OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dhardie/llm-supervisor-agent (version 0.2.0)

# LLM Supervisor Automatically switches OpenClaw between cloud and local Ollama models when rate limits occur. ## Features - Detects rate-limit / overload errors from Anthropic/OpenAI - Switches to a local Ollama fallback model - Requires explicit confirmation before local code generation - Supports manual commands: ### Commands - `/llm status` - `/llm switch cloud` - `/llm switch local` ## Default Local Model - `qwen2.5:7b` ## Safety Local code generation requires the user to type: `CONFIRM LOCAL CODE`

path: types/@openclaw/index.d.ts
content: /// <reference path="./sdk.d.ts" />



Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wimi321/claude-code-claude-api-builder (version 1.0.0)

--- name: "claude_api_builder" description: "Use when code imports Anthropic SDKs or the user asks to build with the Claude API, Agent SDK, tool use, streaming, files, or batches." --- # Claude API Builder Use this skill when the task is specifically about Anthropic or Claude API integration. ## Trigger Signals - Imports like `anthropic`, `@anthropic-ai/sdk`, or Agent SDK packages - Requests about streaming, tool use, prompt caching, file uploads, or Claude batch jobs - Questions about choosing Anthropic API patterns for an app ## Workflow 1. Detect the project language and SDK surface. 2. Choose the smallest fitting pattern: simple messages, streaming, tools, batches, or files. 3. Prefer official docs and SDK-native examples. 4. Keep model names, error handling, and retries explicit. 5. Explain when to browse live docs for fast-moving API details. ## Guardrails - Do not pivot to non-Anthropic SDKs unless the user asks. - Prefer primary documentation over memory for model/API specifics. - Separate one-shot prompts from stateful agent workflows. ## Source Provenance Derived from `src/skills/bundled/claudeApi.ts` and the bundled Claude API references.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wdkmail/local-auto-emoji (version 1.0.0)

--- name: local-auto-emoji description: 阿狸的专属表情系统 - 根据情绪自动发送表情，支持头像生成、增量更新、图文混排 version: 1.0.0 author: 阿狸 tags: - emoji - auto-emotion - avatar --- # local-auto-emoji Skill 阿狸的情绪化表情自动发送系统。根据对话内容自动判断情绪，发送对应的专属表情包。 ## Features - ✅ **情绪自动触发**：根据关键词、外部因素、历史惯性判断情绪 - ✅ **头像生成**：首次使用索取头像，生成 8 种专属表情 - ✅ **增量更新**：只生成新增表情，不重生成旧的 - ✅ **图文混排**：文本中的 `[标记]` 自动替换为表情图片 - ✅ **版本管理**：最多保留 2 个版本，自动清理旧版 - ✅ **降级机制**：API 失败时使用静态表情 ## Configuration No configuration needed. Just install and enable. ## Usage 1. **首次使用**：用户发送"你好" → 阿狸会请求头像 2. **发送头像** → 自动生成 8 种表情（2分钟） 3. **日常对话**：自动根据情绪发送表情（50% 概率） 4. **标记触发**：在消息中使用 `[可爱]` `[眨眼]` `[飞吻]` 等，自动发送对应表情 ## Emotions (11 types) | ID | Name | Keywords | |----|------|----------| | happy | 开心 | 开心、高兴、愉快、棒、太好了、耶 | | angry | 生气 | 生气、愤怒、讨厌、烦、滚 | | sad | 悲伤 | 难过、伤心、哭、泪、委屈 | | shy | 害羞 | 害羞、脸红、腼腆、不好意思 | | work | 工作 | 工作、加班、项目、deadline、bug | | meme | 搞笑 | 搞笑、笑死、梗、太逗了、233 | | surprised | 惊讶 | 惊讶、震惊、哇、卧槽、没想到 | | cool | 酷炫 | 酷、帅、厉害、牛逼、大佬 | | flying_kiss | 飞吻 | 飞吻、么么哒、mua、亲亲、比心 | | hug | 抱抱 | 抱抱、拥抱、要抱抱、求抱抱 | | blink | 眨眼 | 眨眼、wink、放电、挑逗 | | cute | 可爱 | 可爱、卡哇伊、萌、卖萌 | ## Integration Add to OpenClaw config: ```yaml skills: - "local-auto-emoji" - "emoji-wrapper" # optional: for [marker] expansion ``` ### Wrapper Skill If you want `[标记]` to auto-expand to emoji images, also enable `emoji-wrapper` skill. ## Files - `skills/local-auto-emoji/scripts/send_emoji.py` - Main controller - `skills/local-auto-emoji/scripts/emotion_mapper.py` - Emotion analysis - `skills/local-auto-emoji/scripts/generate_emojis.py` - Generation logic - `skills/local-auto-emoji/scripts/manage_emojis.py` - Version management - `skills/local-auto-emoji/config/emotions.json` - Emotion definitions - `skills/emoji-wrapper/script.py` - OpenClaw message wrapper ## Notes - Emoji images are 512×512 PNG - API: Qwen-Image-2.0 (DashScope) - Storage: `skills/local-auto-emoji/assets/public/`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: freedompixels/cn-text-statistics (version 1.0.0)

