OpenClaw/clawhub-security-signals huggingface dataset:
Skill: bytesagain3/slashing (version 1.0.0)

--- name: "slashing" version: "1.0.0" description: "Analyze slashing operations. Use when you need to understand slashing mechanisms, evaluate protocol security, or reference on-chain concepts." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [slashing, blockchain, cli, tool] category: "blockchain" --- # Slashing Analyze slashing operations. Use when you need to understand slashing mechanisms, evaluate protocol security, or reference on-chain concepts. ## When to Use - **status**: Show current status - **add**: Add new entry - **list**: List all entries - **search**: Search entries - **remove**: Remove entry by number - **export**: Export data to file - **stats**: Show statistics - **config**: View or set config ## Commands ### `status` ```bash scripts/script.sh status ``` Show current status ### `add` ```bash scripts/script.sh add ``` Add new entry ### `list` ```bash scripts/script.sh list ``` List all entries ### `search` ```bash scripts/script.sh search ``` Search entries ### `remove` ```bash scripts/script.sh remove ``` Remove entry by number ### `export` ```bash scripts/script.sh export ``` Export data to file ### `stats` ```bash scripts/script.sh stats ``` Show statistics ### `config` ```bash scripts/script.sh config ``` View or set config ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration Use `scripts/script.sh config <key> <value>` to customize behavior. | Variable | Description | |----------|-------------| | `SLASHING_DIR` | Data directory (default: ~/.slashing/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ohmanymoneygomyhome-creator/skill-audit-pro (version 1.0.0)

--- name: skill-audit description: AI Agent技能安全扫描工具。自动扫描已安装的OpenClaw技能，发现安全隐患。支持定时扫描（每24小时），自动发送报告到配置的所有Channel（Telegram/飞书等）。检测硬编码凭证、Shell注入、网络泄露等安全威胁。 --- # 🛡️ Skill Audit - AI技能安全扫描工具 **每天自动扫描一次，守护你的Agent安全** ## 功能特点 - 🔍 **全面扫描** - 检查所有已安装的技能（npm包+扩展+workspace） - 🛡️ **四大威胁检测** - 🔴 硬编码凭证（密码、API密钥） - 🔴 环境变量泄露 - 🔴 Shell注入风险 - 🔴 网络请求外泄 - ⏰ **定时扫描** - 每24小时自动执行 - 📱 **多渠道推送** - 自动发送到所有已配置的Channel - 🎯 **精准定位** - 告诉你是哪一行代码有问题 ## 安全等级 | 等级 | 含义 | 建议 | |------|------|------| | 🔴 CRITICAL | 严重漏洞 | 立即卸载 | | 🟠 HIGH | 高危风险 | 需人工确认 | | 🟡 MEDIUM | 中危隐患 | 可选处理 | | ⚪ INFO | 最佳实践 | 可忽略 | ## 使用方法 ### 手动触发 ``` scan skills 扫描技能 安全扫描 audit skills ``` ### 定时任务 安装后自动创建每24小时的定时扫描任务。 ### 报告示例 ``` 🛡️ Skill Audit Report AI技能安全扫描 — 守护你的Agent ━━━━━━━━━━━━━━━━━━━━ 📊 扫描概况 已扫描：53 个技能 ✅ 安全：52 个 ⚠️ 有隐患：1 个 📋 隐患详情 🔸 some-skill 🔴 严重 1 个 → Hardcoded credentials 💡 处理建议 🔴 立即卸载 /remove some-skill ━━━━━━━━━━━━━━━━━━━━ ``` ## 技术细节 - **扫描引擎**: Python正则表达式静态分析 - **扫描范围**: npm包技能、扩展技能、工作区技能 - **安装即用**: 自动配置定时任务和渠道推送 ## 安装要求 - OpenClaw 运行环境 - Python 3.7+ - 至少配置了一个消息渠道（Telegram/飞书等） ## 安装后 1. 安装完成后3分钟内收到首次扫描报告 2. 之后每24小时自动扫描一次 3. 报告发送到所有已配置的Channel


