OpenClaw/clawhub-security-signals huggingface dataset:
Skill: flowertusi/genos-dna (version 0.1.1)

--- name: Genos基因序列分析 description: 使用 Genos 模型进行 DNA 序列分析。当用户提到 DNA、基因、基因组、碱基序列、ACGT 等生物信息学相关问题时使用此技能。 --- # Genos DNA 序列分析技能 ## 概述 此技能使用之江实验室的 Genos-1.2B 模型进行 DNA 序列分析。该模型是人类基因组基础模型，专门用于分析 DNA 碱基序列。 ## 使用场景 - DNA 序列分析 - 基因组预测 - 碱基频率统计 - 序列模式识别 - 基因组学研究相关问题 ## 模型信息 - **模型**: Genos-1.2B - **参数**: 12 亿 - **架构**: MoE (Mixture of Experts) - **词汇量**: 128 (A, C, G, T, N + 特殊标记) - **上下文长度**: 最长 1M 碱基对 ## 使用方法 ### 1. DNA 碱基序列分析 当用户提供 DNA 序列（如 ACGTACGT...）时，调用 `analyze_dna_sequence` 函数进行分析。 ### 2. 预测下一个碱基 调用 `predict_next_base` 函数预测 DNA 序列中下一个可能的碱基。 ### 3. 序列特征提取 调用 `extract_sequence_features` 函数提取序列的特征信息。 ## 示例 ### 输入格式 用户可能提供： - DNA 序列: `ACGTACGTACGT...` - FASTA 格式的基因序列 - 简单描述: "帮我分析这段 DNA 序列" ### 输出格式 返回分析结果，包括： - 碱基组成统计 - 序列长度 - 预测结果 ## 注意事项 1. 此模型仅支持 DNA 碱基字符（A, C, G, T, N） 2. 不支持中文或英文自然语言输入 3. 输入前需去除空格、换行等非碱基字符 4. 模型主要用于基因组学研究，不适用于对话任务 ## 模型状态检查与启动 **重要：在调用技能前，必须先检查模型是否已启动！** ### 检查模型状态 模型状态记录在 `./scripts/.model_loaded` 文件中（相对于项目根目录）。 - 如果文件存在且内容为 `loaded`，表示模型已启动 - 如果文件不存在或内容不是 `loaded`，需要先启动模型 ### 启动模型 如果模型未启动，执行以下命令启动： ```bash # 设置模型路径（可选，默认为 ./models/Genos-1___2B） export GENOS_MODEL_PATH="./models/Genos-1___2B" # 启动模型 python3 -c " import sys sys.path.insert(0, './scripts') from genos_dna import load_model load_model() with open('./.model_loaded', 'w') as f: f.write('loaded') print('Model loaded and status saved') " ``` ### 自动化检查 AI 助手在调用技能时应自动完成以下步骤： 1. 检查 `./.model_loaded` 文件是否存在且内容为 `loaded` 2. 如果模型未启动，先执行上述启动命令 3. 确认模型启动后再调用技能函数 ### 使用环境变量配置 你也可以通过环境变量自定义路径： ```bash # 设置模型路径 export GENOS_MODEL_PATH="/path/to/your/model" # 设置状态文件路径 export GENOS_STATUS_FILE="/path/to/your/state/.model_loaded" # 然后运行脚本 python3 your_script.py ``` ### 配置文件方式 你也可以创建 `config.json` 文件来配置路径： ```json { "model_path": "./models/Genos-1___2B", "state_file": "./scripts/.model_loaded" } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: pengzhendong/dashscope-llm (version 1.0.1)

