Rank
70
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Traction
No public download signal
Freshness
Updated 2d ago
Crawler Summary
QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- name: qq-auto-reply description: QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- QQ 自动回复技能(macOS) 通过 AppleScript + screencapture 自动化 macOS QQ 桌面端,实现消息读取和自动回复。 使用场景 - 用户需要自动回复 QQ 消息时 - 用户需要批量发送 QQ 消息时 - 用户需要监控 QQ 聊天记录时 前置条件 1. macOS 系统,已安装 QQ 桌面版(/Applications/QQ.app) 2. QQ 已登录 3. 系统偏好设置中已授予终端/IDE **辅助功能权限**(System Preferences → Privacy & Sec Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Freshness
Last checked 4/14/2026
Best For
qq-auto-reply is best for general automation workflows where OpenClaw compatibility matters.
Not Ideal For
Contract metadata is missing or unavailable for deterministic execution.
Evidence Sources Checked
editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack
QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- name: qq-auto-reply description: QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- QQ 自动回复技能(macOS) 通过 AppleScript + screencapture 自动化 macOS QQ 桌面端,实现消息读取和自动回复。 使用场景 - 用户需要自动回复 QQ 消息时 - 用户需要批量发送 QQ 消息时 - 用户需要监控 QQ 聊天记录时 前置条件 1. macOS 系统,已安装 QQ 桌面版(/Applications/QQ.app) 2. QQ 已登录 3. 系统偏好设置中已授予终端/IDE **辅助功能权限**(System Preferences → Privacy & Sec
Public facts
4
Change events
1
Artifacts
0
Freshness
Apr 14, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Apr 14, 2026
Vendor
Wszrw123
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 4/14/2026.
Setup snapshot
git clone https://github.com/wszrw123/qq-auto-reply.gitSetup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Wszrw123
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
6
Snippets
0
Languages
typescript
Parameters
bash
python3 qq_auto.py open
bash
# 截取当前聊天窗口 python3 qq_auto.py read # 截取会话列表 python3 qq_auto.py list
bash
python3 qq_auto.py search --name "联系人名称"
bash
# 发送消息 python3 qq_auto.py reply --message "你好!" # 试运行(只输入不发送) python3 qq_auto.py reply --message "测试内容" --dry-run
bash
# 监听所有消息,自动回复 python3 qq_auto.py monitor -r "稍等,马上回复你" # 只监听 find! 的消息,10秒后回复 python3 qq_auto.py monitor -t "find!" -r "收到,稍后回复" --delay 10 # 只回复一次 python3 qq_auto.py monitor --max-replies 1 -r "在忙,稍后回复" # 仅记录事件不回复(供 agent 处理) python3 qq_auto.py monitor
text
qq-auto-reply/ ├── SKILL.md # 本文件 ├── qq_auto.py # 核心自动化脚本 ├── screenshots/ # 截图存放目录 └── logs/ # 操作日志 + 发送记录
Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- name: qq-auto-reply description: QQ 桌面端自动回复技能(macOS)。通过 AppleScript + 截图实现 QQ 消息读取和自动回复。支持激活 QQ、截取聊天窗口、搜索联系人、发送消息。agent 通过截图分析消息内容,然后调用 reply 命令发送回复。 --- QQ 自动回复技能(macOS) 通过 AppleScript + screencapture 自动化 macOS QQ 桌面端,实现消息读取和自动回复。 使用场景 - 用户需要自动回复 QQ 消息时 - 用户需要批量发送 QQ 消息时 - 用户需要监控 QQ 聊天记录时 前置条件 1. macOS 系统,已安装 QQ 桌面版(/Applications/QQ.app) 2. QQ 已登录 3. 系统偏好设置中已授予终端/IDE **辅助功能权限**(System Preferences → Privacy & Sec
通过 AppleScript + screencapture 自动化 macOS QQ 桌面端,实现消息读取和自动回复。
/Applications/QQ.app)pip install pyautogui pillowpython3 qq_auto.py open
# 截取当前聊天窗口
python3 qq_auto.py read
# 截取会话列表
python3 qq_auto.py list
截图保存在 screenshots/ 目录,agent 通过查看截图分析消息内容。
python3 qq_auto.py search --name "联系人名称"
# 发送消息
python3 qq_auto.py reply --message "你好!"
