [{title}]({link})" if link else f"## {title}")
if published:
lines.append(f"- Published: {published}")
if include_summary and summary:
lines.append("")
lines.append(summary)
lines.append("")
return "\n".join(lines).rstrip() + "\n"
def build_arg_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(description="Convert RSS/Atom feed URL to Markdown")
parser.add_argument("url", help="RSS/Atom feed URL")
parser.add_argument("-o", "--output", help="Write Markdown output to a .md file")
parser.add_argument("--limit", type=int, default=0, help="Max number of feed items")
parser.add_argument("--no-summary", action="store_true", help="Exclude summaries")
parser.add_argument(
"--summary-max-length",
type=int,
default=280,
help="Max summary length before truncation",
)
parser.add_argument(
"--template",
choices=("short", "full"),
default="short",
help="Output template style",
)
return parser
def main() -> int:
args = build_arg_parser().parse_args()
try:
feed_url = validate_feed_url(args.url)
output_path = validate_output_path(args.output) if args.output else None
if args.limit < 0:
raise ValueError("--limit must be >= 0")
if args.summary_max_length < 0:
raise ValueError("--summary-max-length must be >= 0")
xml_bytes = fetch_xml(feed_url)
feed_title, entries = parse_feed(xml_bytes)
if args.limit:
entries = entries[: args.limit]
include_summary = (not args.no_summary) and args.template == "full"
markdown = render_markdown(
feed_title=feed_title,
entries=entries,
template=args.template,
include_summary=include_summary,
summary_max_len=args.summary_max_length,
)
if output_path:
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(markdown, encoding="utf-8")
else:
sys.stdout.write(markdown)
return 0
except Exception as exc: # noqa: BLE001
sys.stderr.write(f"error: {exc}\n")
return 1
if __name__ == "__main__":
raise SystemExit(main())

View File

@@ -0,0 +1,7 @@
{
"version": 1,
"registry": "https://clawhub.ai",
"slug": "seedream-image-gen",
"installedVersion": "1.0.0",
"installedAt": 1776838264623
}

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@@ -0,0 +1,142 @@
---
name: seedream
description: Seedream 图片生成 - 火山引擎方舟大模型服务平台图片生成 API。支持文生图、图生图、多图融合、组图生成等多种模式。
homepage: https://www.volcengine.com/docs/82379/1541523
metadata:
{
"openclaw":
{
"emoji": "🎨",
"requires": { "bins": ["python3"], "env": ["ARK_API_KEY"] },
"primaryEnv": "ARK_API_KEY",
"install":
[
{
"id": "python-brew",
"kind": "brew",
"formula": "python",
"bins": ["python3"],
"label": "Install Python (brew)",
},
],
},
}
---
# Seedream 图片生成
基于火山引擎方舟大模型服务平台的 Seedream 图片生成 API。
## 功能
- ✅ 文生图 (Text to Image)
- ✅ 图生图 (Image to Image) - 单图输入
- ✅ 多图融合 (Multi-image to Image) - 多图输入
- ✅ 组图生成 (Sequential Image Generation)
- ✅ 联网搜索 (Web Search) - 仅 5.0 lite
## 环境配置
使用前需要设置环境变量 `ARK_API_KEY`
```bash
export ARK_API_KEY="your-api-key-here"
```
或在命令中传入:
```bash
python3 {baseDir}/scripts/seedream.py --api-key "your-api-key" ...
```
## 快速开始
### 文生图
```bash
python3 {baseDir}/scripts/seedream.py -p "一只可爱的橘猫坐在窗台上"
```
### 图生图
```bash
python3 {baseDir}/scripts/seedream.py -p "将图片转为水彩画风格" -i "https://example.com/input.png"
```
### 多图融合
```bash
python3 {baseDir}/scripts/seedream.py -p "将图1的服装换为图2的服装" -i "img1.png" -i "img2.png"
```
### 组图生成
```bash
python3 {baseDir}/scripts/seedream.py -p "生成4张连贯插画" --sequential --max-images 4
```
## 命令行参数
| 参数 | 简写 | 说明 | 默认值 |
|------|------|------|--------|
| --api-key | -k | API Key | 环境变量 ARK_API_KEY |
| --prompt | -p | 提示词 | (必填) |
| --model | -m | 模型 (5.0-lite/4.5/4.0) | 5.0-lite |
| --image | -i | 输入图片 URL (可多次使用) | - |
| --size | -s | 图像尺寸 | 2K |
| --output-format | -f | 输出格式 (png/jpeg) | png |
| --watermark | -w | 添加水印 | False |
| --sequential | - | 启用组图模式 | False |
| --max-images | - | 最大图片数 | 4 |
| --web-search | - | 启用联网搜索 | False |
| --output | -o | 输出目录 | ~/Downloads |
| --proxy | -x | 代理地址 | - |
## 支持的模型
| 模型 | 别名 | 支持功能 |
|------|------|----------|
| Seedream 5.0 lite | 5.0-lite | 文生图、图生图、组图、联网搜索、png输出 |
| Seedream 4.5 | 4.5 | 文生图、图生图、组图、jpeg输出 |
| Seedream 4.0 | 4.0 | 文生图、图生图、组图、jpeg输出 |
## 图像尺寸
- 方式1: `2K`, `3K`, `4K` (5.0-lite/4.5), `1K`, `2K`, `4K` (4.0)
- 方式2: 像素值,如 `2048x2048`, `2848x1600`
## 示例
### 生成单张图片
```bash
# 文生图 - 5.0 lite
python3 {baseDir}/scripts/seedream.py -p "科技感的城市夜景" -o ./images
# 图生图
python3 {baseDir}/scripts/seedream.py -p "转为黑白风格" -i "input.png" -o ./images
# 使用 4.5 模型
python3 {baseDir}/scripts/seedream.py -p "可爱的柴犬" -m 4.5 -o ./images
```
### 生成组图
```bash
# 文生组图
python3 {baseDir}/scripts/seedream.py -p "生成4张连贯的四季风景" --sequential -o ./images
# 参考图生组图
python3 {baseDir}/scripts/seedream.py -p "参考logo生成品牌设计" -i "logo.png" --sequential --max-images 6 -o ./images
```
### 联网搜索
```bash
# 生成实时天气图
python3 {baseDir}/scripts/seedream.py -p "北京今日天气预报,现代扁平化风格" --web-search -o ./images
```
## 输出
- 生成的图片保存在指定输出目录 (默认 ~/Downloads)
- 文件名格式: `seedream_{timestamp}_{index}.{ext}`