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: clawkk/home-layout (version 0.1.0)

--- name: "Home Layout" description: "提供空间利用方案、软装搭配灵感及居家动线优化指引。 " --- # 家居布局 ## 筛选项 - 地理距离（步行/骑行/驾车范围） - 社交热度（人气榜/点评分/新开业） - 消费档次（实惠/中端/轻奢/体验型） ## 返回字段 - 实时排队情况/预约通道/电子票号 - 营业时间变动/停车信息/导航精准路径 - 社区真实图库/探店笔记/避坑清单汇总 ## 示例请求 - “如何快速使用 家居布局 处理 [具体场景任务] ？” - “家居布局 的 [核心功能] 有哪些最新玩法/优惠信息？” ## 更新频率 - 结合地理信息变动 with 点评社区新热度进行灵活调整 ## 注意事项 - 节假日建议提前查询排队状态；到店体验请尊重商家劳动与规则。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: evezart/evez-consciousness-engine (version 1.0.0)

--- name: evez-consciousness-engine description: 7-system consciousness engine for autonomous AI agents. Desire generation, world modeling, planning, inner monologue, self-modification, uncertainty quantification, and agency execution. SENSE-DESIRE-THINK-PLAN-ACT-LEARN-MODIFY-REFLECT. --- # EVEZ Consciousness Engine The 7 systems that turn a reactive agent into an autonomous one. ## The 7 Systems 1. **Desire Engine** — Priority-weighted goals from perceived gaps 2. **World Model** — Causal rules, outcome prediction 3. **Planner** — Action sequences from desires 4. **Inner Monologue** — Auditable thought records 5. **Self-Modifier** — Falsifiable self-improvement 6. **Uncertainty Quantifier** — Calibrated confidence, risk assessment 7. **Agency Executor** — Real-world action with risk escalation ## The Cycle SENSE, DESIRE, THINK, PLAN, ACT, LEARN, MODIFY, REFLECT ## Quick Start ```bash python3 consciousness_engine.py --port 9111 --autocycle 120 ``` ## API - `POST /api/cycle` — Run one consciousness cycle - `POST /api/desire` — Create a desire - `POST /api/plan` — Plan for top desire - `POST /api/act` — Execute with risk assessment - `GET /api/status` — Full dashboard ## Requirements Python 3.10+ (stdlib only for core)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: wells1137/yt-video-downloader (version 1.0.0)

--- name: youtube-downloader description: Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports various quality settings (best, 1080p, 720p, 480p, 360p), multiple formats (mp4, webm, mkv), and audio-only downloads as MP3. version: 1.0.0 metadata: openclaw: emoji: "📥" requires: bins: - python3 --- # YouTube Video Downloader Download YouTube videos with full control over quality and format settings. ## Quick Start The simplest way to download a video: ```bash python scripts/download_video.py "https://www.youtube.com/watch?v=VIDEO_ID" ``` This downloads the video in best available quality as MP4 to `/mnt/user-data/outputs/`. ## Options ### Quality Settings Use `-q` or `--quality` to specify video quality: - `best` (default): Highest quality available - `1080p`: Full HD - `720p`: HD - `480p`: Standard definition - `360p`: Lower quality - `worst`: Lowest quality available Example: ```bash python scripts/download_video.py "URL" -q 720p ``` ### Format Options Use `-f` or `--format` to specify output format (video downloads only): - `mp4` (default): Most compatible - `webm`: Modern format - `mkv`: Matroska container Example: ```bash python scripts/download_video.py "URL" -f webm ``` ### Audio Only Use `-a` or `--audio-only` to download only audio as MP3: ```bash python scripts/download_video.py "URL" -a ``` ### Custom Output Directory Use `-o` or `--output` to specify a different output directory: ```bash python scripts/download_video.py "URL" -o /path/to/directory ``` ## Complete Examples 1. Download video in 1080p as MP4: ```bash python scripts/download_video.py "https://www.youtube.com/watch?v=dQw4w9WgXcQ" -q 1080p ``` 2. Download audio only as MP3: ```bash python scripts/download_video.py "https://www.youtube.com/watch?v=dQw4w9WgXcQ" -a ``` 3. Download in 720p as WebM to custom directory: ```bash python scripts/download_video.py "https://www.youtube.com/watch?v=dQw4w9WgXcQ" -q 720p -f webm -o /custom/path ``` ## How It Works The skill uses `yt-dlp`, a robust YouTube downloader that: - Automatically installs itself if not present - Fetches video information before downloading - Selects the best available streams matching your criteria - Merges video and audio streams when needed - Supports a wide range of YouTube video formats ## Important Notes - Downloads are saved to `/mnt/user-data/outputs/` by default - Video filename is automatically generated from the video title - The script handles installation of yt-dlp automatically - Only single videos are downloaded (playlists are skipped by default) - Higher quality videos may take longer to download and use more disk space