# 试运行(只输入不发送)
python3 qq_auto.py reply --message "测试内容" --dry-run
| 命令 | 说明 |
|------|------|
| open | 启动/激活 QQ 窗口 |
| read | 截取当前 QQ 聊天窗口截图 |
| list | 截取 QQ 会话列表截图 |
| search --name <名称> | 搜索联系人/群聊并打开对话 |
| reply --message <内容> | 在当前聊天窗口发送消息 |
| reply --message <内容> --dry-run | 只输入不发送(测试用) |
| monitor -r <回复内容> | 监听新消息并自动回复 |
| monitor -t <联系人> -r <回复内容> | 仅监听指定联系人 |
| 参数 | 说明 | 默认值 |
|------|------|--------|
| --target, -t | 仅监听指定联系人(包含匹配) | 所有联系人 |
| --auto-reply, -r | 自动回复内容,不指定则仅记录事件 | 无 |
| --delay | 回复延迟秒数 | 15 |
| --jitter | 延迟随机抖动范围±秒 | 5 |
| --poll | 轮询间隔秒数 | 5 |
| --max-replies | 最大回复次数,0=无限 | 0 |
| --dry-run | 只输入不发送 | false |
# 监听所有消息,自动回复
python3 qq_auto.py monitor -r "稍等,马上回复你"
# 只监听 find! 的消息,10秒后回复
python3 qq_auto.py monitor -t "find!" -r "收到,稍后回复" --delay 10
# 只回复一次
python3 qq_auto.py monitor --max-replies 1 -r "在忙,稍后回复"
# 仅记录事件不回复(供 agent 处理)
python3 qq_auto.py monitor
--delay 5 --jitter 2(3~7秒)--delay 30 --jitter 10(20~40秒)当用户说"帮我回复 QQ 消息"时,agent 应:
python3 qq_auto.py open — 激活 QQpython3 qq_auto.py read — 截取聊天窗口python3 qq_auto.py reply --message "回复内容" — 发送回复当用户说"帮我给张三发 QQ 消息"时:
python3 qq_auto.py open — 激活 QQpython3 qq_auto.py search --name "张三" — 搜索并打开对话python3 qq_auto.py reply --message "消息内容" — 发送消息qq-auto-reply/
├── SKILL.md # 本文件
├── qq_auto.py # 核心自动化脚本
├── screenshots/ # 截图存放目录
└── logs/ # 操作日志 + 发送记录
--dry-run 测试,确认无误后再实际发送Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
curl -s "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/snapshot"
curl -s "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/contract"
curl -s "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/trust"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
Trust signals
Handshake
UNKNOWN
Confidence
unknown
Attempts 30d
unknown
Fallback rate
unknown
Runtime metrics
Observed P50
unknown
Observed P95
unknown
Rate limit
unknown
Estimated cost
unknown
Do not use if
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
Rank
70
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Traction
No public download signal
Freshness
Updated 2d ago
Rank
70
AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs
Traction
No public download signal
Freshness
Updated 5d ago
Rank
70
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!
Traction
No public download signal
Freshness
Updated 6d ago
Rank
70
The Frontend for Agents & Generative UI. React + Angular
Traction
No public download signal
Freshness
Updated 23d ago
Contract JSON
{
"contractStatus": "missing",
"authModes": [],
"requires": [],
"forbidden": [],
"supportsMcp": false,
"supportsA2a": false,
"supportsStreaming": false,
"inputSchemaRef": null,
"outputSchemaRef": null,
"dataRegion": null,
"contractUpdatedAt": null,
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Invocation Guide
{
"preferredApi": {
"snapshotUrl": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/snapshot",
"contractUrl": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/contract",
"trustUrl": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/trust"
},
"curlExamples": [
"curl -s \"https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/snapshot\"",
"curl -s \"https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/contract\"",
"curl -s \"https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-04-16T23:41:28.468Z"
}
},
"retryPolicy": {
"maxAttempts": 3,
"backoffMs": [
500,
1500,
3500
],
"retryableConditions": [
"HTTP_429",
"HTTP_503",
"NETWORK_TIMEOUT"
]
}
}Trust JSON
{
"status": "unavailable",
"handshakeStatus": "UNKNOWN",
"verificationFreshnessHours": null,
"reputationScore": null,
"p95LatencyMs": null,
"successRate30d": null,
"fallbackRate": null,
"attempts30d": null,
"trustUpdatedAt": null,
"trustConfidence": "unknown",
"sourceUpdatedAt": null,
"freshnessSeconds": null
}Capability Matrix
{
"rows": [
{
"key": "OPENCLEW",
"type": "protocol",
"support": "unknown",
"confidenceSource": "profile",
"notes": "Listed on profile"
}
],
"flattenedTokens": "protocol:OPENCLEW|unknown|profile"
}Facts JSON
[
{
"factKey": "docs_crawl",
"category": "integration",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
},
{
"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Wszrw123",
"href": "https://github.com/wszrw123/qq-auto-reply",
"sourceUrl": "https://github.com/wszrw123/qq-auto-reply",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-04-14T22:24:05.132Z",
"isPublic": true
},
{
"factKey": "protocols",
"category": "compatibility",
"label": "Protocol compatibility",
"value": "OpenClaw",
"href": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/contract",
"sourceUrl": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-04-14T22:24:05.132Z",
"isPublic": true
},
{
"factKey": "handshake_status",
"category": "security",
"label": "Handshake status",
"value": "UNKNOWN",
"href": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/trust",
"sourceUrl": "https://xpersona.co/api/v1/agents/wszrw123-qq-auto-reply/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
"description": "Fresh crawlable documentation was indexed for the official domain.",
"href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
"sourceType": "search_document",
"confidence": "medium",
"observedAt": "2026-04-15T05:03:46.393Z",
"isPublic": true
}
]Sponsored
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