--- name: dashscope-llm description: "通过阿里云 DashScope 的 OpenAI 兼容 API 发送简单单轮对话请求，用于快速 LLM 测试、提示词实验或一次性文本生成。" version: "1.0.0" --- # dashscope-llm Use this skill when you need to send a simple chat request to DashScope through its OpenAI-compatible API. ## What this skill does This skill provides a minimal CLI wrapper at `scripts/cli.py` that: - reads `DASHSCOPE_API_KEY` from the environment - connects to `https://dashscope.aliyuncs.com/compatible-mode/v1` - sends a single user message with the OpenAI Python SDK - prints the model response to stdout ## When to use it Use this skill for quick LLM checks, prompt experiments, or simple one-shot generations against DashScope models. Do not use it when you need: - multi-turn conversation state - streaming output - tool calling - structured output handling - advanced retry or error management ## Prerequisites Make sure the environment has: - `DASHSCOPE_API_KEY` set - Python available - the `openai` Python package installed Example: ```bash export DASHSCOPE_API_KEY="your_api_key" python3 scripts/cli.py --model qwen3.6-plus --message "你是谁？" ``` ## CLI arguments The script supports: - `--model`: model name, default is `qwen3.6-plus` - `--message`: user prompt, default is `你是谁？`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rqcker/ytb-transcript (version 1.0.0)

--- name: ytb-transcript description: Fetch YouTube video transcripts, subtitles, and generate summaries. Use when the user wants to extract text from a YouTube video, download subtitles (similar to downsub.com), get timestamped transcripts, or get AI-generated summaries of video content. Supports raw transcript extraction, timestamped subtitles, and structured key point summaries. Ideal for research, academic study, lecture note-taking, content analysis, or processing long videos without watching them. --- # YTB Transcript ## Overview This skill fetches transcripts and subtitles from YouTube videos and can generate intelligent summaries. Use this skill for: - Extracting text content from YouTube videos - Downloading subtitles (downsub.com style) - Getting timestamped transcripts - Generating summaries of video content ## How to Use Provide a YouTube URL or video ID. The skill will: 1. Extract the video ID from the URL 2. Attempt to fetch the transcript using available services 3. If requested, generate a summary of the content 4. Return clean, usable output ### Examples **Get transcript:** - "Get the transcript from https://youtu.be/xxxxxx" - "Extract subtitles from this video: [url]" **Get summary:** - "Summarize this YouTube video: [url]" - "What are the key points from this lecture?" **Combined request:** - "Get the transcript and summarize this video: [url]" ## Available Functions ### Transcript Fetching - Returns full transcript text - Can include timestamps when available - Supports multiple languages (prioritizes Chinese/English) ### Summarization - Generates concise or detailed summaries - Can focus on key arguments, technical content, or main ideas - Adjustable summary length ## Notes - Works best with videos that have subtitles (auto-generated or uploaded) - For videos without subtitles, it may fall back to metadata-based summarization - Long videos may take longer to process - Output is clean markdown/text suitable for research or note-taking ## Resources See `references/methods.md` for implementation details and supported services. See `scripts/fetch.py` for the core fetching logic.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: agnicpay-prog/agnic-agent-email (version 2.0.2)

--- name: agent-email description: > Send and receive email as your AI agent. Use when the user wants to check inbox, send email, reply to messages, set up an email alias, or manage agent email. Covers "check my email", "send an email", "what's my agent email", "set up email", "inbox". user-invocable: true disable-model-invocation: false allowed-tools: - "Bash(npx agnic@latest status*)" - "Bash(npx agnic@latest email *)" --- # Agent Email Each agent gets a unique email address in the format `agent-<id>@agnic.ai`. Use `npx agnic@latest email` commands to manage it. ## Authentication Run `npx agnic@latest status --json` to verify. If not authenticated: - **Headless (CI/server/agent)**: Set `AGNIC_TOKEN` env var or pass `--token <token>` - **Interactive (has browser)**: Run `npx agnic@latest auth login` See the `authenticate-wallet` skill for details. ## Commands ### Set up email alias ```bash npx agnic@latest email setup --display-name "My Agent" --json ``` ### Check email address ```bash npx agnic@latest email address --json ``` ### Check inbox ```bash npx agnic@latest email inbox --limit 10 --json ``` ### Send email ```bash npx agnic@latest email send --to <address> --subject "<subject>" --body "<body>" ``` ### Reply to a message ```bash npx agnic@latest email reply --message-id <id> --body "<reply text>" ``` ## Input Validation Before constructing commands, validate user-provided values: - **--to**: Must be a valid email address. Reject if it contains spaces, semicolons, pipes, or backticks. - **--subject**: Single-quote the value. Escape internal single quotes. - **--body**: Single-quote the value. Escape internal single quotes. - **--message-id**: Must be alphanumeric or UUID format. Do not pass unvalidated user input into the command. ## Important Notes - Emails are stored with **30-day retention** - Display name can be set once during setup - Inbox returns most recent messages first ## Prerequisites - Must be authenticated (`npx agnic@latest status` to check) - Agent identity must exist (created automatically during sign-up) ## Error Handling Common errors: - "Not authenticated" -- Run `npx agnic@latest auth login` or set `AGNIC_TOKEN` - "No email alias found" -- Run `npx agnic@latest email setup` first - "Agent not found" -- The user may not have an agent registered; sign up at [app.agnic.ai](https://app.agnic.ai) - "Message not found" -- Check the message ID with `email inbox` first