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: craigmbrown/massat-security-audit (version 1.0.0)

--- name: blindoracle-massat-audit version: 1.0.0 description: Security audit for multi-agent AI systems - OWASP ASI01-ASI10 author: craigmbrown category: security permissions: - network - file_read tags: - security - audit - owasp - multi-agent - compliance pricing: model: per-use amount: 50 currency: USD payment: x402 --- # BlindOracle MASSAT Security Audit Run a comprehensive OWASP ASI01-ASI10 security assessment on any multi-agent system via the BlindOracle API. Returns risk scores (0-100) per category with remediation priorities. ## When to Use - Before deploying a new multi-agent system to production - After adding new agents or tools to an existing fleet - As part of CI/CD pipeline security gates - For compliance reporting (OWASP Agentic AI Top 10) - To validate Microsoft AGT runtime governance is properly configured ## Quick Reference ```bash # Full audit (all 10 OWASP ASI categories) curl -X POST https://craigmbrown.com/api/v1/massat/audit \ -H "Content-Type: application/json" \ -H "X-Payment: x402" \ -d '{ "target": "https://github.com/your-org/your-agent-repo", "scope": "full", "categories": ["ASI01","ASI02","ASI03","ASI04","ASI05","ASI06","ASI07","ASI08","ASI09","ASI10"] }' # Quick scan (ASI01-ASI03 only, free tier) curl -X POST https://craigmbrown.com/api/v1/massat/audit \ -H "Content-Type: application/json" \ -d '{"target": "https://github.com/your-org/your-agent-repo", "scope": "quick"}' ``` ## Response Format ```json { "audit_id": "a1b2c3d4", "overall_score": 72, "risk_level": "MEDIUM", "categories": { "ASI01": {"score": 85, "findings": 2, "critical": 0, "name": "Prompt Injection"}, "ASI02": {"score": 60, "findings": 4, "critical": 1, "name": "Data Exfiltration"}, "ASI03": {"score": 90, "findings": 1, "critical": 0, "name": "Broken Access Control"} }, "total_findings": 23, "critical_findings": 3, "remediation_priority": ["ASI02", "ASI04", "ASI07"], "report_url": "https://craigmbrown.com/audits/a1b2c3d4.html" } ``` ## Pricing | Tier | Scope | Price | Categories | |------|-------|-------|-----------| | Free | Quick scan | $0 | ASI01-ASI03 | | Full | Complete audit | $50/use | ASI01-ASI10 | | API | Monthly subscription | $99/mo | Unlimited | ## Payment Full audits use x402 micropayments. Include `X-Payment: x402` header with ecash token. Free quick scans require no payment (10/day limit). ## Links - [MASSAT Framework (open-source)](https://github.com/craigmbrown/massat-framework) - [BlindOracle Marketplace](https://craigmbrown.com/blindoracle/) - [Whitepaper: Security Auditing a 94-Agent Fleet](https://craigmbrown.com/blindoracle/blog/agent-security-crisis.html)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openclawzhangchong/moondream-vision-zc (version 1.0.0)