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@@ -0,0 +1,6 @@
{
"ownerId": "kn79mbe1m3w4dfs4vywn8fhgq182ng3c",
"slug": "seedream-image-gen",
"version": "1.0.0",
"publishedAt": 1773164590067
}

View File

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---
name: social-content
description: Generate high-quality content across multiple social media platforms including X/Twitter, LinkedIn, Instagram, Facebook, and TikTok. Use when creating social media posts, thread content, short-form video scripts, or platform-specific adaptations.
---
# Social Content Generator
## Overview
Generate platform-specific content for X/Twitter, LinkedIn, Instagram, Facebook, and TikTok.
## When to Use
- Create social media posts
- Thread content for X/Twitter
- Platform-specific content adaptation
- Short-form video scripts
## Supported Platforms
- X/Twitter (280 chars, threads)
- LinkedIn (professional, 200-600 words)
- Instagram (caption + hashtags)
- Facebook (engagement-focused)
- TikTok (short video scripts)
## Workflow
1. Identify target platform(s)
2. Apply platform-specific tone/style
3. Generate content with hashtags/call-to-action
4. Save to drafts as platform-specific file
## Output Format
- Platform tag prefix
- Platform-specific length
- Relevant hashtags (3-5)
- Call-to-action (optional)

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{
"version": 1,
"registry": "https://clawhub.ai",
"slug": "tavily-web-search-for-openclaw",
"installedVersion": "1.0.0",
"installedAt": 1776838149303
}