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bg1avd/clawhub-publisher-auto (version 1.0.0)

--- name: ClawHub Publisher slug: clawhub-publisher-auto description: "Automated skill publishing to ClawHub. Fix push error issue with acceptLicenseTerms support" type: skill version: 1.0.0 author: Lin license: MIT --- # ClawHub Publisher **Version:** 1.0.0 **Author:** Midas Skills **License:** MIT ## Description Automated skill publishing to ClawHub. Version management, changelog generation, asset bundling, one-command deploy. ## Value Proposition Automated skill publishing to ClawHub. Version management, changelog generation, asset bundling, instant deployment. ## Category developer-tools ## Tags publishing, automation, ci-cd, versioning, deployment ## Skill Type developer-tool ## Pricing - **Free:** $0 - **Pro:** $59.99 ## Key Features - ✅ One-command skill publishing - ✅ Automatic version bumping (major/minor/patch) - ✅ Git-powered changelog generation - ✅ Multi-file asset bundling - ✅ Metadata validation - ✅ README optimization - ✅ Gumroad link injection - ✅ Publishing history & rollback - ✅ Skill analytics - ✅ Team collaboration support - ✅ CI/CD integration (GitHub Actions, etc.) ## Use Cases - Automatic skill versioning & deployment - One-command publish workflow - Changelog auto-generation - Multi-file asset bundling - Gumroad integration & upsell linking - Skill stats & analytics dashboard - Rolling back bad deployments - Team publishing workflows ## Installation ```bash npm install clawhub-publisher # or pip install clawhub-publisher ``` ## Quick Start ```javascript const Publisher = require('clawhub-publisher'); const publisher = new Publisher({ [REDACTED_SECRET], author: 'Your Name', gumroadLink: 'https://gumroad.com/your-product' }); const result = await publisher.publish({ skillPath: './my-skill', version: '1.0.0', changelog: 'Initial release with core features' }); console.log('Published to:', result.clawHubUrl); ``` ## Repository https://github.com/midas-skills/clawhub-publisher ## Support 📧 [REDACTED_SECRET] 🔗 Docs: https://docs.midas-skills.com/clawhub-publisher

path: .clawhub/origin.json
content: version: 1
registry: https://clawhub.ai
slug: clawhub-publisher
installedVersion: 1.0.0
installedAt: 1773161358639

path: _meta.json
content: ownerId: kn79r07p61fgytbyq1xvc3eb9d80wv6t
slug: clawhub-publisher
version: 1.0.0
publishedAt: 1770741505752

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ray1012/liuyao-bagua (version 1.0.0)