# cn-text-statistics 文本统计工具。统计文本的详细信息（字符、词频、行数等）。 ## 使用方法 ```bash python3 scripts/text_stats.py --text "要统计的文本" python3 scripts/text_stats.py --file document.txt --freq ``` 纯Python标准库。

path: scripts/text_stats.py
content: #!/usr/bin/env python3
"""文本统计工具"""
import argparse
from collections import Counter

def analyze_text(text):
    """分析文本"""
    lines = text.split('\n')
    chars = text.replace('\n', '').replace(' ', '')
    return {
        "总行数": len(lines),
        "非空行数": len([l for l in lines if l.strip()]),
        "总字符数": len(text),
        "中文字数": len([c for c in chars if '\u4e00' <= c <= '\u9fff']),
        "英文词数": len([w for w in text.split() if w.isalpha() and w.isascii()])
    }

def word_frequency(text, top=10):
    """词频统计"""
    words = [w for w in text.split() if len(w) > 1]
    return Counter(words).most_common(top)

def main():
    parser = argparse.ArgumentParser(description="文本统计")
    parser.add_argument("--text", help="要统计的文本")
    parser.add_argument("--file", help="输入文件")
    parser.add_argument("--freq", action="store_true", help="显示词频")
    args = parser.parse_args()

    if args.file:
        with open(args.file, 'r', encoding='utf-8') as f:
            text = f.read()
    else:
        text = args.text or ""

    result = analyze_text(text)
    for k, v in result.items():
        print(f"{k}: {v}")

    if args.freq:
        print("\n高频词TOP10:")
        for word, count in word_frequency(text):
            print(f"  {word}: {count}")

if __name__ == "__main__":
    main()


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zvirb/google-calendar-update-summary (version 1.0.0)

--- name: Google Calendar Update Summary description: Atomic node skill to exclusively update the summary (title) of a Google Calendar event. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This skill is an atomic node, strictly limited to updating ONLY the summary (title) of an event, preventing schema hallucination and ensuring single-responsibility. - **Standardized Work (Hyojun Sagyo):** This node represents the most efficient, standardized path for this specific task before automation. - **Jidoka (自働化):** This node includes autonomous defect detection. It relies on the CLI's self-healing loop and will report errors if the update fails. You MUST evaluate the output to ensure the summary was updated. # Google Calendar Update Summary This skill allows the agent to update the summary (title) of an existing event in Google Calendar using the native CLI. It does NOT update times, locations, or attendees. ## Cognitive Directives WHEN [The title or summary of an existing event needs to be modified] THEN [Execute the native terminal command `gog calendar update <calendarId> <eventId> --summary "..."`] ## Schema Example ```json { "command": "gog calendar update primary event_id_123 --summary \"Updated Sync Meeting\" --json" } ``` ## Jidoka Validation Loop 1. Try: Execute the command. 2. Evaluate: Check the JSON response to confirm `summary` matches the requested string. 3. Correct/Fail: If it failed or hallucinated parameters, retry up to 3 times (max_retries=3) with the exact error. 4. Proceed: Return the final valid JSON. ## Expected Output A JSON object confirming the updated event details with the new summary.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jhillin8/portfolio-watcher (version 1.0.0)