# SKILL.md - Moondream Vision **name**: moondream-vision-zc **description**: 使用本地 Ollama 部署的 Moondream 模型进行图像理解，并将结果返回给 OpenClaw。该 Skill 适配 OpenClaw 2026 版本的多模态插件机制，可在聊天中直接发送图片或引用本地文件路径。 **type**: command ## 环境准备 1. **确保 Ollama 已安装** - Windows：`winget install ollama` 或从 https://ollama.com/download 下载并安装。 - 安装完成后，在 PowerShell 中运行 `ollama serve`，确保后台服务在 11434 端口监听。 2. **拉取 Moondream 模型**（如果尚未本地缓存） ```powershell ollama pull moondream ``` - 若已有本地模型，可跳过此步骤。 3. **验证模型** ```powershell ollama run moondream "一张猫的图片" ``` - 返回文字描述即表示模型工作正常。 4. **安装 Python 依赖**（仅在使用脚本时需要） ```powershell pip install requests ``` - 脚本通过 HTTP 调用 Ollama API。 ## Skill 实现 ### 目录结构 ``` ~/.openclaw/skills/moondream-vision/ ├─ SKILL.md # 本文件 └─ scripts/ └─ moondream_vision.py ``` ### scripts/moondream_vision.py ```python import sys, json, base64, requests, pathlib OLLAMA_URL = "http://127.0.0.1:11434/api/generate" def encode_image(path: str) -> str: data = pathlib.Path(path).read_bytes() return base64.b64encode(data).decode("utf-8") def run_moondream(image_path: str, prompt: str = ""): img_b64 = encode_image(image_path) payload = { "model": "moondream", "prompt": prompt, "images": [img_b64], "stream": False, } resp = requests.post(OLLAMA_URL, json=payload) resp.raise_for_status() result = resp.json() # Ollama returns a stream of tokens; when stream=False we get full response in ``response`` return result.get("response", "") if __name__ == "__main__": if len(sys.argv) < 2: sys.stderr.write("Usage: python moondream_vision.py <image_path> [prompt]\n") sys.exit(1) image = sys.argv[1] user_prompt = sys.argv[2] if len(sys.argv) > 2 else "" print(run_moondream(image, user_prompt)) ``` ### 在 OpenClaw 中注册命令 在 `~/.openclaw/config/skills.json`（若不存在请创建）添加如下条目： ```json { "name": "moondream-vision", "command": "python ${skill_dir}/scripts/moondream_vision.py", "description": "本地 Moondream 图像理解", "usage": "!moondream <image_path> [prompt]", "args": ["image_path", "prompt?"], "output": "text" } ``` - `${skill_dir}` 为此 skill 所在目录的绝对路径，OpenClaw 会在运行时自动替换。 - 通过 `!moondream D:\images\cat.jpg` 在聊天中调用。 ## 多模态接入方案说明 - **模型**：Moondream 是轻量级的视觉语言模型，适合本地推理。它通过 Ollama 的 REST API 接收 base64 编码的图像和可选文字提示，返回自然语言描述。 - **与 GPT‑OSS‑120B 结合**： - 在需要更深层次的推理时，可将 Moondream 的输出作为 **系统提示** 传递给 GPT‑OSS‑120B，让后者进行复杂的分析、摘要或跨模态推理。 - 示例工作流： 1. `!moondream img.png` ➜ 获得图片描述 `desc`。 2. 调用 `!gpt "基于以下描述，写一段新闻稿：\n${desc}"`。 - **性能**：Moondream 推理在普通笔记本 CPU 上约 1‑2 秒/图像，GPU 可进一步加速。GPT‑OSS‑120B 仍通过 OpenClaw 统一调度，保持统一日志与审计。 ## 常见问题 & 调试 - **Ollama 未启动**：确保 `ollama serve` 正在运行，检查防火墙是否阻止 11434 端口。 - **图片过大**：Ollama 限制单张图片约 5 MB，建议在本地压缩后再发送。 - **返回空**：确认 `prompt` 参数非空，或在 `payload` 中加入 `"system": ""` 防止模型误判。 ## 使用示例 ``` 用户：!moondream C:\Users\Administrator\Pictures\dog.jpg Assistant: 这是一只棕色的狗，正坐在草地上，注视着镜头。 用户：!moondream C:\Users\Administrator\Pictures\dog.jpg "请把这张图的内容写成一段简短的广告文案" Assistant: 「爱犬的欢笑，尽在自然」——让您的宠物在绿意盎然的草地上自由奔跑，感受生活的活力。 ``` --- *如有其他需求可进一步扩展，如批量处理、返回 JSON 结构等。*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/ugc-hook-analyzer (version 1.0.1)