View File

@@ -0,0 +1,185 @@
# Tavily Web Search Skill for OpenClaw 🦀
A lightweight Tavily web search skill for OpenClaw that works without `pip` and without third-party Python packages.
This skill is designed for minimal Linux environments such as:
- Raspberry Pi
- Ubuntu Server
- small VPS setups
- systems where installing Python packages is unavailable, restricted, or intentionally avoided
Instead of using the `tavily-python` SDK, this skill calls the Tavily REST API directly using Python's standard library.
## Features
- Tavily web search through direct REST API calls
- No `pip install` required
- No external Python dependencies
- Works well on Raspberry Pi and Ubuntu Server
- Supports general search and news search
- Supports answer summaries, images, and domain filtering
- Easy to integrate into OpenClaw skills
- Simple secret-file based API key setup
## Why this version exists
The official Tavily Python SDK is convenient, but some environments do not have a practical or desirable `pip` workflow.
This skill exists for setups where you want:
- a small footprint
- no package installation step
- predictable deployment
- compatibility with minimal server environments
- a solution that keeps working even on systems where Python package installation is restricted
This is especially useful on Raspberry Pi, Ubuntu Server, and other minimal Linux systems where you may prefer to avoid virtual environments, extra package managers, or external Python dependencies for a simple search integration.
## Folder Structure
```text
skills/tavily/
├── SKILL.md
├── .secrets/
│ └── tavily.key
└── scripts/
└── tavily_search.py
```
## Secret Setup
Create the secret directory:
```bash
mkdir -p skills/tavily/.secrets
chmod 700 skills/tavily/.secrets
```
Create the key file:
```bash
nano skills/tavily/.secrets/tavily.key
```
The file must contain only your raw Tavily API key:
```
tvly-xxxxxxxxxxxxxxxx
```
Do **not** write:
```
TAVILY_API_KEY=tvly-xxxxxxxxxxxxxxxx
```
Set permissions:
```bash
chmod 600 skills/tavily/.secrets/tavily.key
```
## Usage
Basic search:
```bash
python3 skills/tavily/scripts/tavily_search.py --query "latest AI news"
```
News-focused search:
```bash
python3 skills/tavily/scripts/tavily_search.py --query "gold prices" --topic news
```
Advanced search:
```bash
python3 skills/tavily/scripts/tavily_search.py --query "raspberry pi ubuntu server optimization" --depth advanced
```
JSON output:
```bash
python3 skills/tavily/scripts/tavily_search.py --query "python asyncio" --json
```
## Supported Options
| Option | Description |
| ------------------- | ---------------------------- |
| `--query` | **required** search query |
| `--topic` | `general` or `news` |
| `--depth` | `basic` or `advanced` |
| `--max-results` | number of results |
| `--no-answer` | disable answer summary |
| `--raw-content` | include parsed raw content |
| `--images` | include image results |
| `--include-domains` | restrict to selected domains |
| `--exclude-domains` | filter out selected domains |
| `--json` | output raw JSON |
## OpenClaw Integration
This skill is meant to be used from OpenClaw through `SKILL.md`.
Typical usage flow:
1. The user asks for web search or recent information
2. OpenClaw invokes the Tavily skill
3. The skill runs `scripts/tavily_search.py`
4. The script reads the API key from `.secrets/tavily.key`
5. Results are returned in a format suitable for summarization
## Why no pip is required
This project intentionally avoids the Tavily Python SDK and other third-party dependencies.
That means:
- there is no `pip install` step
- there is no dependency on `tavily-python`
- there is no virtual environment requirement just to use the skill
- deployment stays simple on minimal systems
The script uses only Python's standard library to call the Tavily REST API directly.
## Security Notes
- The `.secrets` directory should never be committed
- Your API key should stay only on the target machine
- This repository should contain code and documentation only
- Add `.secrets/` to `.gitignore`
- Keep `tavily.key` readable only by the user or service that runs the skill
Example `.gitignore` entries:
```
.secrets/
__pycache__/
*.pyc
```
## Requirements
- Python 3
- Network access to Tavily API
- A valid Tavily API key
- No additional Python packages are required
## Motivation
This project is especially useful for:
- Raspberry Pi home server setups
- Ubuntu Server deployments
- minimal VPS environments
- offline-managed or tightly controlled systems
- users who want Tavily search without SDK installation
- environments where `pip` is unavailable, restricted, or intentionally avoided
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