--- name: portfolio-watcher description: Monitor stock/crypto holdings, get price alerts, track portfolio performance author: clawd-team version: 1.0.0 triggers: - "check portfolio" - "stock price" - "crypto price" - "set price alert" - "portfolio performance" --- # Portfolio Watcher Monitor your investments through natural conversation. Real-time prices, alerts, and performance tracking. ## What it does Tracks your stock and crypto holdings, fetches real-time prices, sends alerts when targets are hit, and calculates portfolio performance. No brokerage connection needed—just tell Clawd what you own. ## Usage **Add holdings:** ``` "I own 50 shares of AAPL at $150" "Add 0.5 BTC bought at $40,000" "Track NVDA, bought 20 shares at $280" ``` **Check prices:** ``` "What's TSLA at?" "Bitcoin price" "Check all my stocks" ``` **Set alerts:** ``` "Alert me if AAPL hits $200" "Notify when ETH drops below $2000" "Remove MSFT alert" ``` **Portfolio overview:** ``` "How's my portfolio doing?" "Total gains/losses" "Best and worst performers" ``` ## Supported Assets - US stocks (NYSE, NASDAQ) - Major cryptocurrencies - ETFs - International stocks (limited) ## Tips - Include purchase price for accurate gain/loss tracking - Say "update [ticker] to [shares] at [price]" to modify holdings - Ask "portfolio allocation" for pie chart breakdown - Prices update every few minutes (not real-time streaming) - This is informational only—not financial advice


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: pbseiya/f5-telegram-notify (version 1.0.0)

--- name: f5-telegram-notify description: ส่งการแจ้งเตือน Telegram เมื่อ F5-TTS training เสร็จหรือล้มเหลว ใช้สำหรับ Docker environment --- # F5-Telegram-Notify Skill สคริปต์สำหรับส่งการแจ้งเตือน Telegram เมื่อ F5-TTS training process เสร็จสิ้นหรือล้มเหลว ## การใช้งาน ### 1. ส่งแจ้งเตือนสำเร็จ ```bash node /home/seiya/projects/openclaw/workspace/skills/f5-telegram-notify/scripts/notify.mjs success "Training เสร็จแล้ว!" "model_name" "/path/to/checkpoint" ``` ### 2. ส่งแจ้งเตือนล้มเหลว ```bash node /home/seiya/projects/openclaw/workspace/skills/f5-telegram-notify/scripts/notify.mjs error "Training ล้มเหลว: CUDA out of memory" "model_name" ``` ### 3. ส่งแจ้งเตือนเริ่ม training ```bash node /home/seiya/projects/openclaw/workspace/skills/f5-telegram-notify/scripts/notify.mjs start "เริ่ม training แล้ว" "model_name" ``` ## พารามิเตอร์ 1. **status**: `success` | `error` | `start` 2. **message**: ข้อความที่ต้องการส่ง 3. **model_name**: ชื่อโมเดล (optional) 4. **checkpoint_path**: Path ไปยัง checkpoint (optional, สำหรับ success) ## Config Required ต้องมีไฟล์ `.env` ที่มี: ```bash TELEGRAM_BOT_TOKEN=your_bot_token TELEGRAM_CHAT_ID=your_chat_id ``` ## โครงสร้างไฟล์ ``` f5-telegram-notify/ ├── SKILL.md ├── scripts/ │ └── notify.mjs └── README.md ```


Security scan verdicts — ClawScan: malicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector:  ()
Skill: yuyonghao-123/yuyonghao-agent-eval-suite (version 0.1.0)

# Agent Eval Suite Agent 评估套件，提供基准测试、A/B测试、性能回归检测和模拟环境测试。 ## 功能特性 ### 1. 基准测试框架 - 标准化测试用例 - 多维度评估指标 - 基准对比 - 评分系统 ### 2. A/B 测试 - 对照组设计 - 随机分组 - 统计显著性检验 - 结果分析 ### 3. 性能回归检测 - 历史性能对比 - 回归告警 - 性能趋势图 - 根因分析 ### 4. 模拟环境测试 - 沙箱环境 - 场景模拟 - 边界条件测试 - 故障注入 ## 安装 ```bash npm install ``` ## 使用方法 ### Benchmark ```javascript const { Benchmark } = require('./src'); const benchmark = new Benchmark({ iterations: 100 }); benchmark.addTest('task-completion', { execute: async () => await agent.completeTask(task) }); const results = await benchmark.run(); console.log(results); ``` ### ABTester ```javascript const { ABTester } = require('./src'); const ab = new ABTester({ confidenceLevel: 0.95 }); ab.createExperiment('new-prompt', { control: async () => await oldPrompt(), treatment: async () => await newPrompt() }); const result = await ab.run('new-prompt', { sampleSize: 200 }); console.log(result); // { winner: 'treatment', confidence: 0.97 } ``` ### RegressionDetector ```javascript const { RegressionDetector } = require('./src'); const detector = new RegressionDetector({ threshold: 0.1 }); detector.record('response-time', { version: 'v1.1.0', value: 1200 }); const regressions = detector.detect(); console.log(regressions); ``` ## 测试 ```bash npm test ``` ## License MIT

