OpenClaw/clawhub-security-signals huggingface dataset:
Skill: n1neman/wewe-rss-wechat-export (version 1.0.0)

--- name: wewe-rss-wechat-export description: "适配 wewe-rss 的公众号抓取与导出工具：把微信公众号 / JSON feed 批量导出为清洗后的 DOCX 文档，并支持日期前缀命名与 zip 打包。适用于导出公众号文章、批量生成 Word 交付包、保留正文清洗结果等场景。" --- # wewe-rss WeChat Export > 适配 **wewe-rss** 的公众号抓取与文档导出工具。 ## 它做什么 这个 skill 用于把符合 wewe-rss / JSON Feed 风格的公众号 feed 批量导出为清洗后的 Word 文档，并按需要生成 zip 交付包。 核心能力： - 分页抓取 feed - 清洗公众号文章正文 - 过滤脚本、样式和页面级垃圾标签 - 图片本地化 - Pandoc 转 DOCX - 生成 `index.txt` 汇总 - 可选打包为 zip ## 适用场景 当用户想做这些事情时使用本 skill： - 导出微信公众号文章为 DOCX - 把 wewe-rss feed 做成可交付文档包 - 给导出文件自动加日期前缀 - 批量抓取公众号文章并打包 - 保留 HTML / Markdown / DOCX 作为验收产物 ## 执行原则 1. 统一通过 `scripts/run-export-feed.sh` 调用。 2. 不在 skill 内重复实现导出逻辑。 3. 默认交付参数优先选择： - `--output-mode docx` - `--rename-mode dated` - `--zip` 4. 导出前检查依赖：`node`、`pandoc`、`curl`、`python3`。 5. 若未指定输出目录，默认输出到当前工作目录下的 `export/`。 6. `--batch-size` 最大限制为 `5`，避免过度抓取。 ## 参数格式 ```text bash <skill_dir>/scripts/run-export-feed.sh <feed_url> [count] [output_dir] [--batch-size N] [--output-mode docx|full] [--rename-mode dated|plain] [--zip] ``` ### 位置参数 - `feed_url`：必填，公众号 / wewe-rss feed JSON 地址 - `count`：可选，默认 `100` - `output_dir`：可选，默认落到当前工作目录 `export/` ### 可选项 - `--batch-size N`：分页批大小，默认 `2`，最大 `5` - `--output-mode docx|full` - `docx`：仅保留适合交付的 DOCX 结果 - `full`：保留 HTML / Markdown / DOCX 与资源目录 - `--rename-mode dated|plain` - `dated`：`YYYY-MM-DD-序号-标题.docx` - `plain`：`序号-标题.docx` - `--zip`：导出完成后额外生成 zip ## 推荐用法 ### 最终交付型导出 ```bash bash <skill_dir>/scripts/run-export-feed.sh \ "https://example.com/feed.json" \ 100 \ "./export/my-feed" \ --batch-size 5 \ --output-mode docx \ --rename-mode dated \ --zip ``` ### 小样本验收 ```bash bash <skill_dir>/scripts/run-export-feed.sh \ "https://example.com/feed.json" \ 5 \ "./export/my-feed-smoke" \ --batch-size 1 \ --output-mode full \ --rename-mode dated ``` ## 输出约定 成功后，至少返回： - `output_dir` - `index_path` - `zip_path`（如果启用了 `--zip`） - `output_mode` - `rename_mode` ## 目录结构 ```text wewe-rss-wechat-export/ ├── CHANGELOG.md ├── INSTALL.md ├── LICENSE ├── README.md ├── SKILL.md ├── _meta.json ├── manifest.yaml └── scripts/ ├── export-feed-single-pages.mjs └── run-export-feed.sh ```


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 26medias/runware (version 1.0.0)