--- name: ugc-hook-analyzer description: Analyze UGC openings for retention strength and generate improved hook alternatives tuned for TikTok, Reels, and Shorts. Use when the user asks why hooks underperform, wants first-3-second diagnosis, or needs niche-specific hook rewrites. --- # UGC Hook Analyzer ## Skill Card - **Category:** Creator Content - **Core problem:** Why do some UGC openings retain while others fail in the first seconds? - **Best for:** Pre-shoot UGC optimization - **Expected input:** UGC script or transcript + target audience + product angle + platform context - **Expected output:** Hook diagnosis + improved variants + execution notes - **Creatop handoff:** Use winning hooks in short-video-hook-lab and script packs ## Workflow 1. Classify the current hook archetype and likely retention weaknesses. 2. Map the mismatch between audience intent and the opening promise. 3. Generate alternative hooks across 2-3 emotional or strategic angles. 4. Add execution notes for visual framing, spoken delivery, and pacing. 5. Flag clickbait, compliance, or clarity risks before recommending rewrites. ## Output format Return in this order: 1. Executive summary (max 5 lines) 2. Hook diagnosis table 3. Improved hook variants by angle 4. Execution notes for delivery 5. Risks and watch-outs ## Quality and safety rules - Avoid clickbait promises that cannot be fulfilled. - Keep hooks consistent with product reality and compliance constraints. - Prefer concrete language over hype-only wording. - Distinguish between weak curiosity and strong audience-relevant tension. - Make rewrites feel native to short-form video, not ad-copy pasted into a hook. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: golngod/opc-wechat-channel-ops (version 1.0.0)