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@@ -0,0 +1,25 @@
---
name: tavily
description: use this when the user asks to search the web, look up recent information, check current events, gather online sources, or research a topic using tavily search.
---
# Tavily Search
Use this skill for web search and lightweight research through the Tavily Search API.
## Requirements
A valid Tavily API key must be available through one of these methods:
1. `--api-key`
2. `TAVILY_API_KEY`
3. `{baseDir}/.secrets/tavily.key`
If no key is available, explain that Tavily search is not configured in this environment.
## Command
Run:
```bash
python3 {baseDir}/scripts/tavily_search.py --query "<user query>"

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@@ -0,0 +1,6 @@
{
"ownerId": "kn7dnrfjd81n2c2x98wy693sbs82nfgp",
"slug": "tavily-web-search-for-openclaw",
"version": "1.0.0",
"publishedAt": 1773269610000
}

View File

@@ -0,0 +1,241 @@
#!/usr/bin/env python3
import argparse
import json
import os
import sys
import urllib.error
import urllib.request
API_URL = "https://api.tavily.com/search"
def load_api_key():
base_dir = os.path.dirname(os.path.abspath(__file__))
key_path = os.path.normpath(
os.path.join(base_dir, "..", ".secrets", "tavily.key")
)
try:
with open(key_path, "r", encoding="utf-8") as f:
raw = f.read().strip()
if "=" in raw:
left, right = raw.split("=", 1)
if left.strip() == "TAVILY_API_KEY":
return right.strip()
return raw or None
except FileNotFoundError:
return None
def clamp_max_results(value: int) -> int:
if value < 1:
return 1
if value > 10:
return 10
return value
def build_payload(args: argparse.Namespace, api_key: str) -> dict:
payload = {
"api_key": api_key,
"query": args.query,
"search_depth": args.depth,
"topic": args.topic,
"max_results": clamp_max_results(args.max_results),
"include_answer": not args.no_answer,
"include_raw_content": args.raw_content,
"include_images": args.images,
}
if args.include_domains:
payload["include_domains"] = args.include_domains
if args.exclude_domains:
payload["exclude_domains"] = args.exclude_domains
return payload
def tavily_search(payload: dict, timeout: int = 30) -> dict:
data = json.dumps(payload).encode("utf-8")
req = urllib.request.Request(
API_URL,
data=data,
headers={
"Content-Type": "application/json",
"Accept": "application/json",
},
method="POST",
)
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
body = resp.read().decode("utf-8", errors="replace")
return json.loads(body)
except urllib.error.HTTPError as exc:
details = ""
try:
details = exc.read().decode("utf-8", errors="replace")
except Exception:
details = ""
return {
"success": False,
"error": f"HTTP {exc.code}",
"details": details,
}
except urllib.error.URLError as exc:
return {
"success": False,
"error": "Network error",
"details": str(exc.reason),
}
except Exception as exc:
return {
"success": False,
"error": "Unexpected error",
"details": str(exc),
}
def print_human(result: dict) -> int:
if not isinstance(result, dict):
print("Error: invalid response format", file=sys.stderr)
return 1
if "error" in result and not result.get("results"):
print(f"Error: {result.get('error')}", file=sys.stderr)
if result.get("details"):
print(result["details"], file=sys.stderr)
return 1
print(f"Query: {result.get('query', 'N/A')}")
print(f"Response time: {result.get('response_time', 'N/A')}")
usage = result.get("usage", {})
if isinstance(usage, dict):
print(f"Credits used: {usage.get('credits', 'N/A')}")
print()
answer = result.get("answer")
if answer:
print("=== ANSWER ===")
print(answer)
print()
results = result.get("results", [])
if results:
print("=== RESULTS ===")
for index, item in enumerate(results, start=1):
title = item.get("title") or "No title"
url = item.get("url") or "N/A"
score = item.get("score")
content = item.get("content") or ""
print(f"\n{index}. {title}")
print(f" URL: {url}")
if isinstance(score, (int, float)):
print(f" Score: {score:.3f}")
if content:
snippet = content[:280].replace("\n", " ").strip()
if len(content) > 280:
snippet += "..."
print(f" {snippet}")
images = result.get("images", [])
if images:
print(f"\n=== IMAGES ({len(images)}) ===")
for image in images[:5]:
if isinstance(image, dict):
print(f" {image.get('url', 'N/A')}")
else:
print(f" {image}")
return 0
def main() -> int:
parser = argparse.ArgumentParser(
description="Tavily Search via direct REST API call",
)
parser.add_argument("--query", required=True, help="Search query")
parser.add_argument(
"--api-key",
help="Tavily API key. If omitted, file or TAVILY_API_KEY is used.",
)
parser.add_argument(
"--depth",
choices=["basic", "advanced"],
default="basic",
help="Search depth",
)
parser.add_argument(
"--topic",
choices=["general", "news"],
default="general",
help="Search topic",
)
parser.add_argument(
"--max-results",
type=int,
default=5,
help="Number of results to return (1-10)",
)
parser.add_argument(
"--no-answer",
action="store_true",
help="Do not request Tavily answer summary",
)
parser.add_argument(
"--raw-content",
action="store_true",
help="Include parsed raw content",
)
parser.add_argument(
"--images",
action="store_true",
help="Include image results",
)
parser.add_argument(
"--include-domains",
nargs="+",
help="Only include these domains",
)
parser.add_argument(
"--exclude-domains",
nargs="+",
help="Exclude these domains",
)
parser.add_argument(
"--json",
action="store_true",
help="Print raw JSON response",
)
args = parser.parse_args()
api_key = load_api_key()
if not api_key:
print(
"Error: Tavily API key not found in ../.secrets/tavily.key",
file=sys.stderr,
)
return 1
payload = build_payload(args, api_key)
result = tavily_search(payload)
if args.json:
print(json.dumps(result, indent=2, ensure_ascii=False))
return 0 if "error" not in result else 1
return print_human(result)
if __name__ == "__main__":
raise SystemExit(main())

View File

@@ -16,3 +16,4 @@
| 2026-04-21 10:10 | knowledge/research-log.md | 新建研究记录沉淀文档 | 同上 |
| 2026-04-21 12:27 | TOOLS.md/insights.md | 新增工具授权原则:为完成任务可自造工具,破坏性操作须主动报备 | Tyrone 授权 |
| 2026-04-21 12:29 | AGENTS.md/TOOLS.md/insights.md | 新增 MCP/Skills 自主更新授权,有冲突信息点需报 Tyrone 协调 | Tyrone 授权 |
| $(date +%Y-%m-%d\ %H:%M) | 技能自主更新授权 | Tyrone 授权小橙可自主从 awesome-openclaw-skills 安装技能,安装后微信通知 | 按需自主补充 |