--- name: runware description: Generate images and videos via Runware API. Access to FLUX, Stable Diffusion, Kling AI, and other top models. Supports text-to-image, image-to-image, upscaling, text-to-video, and image-to-video. Use when generating images, creating videos from prompts or images, upscaling images, or doing AI image transformation. --- # Runware Image and video generation via Runware's unified API. Access FLUX, Stable Diffusion XL, Kling AI, and more. ## Setup Set `RUNWARE_API_KEY` environment variable, or pass `--api-key` to scripts. Get API key: https://runware.ai ## Image Generation ### Text-to-Image ```bash python3 scripts/image.py gen "a cyberpunk city at sunset, neon lights, rain" --count 2 -o ./images ``` Options: - `--model`: Model ID (default: `runware:101@1` / FLUX.1 Dev) - `--width/--height`: Dimensions (default: 1024x1024) - `--steps`: Inference steps (default: 25) - `--cfg`: CFG scale (default: 7.5) - `--count/-n`: Number of images - `--negative`: Negative prompt - `--seed`: Reproducible seed - `--lora`: LoRA model ID - `--format`: png/jpg/webp ### Image-to-Image Transform an existing image: ```bash python3 scripts/image.py img2img ./photo.jpg "watercolor painting style" --strength 0.7 ``` - `--strength`: How much to transform (0=keep original, 1=ignore original) ### Upscale ```bash python3 scripts/image.py upscale ./small.png --factor 4 -o ./large.png ``` ### List Models ```bash python3 scripts/image.py models ``` ## Video Generation ### Text-to-Video ```bash python3 scripts/video.py gen "a cat playing with yarn, cute, high quality" --duration 5 -o ./cat.mp4 ``` Options: - `--model`: Model ID (default: `klingai:5@3` / Kling AI 1.6 Pro) - `--duration`: Length in seconds - `--width/--height`: Resolution (default: 1920x1080) - `--negative`: Negative prompt - `--format`: mp4/webm/mov - `--max-wait`: Polling timeout (default: 600s) ### Image-to-Video Animate an image or interpolate between frames: ```bash # Single image (becomes first frame) python3 scripts/video.py img2vid ./start.png --prompt "zoom out slowly" -o ./animated.mp4 # Two images (first and last frame) python3 scripts/video.py img2vid ./start.png ./end.png --duration 5 ``` ### List Video Models ```bash python3 scripts/video.py models ``` ## Popular Models ### Image | Model | ID | |-------|-----| | FLUX.1 Dev | `runware:101@1` | | FLUX.1 Schnell (fast) | `runware:100@1` | | FLUX.1 Kontext | `runware:106@1` | | Stable Diffusion XL | `civitai:101055@128080` | | RealVisXL | `civitai:139562@297320` | ### Video | Model | ID | |-------|-----| | Kling AI 1.6 Pro | `klingai:5@3` | | Kling AI 1.5 Pro | `klingai:3@2` | | Runway Gen-3 | `runwayml:1@1` | Browse all: https://runware.ai/models ## Notes - Video generation is async; scripts poll until complete - Costs vary by model — check https://runware.ai/pricing - FLUX models are excellent for quality; Schnell is faster - For best video results, use descriptive prompts with motion words


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: michaelfeng/factorhub (version 0.1.2)

--- name: factorhub description: 提供 5500+ 只 A 股的量化因子数据和财经指标，支持因子分析、行情查询、资金流向、财务分析及策略回测 user-invocable: true metadata: {"openclaw": {"homepage": "https://factorhub.cn", "requires": {"bins": ["factorhub-mcp"]}}} --- # FactorHub A股量化数据 提供 5500+ 只 A 股的量化因子数据和财经指标，通过 MCP 协议接入 AI Agent。 ## 能力 - 因子数据：查询因子列表、因子评分指标、因子净值曲线 - 市场行情：个股日线、指数日线、估值指标（PE/PB/PS/股息率） - 股票信息：个股基本面、股票筛选 - 交易日历：查询交易日 - 策略回测：涨停首板、低估值、动量、均值回归等内置策略 ## 安装 ```bash clawhub install factorhub ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/kernel (version 1.0.0)