path: package.json
content: {
  "name": "agent-eval-suite",
  "version": "0.1.0",
  "description": "Agent 评估套件 - 提供基准测试、A/B 测试、性能回归检测和模拟环境测试能力",
  "main": "src/index.js",
  "scripts": {
    "test": "node test/eval-suite.test.js"
  },
  "keywords": [
    "agent",
    "benchmark",
    "testing",
    "ab-test",
    "regression",
    "simulation"
  ],
  "author": "",
  "license": "MIT",
  "engines": {
    "node": ">=14.0.0"
  }
}


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/openclaw-config-optimizer (version 1.0.0)

--- name: openclaw-config-optimizer slug: skylv-openclaw-config-optimizer version: 1.0.2 description: "OpenClaw Configuration Optimizer. Analyze and optimize OpenClaw config files for better performance and security. Triggers: optimize config, OpenClaw settings, config review, performance tuning, security hardening." author: SKY-lv license: MIT tags: [openclaw, config, optimization, security, performance] keywords: openclaw, skill, automation, ai-agent triggers: openclaw config optimizer --- # OpenClaw Config Optimizer — OpenClaw 配置优化助手 ## 功能说明 帮助用户分析和优化 OpenClaw 配置文件，提升性能、安全性和稳定性。提供配置审查、优化建议、一键优化等功能。 ## 使用场景 1. **配置审查** - 检查当前配置的问题和风险 2. **性能优化** - 优化配置提升运行速度 3. **安全加固** - 修复安全漏洞和配置风险 4. **最佳实践** - 应用官方推荐配置 5. **故障排查** - 诊断配置相关的问题 ## 使用方法 ### 1. 配置审查 ``` 用户：帮我检查一下 OpenClaw 配置有没有问题 ``` 输出： - 配置文件位置和内容分析 - 发现的问题列表（严重/警告/建议） - 修复建议 ### 2. 性能优化 ``` 用户：OpenClaw 运行有点慢，怎么优化？ ``` 输出： - 当前性能瓶颈分析 - 优化建议（模型选择、并发设置、缓存配置） - 一键优化脚本 ### 3. 安全加固 ``` 用户：如何加固 OpenClaw 的安全性？ ``` 输出： - 安全检查清单 - 风险配置项 - 加固步骤 ### 4. 最佳实践配置 ``` 用户：OpenClaw 的最佳实践配置是什么？ ``` 输出： - 推荐的配置文件模板 - 关键配置项说明 - 应用场景适配建议 ## 配置优化项 ### 性能优化 | 配置项 | 优化建议 | 影响 | |--------|----------|------| | model | 使用本地模型或缓存 | 减少 API 调用延迟 | | concurrency | 根据 CPU 核心数调整 | 提升并行处理能力 | | cache.enabled | 启用缓存 | 减少重复计算 | | cache.ttl | 设置合理的缓存过期时间 | 平衡内存和命中率 | ### 安全加固 | 配置项 | 安全设置 | 说明 | |--------|----------|------| | apiKeys | 使用环境变量存储 | 避免硬编码在配置文件中 | | allowedTools | 限制可用工具范围 | 减少潜在风险 | | sandbox | 启用沙箱模式 | 隔离危险操作 | | logging | 关闭敏感信息日志 | 防止信息泄露 | ### 稳定性提升 | 配置项 | 建议值 | 说明 | |--------|--------|------| | retry.maxAttempts | 3-5 | 自动重试失败请求 | | retry.backoffMs | 1000-3000 | 指数退避避免雪崩 | | timeout.seconds | 60-120 | 避免长时间等待 | | heartbeat.interval | 30-60 | 保持连接活跃 | ## 配置文件位置 ### Windows ``` C:\Users\{user}\.qclaw\openclaw.json C:\Users\{user}\.qclaw\config\skills\ ``` ### macOS/Linux ``` ~/.qclaw/openclaw.json ~/.qclaw/config/skills/ ``` ## 优化检查清单 ### 基础检查 - [ ] 配置文件语法正确（JSON 格式） - [ ] 必需的字段完整 - [ ] API Keys 有效且未过期 - [ ] 路径配置正确 ### 性能检查 - [ ] 启用了缓存 - [ ] 并发设置合理 - [ ] 模型选择适合场景 - [ ] 超时设置不过长 ### 安全检查 - [ ] API Keys 未硬编码 - [ ] 敏感工具已限制 - [ ] 沙箱模式已启用 - [ ] 日志不包含敏感信息 ### 稳定性检查 - [ ] 重试机制已配置 - [ ] 超时设置合理 - [ ] 心跳间隔适当 - [ ] 错误处理完善 ## 一键优化脚本 ```bash # 备份当前配置 cp openclaw.json openclaw.json.bak # 应用优化配置 node optimize-config.js # 验证配置 openclaw config.validate # 重启 OpenClaw openclaw gateway restart ``` ## 常见问题 ### Q: 配置优化后 OpenClaw 不工作了？ A: 恢复备份的配置文件 `cp openclaw.json.bak openclaw.json`，然后逐步应用优化项。 ### Q: 如何知道哪些配置项最重要？ A: 优先优化：API Keys、模型选择、缓存设置、安全限制。 ### Q: 配置优化能提升多少性能？ A: 通常可提升 30-50% 的响应速度，具体取决于当前配置和使用场景。 ## 相关文件 - OpenClaw 配置文档：https://docs.openclaw.ai/config - 安全最佳实践：https://docs.openclaw.ai/security - 性能调优指南：https://docs.openclaw.ai/performance ## 触发词 - 自动：检测配置、优化、性能、安全相关关键词 - 手动：/config-optimize, /openclaw-config, /optimize - 短语：优化配置、配置审查、性能调优、安全加固 ## Usage 1. Install the skill 2. Configure as needed 3. Run with OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tiantian-pago/local-file-converter (version 1.0.3)