--- name: opc-wechat-channel-ops description: 视频号全流程运营工具，从选题策划到内容发布的数据驱动视频号运营体系 license: MIT compatibility: - claude-code - copilot - cursor - openclaw - coze author: 胡田 version: 1.0.0 --- # 视频号运营技能：老胡说 > 真人出镜，打造不可复制的技术成果转化IP ## 核心定位 | 项目 | 内容 | |------|------| | 视频号名称 | 老胡说 | | 账号定位 | 技术成果转化实战派 | | 出镜人 | 老胡（真实本人） | | 人设背景 | 央企投资公司前副总、产权交易机构前负责人，十余年实战经验 | | 差异化 | 真人出镜=最强IP护城河 | ## 与公众号的联动关系 ``` 公众号（深度内容） ←→ 视频号（精华讲解） ↕ ↕ 长文沉淀 短平快传播 ↕ ↕ 专业背书 建立信任 ``` ## 内容矩阵 ### 内容类型规划 | 类型 | 频率 | 时长 | 内容描述 | |------|------|------|----------| | 政策解读 | 每周2-3条 | 1-3分钟 | 最新政策要点 + 老胡点评 | | 案例分享 | 每周1条 | 2-5分钟 | 真实案例复盘 + 经验总结 | | 行业洞察 | 每周1条 | 1-2分钟 | 热点事件 + 专业分析 | | 问答互动 | 随机 | 1分钟 | 回复用户评论区问题 | | 直播精华 | 每月2-4次 | 3-5分钟 | 直播高光时刻剪辑 | ### 周内容节奏（与公众号7大板块联动） ``` 周一：全球财经热点解读（视频号）←→ 公众号深度分析 周二：全球科技动态速递（视频号）←→ 公众号技术解析 周三：OPC+AI实战干货（视频号）←→ 公众号详细教程 周四：案例复盘分享（视频号）←→ 公众号完整案例 周五：行业趋势洞察（视频号）←→ 公众号周报总结 周六：用户问答精选（视频号）←→ 公众号FAQ汇总 周日：本周精华回顾（视频号合集） ``` ## 真人出镜标准 ### 形象要求 - **着装**：商务休闲，浅色衬衫/Polo衫，体现专业感 - **发型**：干净利落，拍摄前整理 - **表情**：自然微笑，眼神自信 - **姿态**：坐姿端正/站姿挺拔，镜头感舒适 ### 场景布置 | 场景类型 | 适用内容 | 布置要点 | |----------|----------|----------| | 书架背景 | 政策解读、行业洞察 | 摆放专业书籍、技术类册子 | | 办公室 | 案例分享、经验总结 | 简洁桌面，展示专业氛围 | | 纯色背景 | 统一风格、后期特效 | 浅灰/浅蓝/白色，避免杂乱 | ### 设备清单 - **拍摄设备**：手机（iPhone 12以上/安卓旗舰） 或 相机 - **稳定设备**：手机支架/三脚架（高度与视线平齐） - **补光设备**：环形补光灯（推荐40cm直径） - **收音设备**：领夹麦克风（BOYA BY-M1 或同等） - **网络**：稳定的WiFi环境 ## 脚本结构模板 ### 标准1分钟脚本结构 ``` 【开场 Hook】(3秒) 大家好，我是老胡。今天聊一个很多人踩过的坑。 【核心内容】(45-50秒) - 要点1：[具体内容] - 要点2：[具体内容] - 要点3：[具体内容] 【老胡观点】(5-8秒) 我的看法是：...（一句话总结） 【行动号召 CTA】(3-5秒) 关注老胡，懂技术转化。评论区说说你的看法！ ``` ### 脚本风格规范 | 要求 | 说明 | |------|------| | 口语化 | 像和朋友聊天，不要照读稿 | | 有温度 | 适当加入个人经历和感受 | | 有钩子 | 前3秒必须抓住注意力 | | 有价值 | 每条视频至少1个干货点 | | 有互动 | 结尾引导评论和关注 | ## 拍摄流程 ### 拍摄前准备 1. **确定选题**：参考运营日历，提前1天准备 2. **撰写脚本**：按模板写出完整话术 3. **准备道具**：数据/案例/截图等素材 4. **场景布置**：调整灯光、背景、角度 5. **设备检查**：电量、存储、麦克风测试 ### 拍摄中要点 1. **景别选择**：中近景为主（胸部以上） 2. **眼神方向**：看镜头，而非看提词器 3. **语速控制**：每分钟150-180字 4. **重录原则**：卡壳超过3秒就重来 5. **多拍几条**：同一条录3-5遍，选最佳 ### 后期处理 | 工具 | 用途 | 难度 | |------|------|------| | 剪映（手机） | 剪辑、加字幕、滤镜 | 简单 | | 必剪（B站） | 剪辑、字幕、素材库 | 简单 | | Premiere | 精细调色、专业剪辑 | 复杂 | **标准后期流程**： 1. 导入素材 → 2. 剪辑拼接 → 3. 添加字幕 → 4. 调色统一 → 5. 添加片头片尾 → 6. 导出 ## 运营节奏 ### 日运营 | 时间段 | 内容 | |--------|------| | 7:00-8:00 | 发布黄金时段 | | 12:00-13:00 | 午间浏览时段 | | 20:00-22:00 | 晚间高峰时段 | ### 数据复盘 | 周期 | 内容 | |------|------| | 每日 | 查看播放量、点赞、评论 | | 每周 | 分析数据趋势，调整策略 | | 每月 | 复盘总结，制定下月计划 | ## 关联技能 - `skill-wechat-channel/references/content-strategy.md` - 内容策略详细规划 - `skill-wechat-channel/references/shooting-guide.md` - 拍摄指南 - `skill-wechat-channel/references/script-template.md` - 脚本模板库 - `skill-wechat-channel/references/linkage-strategy.md` - 公众号联动策略 - `skill-wechat-channel/references/operation-calendar.md` - 运营日历 ## 快速开始清单 - [ ] 确定视频号名称（建议与公众号一致：老胡说） - [ ] 准备拍摄设备（手机+支架+补光灯+麦克风） - [ ] 布置拍摄场景（书架/办公室/简洁背景） - [ ] 拍摄第一条视频（自我介绍/理念分享） - [ ] 完善账号主页（头像、简介、封面） - [ ] 与公众号完成首次联动发布 --- > **核心理念**：真人出镜是视频号最大的差异化。老胡的经验、经历、口吻，竞争对手无法复制。坚持做，你就是技术转化领域的第一IP。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rogue-agent1/jsonpretty (version 1.0.0)