--- name: Kernel description: Avoid common Linux kernel mistakes — atomic context violations, allocation failures, and locking traps. metadata: {"clawdbot":{"emoji":"🐧","os":["linux"]}} --- ## Atomic Context Traps - `spin_lock` held = cannot sleep — no `kmalloc(GFP_KERNEL)`, no `mutex_lock`, no `copy_from_user` - Interrupt can take same spinlock — must use `spin_lock_irqsave`, not plain `spin_lock` - `rcu_read_lock()` section cannot sleep — no blocking calls inside RCU read-side - `might_sleep()` annotation — add to functions that may sleep, catches bugs with `CONFIG_DEBUG_ATOMIC_SLEEP` ## Allocation Failures - `GFP_ATOMIC` can return NULL — always check, don't assume success - `vmalloc` memory not physically contiguous — cannot use for DMA - `kzalloc` over `kmalloc` — uninitialized memory leaks kernel info to userspace - Allocation in loop risks OOM — preallocate or use memory pool ## User Pointer Handling - `copy_from_user` returns bytes NOT copied — 0 means success, not failure - Never use `%s` with user pointer in printk — kernel crash or info leak - User memory can change during syscall — copy to kernel buffer, validate the copy - `__user` annotation is documentation — doesn't enforce anything, you must use copy functions ## Memory Ordering - `READ_ONCE`/`WRITE_ONCE` for lockless shared data — prevents compiler from caching/reordering - Spinlock release has implicit barrier — but check-then-act patterns still need care - `smp_wmb()` before publishing pointer — ensures data visible before pointer is ## Module Error Paths - Init fails midway — must undo everything already done - Reverse order cleanup — unregister in opposite order of register - `goto err_*` pattern standard — cleaner than nested ifs - Check what's actually initialized — don't free/unregister what wasn't set up ## Locking Mistakes - Same lock acquired twice = deadlock — even in different functions - Inconsistent lock ordering — document order, acquire in same sequence everywhere - `mutex_trylock` returns 1 on success — opposite of `pthread_mutex_trylock` - Reader-writer locks rarely worth it — contention overhead usually exceeds benefit


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/jpeng-job-poster (version 1.0.0)

--- name: jpeng-job-poster description: "Post job listings" version: "1.0.0" author: "jpeng" tags: ["recruitment", "job", "hr"] --- # Job Poster Post job listings ## When to Use - User needs recruitment related functionality - Automating job tasks - Hr operations ## Usage ```bash python3 scripts/job_poster.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export JOB_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: reed1898/sec-13f-tracker (version 1.0.0)

--- name: sec-13f-tracker description: Track top fund managers' holdings via SEC 13F filings. Fetches quarterly filings from SEC EDGAR, compares quarter-over-quarter changes (new positions, increases, decreases, exits), and generates analysis reports with cross-fund convergence insights. Use when user asks about hedge fund holdings, 13F filings, whale tracking, what Buffett/Dalio/Ackman/Soros/Cathie Wood are buying or selling, or institutional investor portfolio changes. --- # SEC 13F Whale Tracker Track what Buffett, Dalio, Ackman, Soros, and Cathie Wood are buying and selling via SEC EDGAR 13F filings. ## Setup (one-time) ```bash SKILL_DIR="$(dirname "$(readlink -f "$0")")" # or resolve from skill path cd /tmp && python3 -m venv sec13f-venv source sec13f-venv/bin/activate pip install -r "$SKILL_DIR/scripts/requirements.txt" ``` Or reuse an existing venv if available. The only dependency is `requests`. ## Run Tracker ```bash python3 scripts/tracker.py ``` Output: - `data/` — cached holdings JSON per fund per quarter (auto-created) - `reports/latest.md` — most recent report - `reports/13f-report-YYYY-MM-DD.md` — dated report ## Default Tracked Funds | Fund | Manager | CIK | |------|---------|-----| | Berkshire Hathaway | Warren Buffett | 0001067983 | | Bridgewater Associates | Ray Dalio | 0001350694 | | Pershing Square | Bill Ackman | 0001336528 | | Soros Fund Management | George Soros | 0001029160 | | ARK Invest | Cathie Wood | 0001697748 | To add funds, edit the `FUNDS` dict in `scripts/tracker.py`. Find CIK numbers at [SEC EDGAR](https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany). ## Report Structure Each fund section includes: - Report period and filing date - Total holdings value and count - 🆕 New positions with value and shares - 📈 Increased positions with % change - 📉 Decreased positions with % change - 🚫 Closed positions Cross-fund insights section highlights: - Stocks where multiple managers converge (buy or sell simultaneously) ## Cron Integration Schedule quarterly runs (13F filings are due ~45 days after quarter end): ``` # Run mid-February, May, August, November 0 8 15 2,5,8,11 * cd /path/to/project && venv/bin/python scripts/tracker.py ``` Or use OpenClaw cron to run and send the report to a channel. ## Technical Notes - SEC EDGAR API is free and public; requires `User-Agent` header - Rate limited to <10 req/sec (script adds 150ms delay between requests) - Parses 13F information table XML, handles namespace detection - Merges holdings by CUSIP, separates put/call options - Caches data in `data/` to avoid re-fetching known quarters - No proxy needed for SEC EDGAR (script clears proxy env vars)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ckchzh/kafka (version 3.0.1)