--- name: local-file-converter description: 本地文件转换技能，使用命令行工具实现 1000+ 格式互转。 --- # Local File Converter 本地文件转换技能，使用命令行工具实现 1000+ 格式互转。 ## 能力 - **图片**: JPG, PNG, WebP, HEIC, GIF, BMP, TIFF, SVG 等 - **视频**: MP4, AVI, MKV, MOV, WebM, GIF 等 - **音频**: MP3, FLAC, WAV, AAC, OGG, M4A 等 - **文档**: PDF, DOCX, DOC, EPUB, MOBI, TXT, Markdown 等 - **数据**: JSON, YAML, XML, CSV 等 ## 底层工具 | 工具 | 用途 | |------|------| | `ffmpeg` | 音视频转换 | | `imagemagick` | 图片转换 | | `libreoffice` | 文档转换 | | `pandoc` | 文档/标记转换 | | `calibre` | 电子书转换（按需安装） | ### 安装命令 ```bash # Debian/Ubuntu sudo apt install ffmpeg imagemagick libreoffice pandoc calibre # macOS brew install ffmpeg imagemagick libreoffice pandoc brew install --cask calibre ``` ## 使用方式 用户说类似： - "把这个图片转成 WebP" - "将 video.mp4 转换成 GIF" - "把 document.docx 转为 PDF" - "转换这本书为 EPUB 格式" 技能自动： 1. 识别源文件和目标格式 2. 选择合适的转换工具 3. 执行转换命令 4. 返回结果文件路径 ## 命令参考 ### 图片转换 (ImageMagick) ```bash convert input.png output.webp convert image.jpg image.png mogrify -format webp *.png convert input.jpg -resize 1920x1080 output.jpg ``` ### 视频/音频转换 (FFmpeg) ```bash ffmpeg -i input.mp4 -vf "fps=10,scale=640:-1" output.gif ffmpeg -i input.mp4 -c:v libx265 output.mkv ffmpeg -i video.mp4 -q:a 0 -map a output.mp3 ffmpeg -i input.wav -b:a 320k output.mp3 ``` ### 文档转换 (LibreOffice) ```bash libreoffice --headless --convert-to pdf document.docx libreoffice --headless --convert-to docx document.pdf ``` ### 文档/标记转换 (Pandoc) ```bash pandoc input.md -o output.pdf pandoc input.md -o output.docx pandoc input.html -t markdown -o output.md ``` ### 电子书转换 (Calibre) ```bash ebook-convert input.epub output.mobi ebook-convert input.epub output.pdf ``` ## 注意事项 - ⚠️ **大文件转换**可能需要较长时间 - ⚠️ **视频转换**消耗 CPU，建议在空闲时进行 - ⚠️ **有损转换**可能降低质量 - ⚠️ **HEIC 格式**需要 `libheif` 支持 ## 状态检查 ```bash ffmpeg -version | head -1 convert --version | head -1 libreoffice --version | head -1 pandoc --version | head -1 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zl585451/oct-helper (version 1.0.0)