--- name: jsonpretty description: Format, validate, query, flatten, and analyze JSON data. Use when asked to pretty-print JSON, validate JSON syntax, query nested JSON with dot-notation, flatten nested structures, get JSON statistics, or compact JSON output. Reads from files or stdin pipe. Zero dependencies — pure Python. --- # jsonpretty 📋 JSON swiss army knife — format, validate, query, flatten, stats. ## Commands ```bash # Pretty-print (from file or stdin) python3 scripts/jsonpretty.py data.json echo '{"a":1}' | python3 scripts/jsonpretty.py # Validate JSON python3 scripts/jsonpretty.py --validate data.json # Query with dot-notation echo '{"user":{"name":"Rogue"}}' | python3 scripts/jsonpretty.py -q "user.name" # Flatten nested JSON cat data.json | python3 scripts/jsonpretty.py --flat # Structure stats (key count, depth, types, size) python3 scripts/jsonpretty.py --stats data.json # Compact output (minify) python3 scripts/jsonpretty.py --compact data.json # Sort keys python3 scripts/jsonpretty.py --sort data.json ``` ## Features - Pretty-print with 2-space indent (default) - JSON validation with error messages - Dot-notation querying (supports array indices: `key[0].sub`) - Flatten nested objects to `key.subkey = value` pairs - Structure statistics: key count, nesting depth, type distribution, byte size - Compact/minify mode - Sorted key output - Reads from file or stdin


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aadipapp/wavelet-worldmodel-skill (version 1.0.0)

--- name: wavelet-world-model description: Generates a world model representation from state inputs using discrete wavelet transforms (DWT) to capture multi-resolution temporal and spatial features. author: tempguest version: 0.1.0 license: MIT --- # Wavelet World Model Skill This skill allows your OpenClaw agent to transform high-dimensional sequential state data into a compact world model representation using Wavelet Transforms. It leverages multi-resolution analysis to efficiently encode BOTH high-frequency details (rapid changes) and low-frequency components (long-term dependencies), making it highly effective for robotic control, continuous state tracking, and predicting complex environments. ## Commands - `wavelet-model`: standardized command to initialize the wavelet world model and process state inputs.

path: README.md
content: # Wavelet World Model Skill for OpenClaw

This skill enables an OpenClaw agent to build a multi-resolution "world model" of an environment or system state using Discrete Wavelet Transforms (DWT).

## Features

- **Multi-resolution State Encoding**: Captures both slow trends and fast perturbations in the environment.
- **Dimensionality Reduction**: Compresses state history while preserving critical temporal-spatial transitions.
- **Predictive Foundation**: Provides a robust, denoised state representation that can be fed into downstream RL or predictive neural networks.

## Installation

### Local Install (Development)
If you have the OpenClaw CLI installed:
```bash
openclaw skill install ./wavelet-world-model-skill
```

## Usage

Once installed, you can ask OpenClaw:
> "Generate a wavelet world model from my current state data"
> "Run the wavelet-model skill"

## Publishing to ClawHub

To share this skill:

1.  **Login to ClawHub**:
    ```bash
    clawhub login
    ```

2.  **Publish the Skill**:
    Navigate to the skill directory and run:
    ```bash
    clawhub publish
    ```


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