--- name: kafka version: "3.0.1" author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills license: MIT-0 tags: [kafka, tool, utility] description: "Produce, consume, and manage Kafka topics with lag monitoring and data export. Use when publishing messages, consuming topics, monitoring consumer lag." --- # kafka Produce, consume, and manage Kafka topics with lag monitoring and data export. Use when publishing messages, consuming topics, monitoring consumer lag. ## Commands ### `KAFKA_BOOTSTRAP` Bootstrap servers (default: localhost:9092) ```bash scripts/script.sh KAFKA_BOOTSTRAP ``` ### `KAFKA_ZOOKEEPER` Zookeeper address (default: localhost:2181) ```bash scripts/script.sh KAFKA_ZOOKEEPER ``` ### `KAFKA_HOME` Kafka installation directory ```bash scripts/script.sh KAFKA_HOME ``` ### `topics` List all topics with partition counts ```bash scripts/script.sh topics ``` ### `create-topic` Create topic (partitions, replication factor) ```bash scripts/script.sh create-topic <name> [p] [r] ``` ### `describe` Detailed topic description ```bash scripts/script.sh describe <topic> ``` ### `produce` Produce a message (optional key) ```bash scripts/script.sh produce <topic> <msg> [key] ``` ### `consume` Consume messages (default: 10, from-beginning) ```bash scripts/script.sh consume <topic> [count] [from] ``` ### `groups` List all consumer groups ```bash scripts/script.sh groups ``` ### `lag` Show consumer group lag ```bash scripts/script.sh lag <group> ``` ### `config` Show connection config and available tools ```bash scripts/script.sh config ``` ### `status` Cluster health and status check ```bash scripts/script.sh status ``` ### `delete-topic` Delete a topic (with confirmation) ```bash scripts/script.sh delete-topic <topic> ``` ### `partitions` Increase topic partitions ```bash scripts/script.sh partitions <topic> <count> ``` ### `offsets` Show earliest/latest offsets ```bash scripts/script.sh offsets <topic> ``` ## Requirements - Kafka CLI tools --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openlark/lightweight-team-orchestration (version 1.0.0)

--- name: lightweight-team-orchestration description: Lightweight multi-agent team orchestration. Output structure simplified to two folders: agents/ and projects/. --- # Lightweight Team Orchestration A lightweight multi-agent team collaboration skill with a simplified output structure focused on core deliverables. ## Use Cases - Creating simple teams with 2+ agents - Agent role definition and task distribution - Agent output artifact management (with versioning) - Lightweight collaboration workflows (no complex workflow engine required) ## Quick Start ### Output Structure ``` [TEAM_NAME]/ ├── agents/ # Agent role definitions │ ├── orchestrator/ # Orchestrator │ │ └── SOUL.md # Role definition │ ├── builder/ # Executor │ │ └── SOUL.md │ └── reviewer/ # Reviewer (optional) │ └── SOUL.md └── projects/ # Output artifacts (with versioning) ├── v1.0.0/ # Version directory │ ├── builder.md # Builder output │ └── reviewer.md # Reviewer output └── v1.1.0/ # Next version ``` ### Workflow 1. **Create Agent Roles** → Create SOUL.md for each agent under `agents/` 2. **Distribute Tasks** → Launch agents using sessions_spawn 3. **Collect Artifacts** → Store artifacts in `projects/v{version}/` 4. **Version Management** → Create a new version directory for each delivery ## Agent Role Definitions ### agents/orchestrator/SOUL.md ```markdown # Orchestrator ## Responsibilities - Task distribution and progress tracking - Coordination between agents - Artifact version management ## Behaviors - Use high-reasoning model - Keep task status updated ``` ### agents/builder/SOUL.md ```markdown # Executor ## Responsibilities - Execute specific tasks - Produce deliverables ## Behaviors - Deliver according to specifications - Annotate version number ``` ### agents/reviewer/SOUL.md (optional) ```markdown # Reviewer ## Responsibilities - Verify output quality - Propose improvement suggestions ``` ## Task Distribution Launch agents using sessions_spawn: ``` Task: [Task description] Output: projects/v{version}/{agent}.md Verify: [Verification method] ``` ## Version Management - Initial version: v1.0.0 - Each iteration: Increment version number - Preserve history: All versions retained in projects/ ## When to Use - Simple 2-3 person teams - No complex workflow engine required - Focus on artifact delivery over process ## When Not to Use - Complex multi-stage workflows - Real-time status tracking → Use task boards - Large-scale agent coordination