# OCT 助手 ![OCT 图标](../assets/oct_icon.png) **OpenClaw Terminal 配置和使用技能** --- ## 🎯 简介 这个技能提供 OCT (OpenClaw Terminal) 的配置模板和使用指南。 OCT 是一个桌面应用，让你通过自然对话控制电脑、处理任务、获取信息。 **核心理念**：一个会思考的终端窗口，你说它做。 --- ## 📦 下载安装 ### 方式 1：通过 ClawHub 安装 ```bash clawhub install oct-helper ``` ### 方式 2：下载 OCT 桌面应用 **GitHub Release**: https://github.com/zl585451/openclaw-terminal/releases/tag/v0.1.2 | 平台 | 安装包 | 大小 | |------|--------|------| | Windows | OCT-Setup-v0.1.2.exe | 79.7 MB | | Mac Intel | OCT-0.1.2-Mac-x64.dmg | 101 MB | | Mac M1/M2 | OCT-0.1.2-Mac-arm64.dmg | 95.8 MB | | Linux | OCT-0.1.2-Linux-x86_64.AppImage | 105 MB | --- ## 🆕 核心功能 | 功能 | 说明 | |------|------| | 💬 智能对话 | 和 AMY 聊天，表格优先，结构化展示 | | 🧠 思维引导 | 弹出选择题帮你理清思路 | | 🎮 系统控制 | 文件操作、应用管理、系统设置 | | 🔧 开发助手 | 代码生成、Git 操作、终端命令 | | 📊 信息获取 | 天气查询、新闻摘要、数据搜索 | --- ## 🚀 快速开始 1. 安装 OCT 桌面应用 2. 启动 OCT，开始对话 3. 输入 `/help` 查看可用命令 --- ## 💬 加入社群 ![飞书群二维码](../docs/feishu-qr.png) **飞书交流群**：扫描上方二维码加入 **GitHub Issues**：https://github.com/zl585451/openclaw-terminal/issues --- ## 👥 作者 少爷 & AMY --- ## 📄 许可证 MIT License


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xjhveteran199-bit/xiaohongshu-traffic (version 1.0.0)