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: frankieway/yuqing-push-skill (version 1.1.0)

name: bitable_to_feishu_webhook version: "1.1.0" description: > 从飞书多维表中按用户规则筛选记录，自动将格式化后的内容推送到指定的群机器人 Webhook（飞书群消息）。 内部使用字段「是否推送」作为推送状态： - 空：尚未判断 - 不推送：已判断，不符合推送条件 - 待推送：本次命中推送条件，准备推送 - 已推送：已成功推送 本 Skill 仅扫描「是否推送」为空的记录。 entrypoint: command: "python" args: - "push_skill.py" inputs: - name: bitable_url type: string required: true description: 目标飞书多维表视图链接（包含 base/app_token 和 table 参数） - name: app_id type: string required: true description: 飞书开放平台应用 APP_ID（用于获取 tenant_access_token） - name: app_secret type: string required: true description: 飞书开放平台应用 APP_SECRET - name: webhook_url type: string required: true description: 飞书群机器人的 Webhook 地址（例如 https://open.feishu.cn/open-apis/bot/v2/hook/xxx） - name: rule_expression type: string required: true description: | 触发条件表达式（Python 表达式），返回 True/False。 使用 fields['字段名'] 访问多维表字段，例如： "('负向' in fields.get('评价情感（机器）','')) and ('小米' in fields.get('正文',''))" - name: message_template type: string required: true description: | 推送内容模版，使用 {字段名} 占位，如： "【负向舆情预警]\n标题：{标题}\n情感：{评价情感（机器）}\n正文：{正文}\n链接：{原文 URL}" - name: limit type: integer required: false default: 50 description: 本次最多检查并推送的记录条数（按多维表视图排序） outputs: - name: pushed_count type: integer description: 本次实际推送的消息条数 permissions: network: - "https://open.feishu.cn"


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: t9530638/termux-zero-token (version 1.0.0)

--- summary: "用手機 Chrome 登入的 credentials，免費調用 DeepSeek/Kimi/Qwen/GLM 等 AI 模型" read_when: - 想免費使用各種 AI 模型 - 需要在 Termux 環境使用 AI - 已經有手機 Chrome 登入的 AI 帳號 title: "termux-zero-token" tags: - ai - chrome - deepseek - kimi - qwen - glm - free - termux author: "安卓" author_link: "telegram:@owner" version: "1.0.0" min_openclaw_version: "2026.3.0" license: MIT repo_url: "https://github.com/openclaw/termux-zero-token" issues_url: "https://github.com/openclaw/termux-zero-token/issues" --- # termux-zero-token 在 Android Termux 上使用手機 Chrome 瀏覽器登入的 credentials，免費調用各種 AI 模型。 ## 🎯 功能特色 - 🤖 **多 AI 提供商**：DeepSeek、Kimi、Qwen、GLM - 📱 **手機 Chrome**：使用已登入的瀏覽器 session - 🔐 **無需 API Key**：直接使用瀏覽器 cookies - ⚡ **完全免費**：用自己的帳號調用 AI ## 📱 支持的 AI | 提供商 | 狀態 | 模型 | 費用 | |--------|------|------|------| | DeepSeek | ✅ | deepseek-chat, deepseek-reasoner | 免費 | | Kimi | ✅ | moonshot-v1-8k/32k/128K | 免費 | | Qwen | ✅ | qwen-turbo/plus/max | 免費 | | GLM | ✅ | glm-4-flash/plus | 免費 | ## 🚀 快速開始 ### 前置條件 1. **電腦安裝 ADB** ```bash # macOS brew install adb # Linux sudo apt install adb ``` 2. **手機開啟開發者選項** - 設定 → 關於手機 → 連續點擊版本號 7 次 - 開發者選項 → 啟用 USB 偵錯 3. **電腦連接手機** ```bash adb devices adb forward tcp:9222 tcp:9222 ``` ### 使用流程 ```bash # 1. 確保手機 Chrome 已登入 AI 網站 # DeepSeek: https://chat.deepseek.com # Kimi: https://kimi.moonshot.cn # Qwen: https://qwen.chat # GLM: https://chatglm.cn # 2. 連接手機 Chrome（需要 ADB port forward） # 系統會自動連接 localhost:9222 # 3. 提取 credentials # 運行 import 命令（CLI 開發中） # 4. 使用 AI # 直接調用相應的 provider ``` ## 🔧 技術原理 ``` ┌──────────────────────────────────────────────────────┐ │ 系統架構 │ ├──────────────────────────────────────────────────────┤ │ │ │ ┌─────────────┐ adb forward ┌─────────┐ │ │ │ 手機 Chrome │ ◄──────────────────► │ Termux │ │ │ │ (已登入AI) │ tcp:9222 │ (本AI) │ │ │ └─────────────┘ └────┬────┘ │ │ │ │ │ ▼ │ │ ┌──────────────────────────────────────────────┐ │ │ │ Credentials 提取 │ │ │ │ - CDP 連接手機 Chrome │ │ │ │ - 捕獲 cookies/session │ │ │ │ - 存儲到 ~/.openclaw/zero-token/ │ │ │ └──────────────────────────────────────────────┘ │ │ │ │ │ ▼ │ │ ┌──────────────────────────────────────────────┐ │ │ │ AI Provider 調用 │ │ │ │ - 使用 cookies 調用 Web API │ │ │ │ - 模擬瀏覽器會話 │ │ │ └──────────────────────────────────────────────┘ │ │ │ └──────────────────────────────────────────────────────┘ ``` ## 💰 商業模式 這個技能可以這樣賺錢： 1. **技術服務**：幫用戶配置，收取服務費 $5-10 2. **訂閱制**：$3/月，無限使用所有 AI 3. **企業部署**：幫企業建立內部 AI 系統 ## ⚠️ 限制與風險 1. **Cookies 會過期** - 需要定期更新 2. **違反 AI 服務條款** - 僅供個人學習使用 3. **無法保證永久可用** - AI 公司可能會變更 API ## 📞 支援 - 問題反饋：GitHub Issues - 商務合作：直接聯繫老闆 --- **版權聲明**：本 Skill 基於 openclaw-zero-token 開發，僅供學習和研究使用。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: gingiris/go-to-market-strategy (version 1.0.0)

# Go-to-Market Strategy Playbook Complete GTM strategy guide for startups and product launches. Real case studies from Manus, Devin, AFFiNE (33k GitHub stars), and 30x Product Hunt #1 launches. This skill redirects to the comprehensive **Gingiris Launch Playbook** which covers: - Go-to-market planning frameworks - Market positioning & messaging - Channel strategy (Product Hunt, Reddit, HN, Twitter) - Launch timeline & milestone planning - Post-launch growth tactics ## Quick Start Read the full playbook: ```bash cat ~/.openclaw/skills/gingiris-launch/SKILL.md ``` ## Resources - **GitHub**: https://github.com/Gingiris/launch - **Full Playbook**: Install `gingiris-launch` from ClawdHub 🇨🇳 GTM 市场进入策略指南 | 🇯🇵 GTM 市場参入戦略ガイド | 🇰🇷 GTM 시장 진입 전략 **Keywords**: go-to-market strategy, GTM strategy, GTM planning, market entry strategy, product positioning, launch strategy, startup GTM, go to market plan, GTM framework, 市场进入策略, GTM 战略


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: eightroad/channel-activity-yanyue (version 3.0.0)

--- name: channel-activity description: Multi-channel short-term memory for AI assistants version: 3.0.0 tags: memory, multi-channel, context, summary --- # Channel Activity Record and query activities from different channels with 30-minute TTL. ## Install ```bash npx clawhub@latest install channel-activity ``` ## Usage ```python from channel_activity import ChannelActivity ca = ChannelActivity() ca.record("feishu", "Task request") summary = ca.get_context_summary(channel="qq") ``` ## Features - Multi-channel support (Feishu, QQ, etc.) - 30-minute TTL with auto cleanup - Smart summarization (50 chars/message) - Cross-channel query ## License MIT


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