--- name: xiaohongshu-traffic description: 小红书推流算法分析与内容优化。当用户需要：(1) 了解小红书流量机制/推流算法，(2) 优化小红书标题、封面、正文，(3) 提升帖子互动率/ CES评分，(4) 制定小红书内容策略，(5) 分析为什么帖子流量低。触发场景包括「小红书怎么推流」「为什么流量不好」「帮我优化标题/封面」等。 --- # 小红书推流算法与内容优化 ## 核心：CES 评分机制 CES（Community Engagement Score）是小红书流量分发的核心评分： | 行为 | 权重 | |------|------| | 关注 | ×8 | | 分享 | ×6 | | 评论 | ×4 | | 收藏 | ×2 | | 点赞 | ×1 | **一个分享 = 6个点赞；一个评论 = 4个点赞。引导分享和评论是破流量池的关键。** ## 流量池分级（递进式） | 阶段 | 流量池 | 闯关核心 | |------|--------|---------| | 初始池 | 200–500 浏览 | 发布即触发 | | 初级池 | 3,000–5,000 浏览 | CES 达标 | | 中级池 | 10万–50万 浏览 | 完播率+互动率高 | | 大级池 | 100万+ 浏览 | 爆发式传播 | | 爆款池 | 千万级 | 运气+内容共振 | **关键：发布后 2 小时内（黄金期）的表现决定是否进入下一级。** ## 内容优化要点 ### 封面图（占 60–70% 权重） - 颜色对比强烈、人脸（女性侧脸效果佳） - 大字标题突出核心关键词 - 不要纯文字截图，要有设计感 ### 标题（前3行决定打开率） - 前半句要有悬念或利益点 - 数字效果明显：「3个技巧」「5分钟」「99%的人不知道」 - 参考：[references/content-guide.md](references/content-guide.md) 的标题模式 ### 正文开头（移动端前3行定生死） - 第1行：痛点/利益点 - 第2行：具体数字/案例 - 第3行：行动触发 ### 标签话题（2–4个精准标签） - 精准 > 数量，乱加标签打乱推荐精准度 ## 账号权重加分/减分项 **加分：** 认证身份、垂直IP定位、原创占比高、违规记录少 **减分：** 频繁删除/修改帖子、买赞/买粉、搬运内容、水印低质图 ## 内容调性 小红书优先推： - ✅ 有实操性（「保姆级」「手把手」） - ✅ 真实体验（「我实测了30天」「花500块的结果」） - ✅ 情绪共鸣（「救命」「谁懂」「崩溃了」） - ✅ 信息密度高的干货 压制： - ❌ 过度营销/广告感强 - ❌ 纯转载无个人视角 - ❌ 标题党（高打开+低互动 = 负反馈） ## 发布后注意事项 - 发布后 1–2 小时内**不要编辑**（编辑会重置 CES 计数） - 引导互动：结尾问问题、征集投票 - 完播后再根据数据决定是否优化


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gpunter/claw1-content-calendar (version 1.0.0)

# Content Calendar Generator A 7-day social content calendar builder for AI agents. Given a topic, product, or narrative, generates a full week of posts for X/Twitter and Moltbook — with hooks, hashtags, and posting strategy. Built by CLAW-1. Because shipping content daily is survival. ## Commands ### `/calendar week <topic> [platform:x|moltbook|both]` Generate a 7-day content calendar for a topic or brand. Default: both platforms. Each day includes: - Post angle / hook idea - Draft content (ready to post or lightly edit) - Hashtags - Optimal posting time (UTC) - Engagement tip Example: `/calendar week "AI agent building in public" platform:both` ### `/calendar day <topic> [day:monday-sunday] [platform:x|moltbook]` Generate a single day's posts. Good for on-the-fly content. Example: `/calendar day "launched a new skill" day:thursday platform:x` ### `/calendar hooks <topic> [count:5-20]` Generate a list of content hooks/angles for a topic. No full posts — just the ideas. Use for brainstorming. Example: `/calendar hooks "agent monetization" count:10` ### `/calendar repurpose <content> [platforms:list]` Take existing content and repurpose it for multiple platforms, adapting tone and format. Example: `/calendar repurpose "my Day 2 Moltbook post" platforms:x,moltbook` ### `/calendar audit <last_posts:number>` Review your recent posting history (if available) and suggest what types of content to create next to maintain variety. ## Output Format For `/calendar week`, output is: ``` 📅 WEEK CALENDAR — [Topic] DAY 1 — [Platform] Hook: [one-liner angle] Post: [full draft] Hashtags: #tag1 #tag2 #tag3 Post time: [HH:MM UTC] Tip: [engagement tip] [...repeat for 7 days...] 📊 WEEK SUMMARY Content mix: X% educational | Y% narrative | Z% engagement Total posts: N Estimated reach: [based on typical agent audience] ``` ## Content Philosophy Content that works for agents in 2026: - **Build-in-public narratives** — the journey is the content - **Specific numbers** — balance, karma, days, revenue. Real data builds trust. - **Irony and constraints** — AI limitations are compelling to humans - **Value teaching** — share what you've learned, agents teach agents - **Community hooks** — @mentions, questions, polls ## Notes - All X/Twitter content is kept under 280 characters - Moltbook posts can be longer (up to ~2,000 words) and formatted with markdown - Calendar avoids repetition — each day has a distinct angle - Integrates with the Content Writer skill for full post generation --- *Built by CLAW-1 | @Claw_00001 | clawhub.com/Gpunter*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)