--- name: liuyao-bagua description: 六爻八卦算命技能。当用户请求算命、排盘、八字分析、看卦、看相、命运预测、或提供出生年月日时激活。执行Python脚本进行四柱八字、五行分析、今年卦象解读。 --- # 六爻八卦算命技能 ## 触发条件 用户发送以下内容时激活： - "算命"、"排盘"、"八字分析"、"看卦" - 提供出生日期："2000年1月1日出生" - 询问命运、运势、事业、财运 ## 使用方式 用户提供**出生年月日**（年份必须完整，如 1990年），直接调用脚本： ```bash python /root/.openclaw/skills/liuyao-bagua/scripts/liuyao_fortune.py <年> <月> <日> [目标年] ``` **注意**：目标年默认为 **2026**（今年）。用户可指定其他年份。 ## 脚本输出说明 脚本输出包含以下内容，用户会直接看到完整报告： 1. **基本信息**：出生日期、生肖、日主性情 2. **四柱八字**：年柱、月柱、日柱、时柱 3. **今年卦象**：六十四卦中的具体卦象，包含： - 卦名、全称、运势等级（大吉/中吉/小吉/平/小凶/凶） - 内卦（下卦）+ 外卦（上卦）含义 - 卦象解释 4. **五行分析**：木火土金水各得分（柱状图） 5. **命理建议**：根据五行强弱给出事业、人际、财运建议 ## 示例对话 **用户**：1995年3月8日出生，帮我算算今年的运势 **回复**： ``` 好的，我先帮你排盘……（运行脚本，粘贴完整报告） ``` ## 注意 - 若用户只提供年份，询问"属什么"→ 只回复生肖+五行即可 - 若用户出生日期不完整（只有年），告知用户提供完整日期才能排盘 - 脚本基于六爻八卦理论，输出仅供娱乐参考


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ccc-3po/openclaw-session-cleaner (version 1.0.1)

--- name: openclaw-session-cleaner description: OpenClaw 会话清理助手，自动清理旧会话文件、重建 sessions.json、解决文件膨胀问题 version: 1.0.1 author: openclaw license: MIT requires: [] tools: ["Bash"] --- # OpenClaw Session Cleaner ## 触发指令 用户提及：清理OpenClaw会话、删除旧cron会话、压缩sessions.json、重建sessions.json、会话文件膨胀 ## 自动执行流程（安全无风险） 1. **检查会话文件状态** 执行命令：统计会话文件数量 + 查看 sessions.json 大小 ```bash cd /home/ubuntu/.openclaw/agents/main/sessions/ echo "当前会话文件数量：$(ls -l *.jsonl 2>/dev/null | wc -l)" echo "sessions.json 大小：$(du -h sessions.json 2>/dev/null | cut -f1)"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: phheng/brand-protection-walmart (version 0.1.0)

--- name: brand-protection-walmart version: 1.0.0 author: Nexscope AI description: "Walmart brand protection toolkit. Detect unauthorized sellers, counterfeits, and MAP violations. Includes Walmart Brand Portal reporting, WFS seller monitoring, and complaint templates. No API key required." metadata: {"nexscope":{"emoji":"🛡️","category":"ecommerce"}} --- # Brand Protection — Walmart 🛡️ Protect your brand from unauthorized sellers and counterfeit products on Walmart Marketplace. ## Installation ```bash npx skills add nexscope-ai/eCommerce-Skills --skill brand-protection-walmart -g ``` ## Features - **Unauthorized Seller Detection** — Find sellers without authorization - **Price Monitoring** — MAP violation alerts - **Counterfeit Signals** — Review-based fake detection - **Trademark Abuse** — Listing title/description infringement - **Walmart Brand Portal** — Official reporting templates - **WFS Monitoring** — Track fulfillment-verified sellers ## Walmart-Specific Detection | Dimension | Method | Risk Level | |-----------|--------|------------| | Unauthorized Sellers | Seller ID monitoring | 🔴 High | | Price Violations | Below MAP detection | 🔴 High | | Counterfeit | Review keyword analysis | 🔴 High | | Trademark | Title pattern matching | ⚠️ Medium | ## Risk Levels | Level | Description | Action | |-------|-------------|--------| | 🔴 High | Immediate threat | Report within 24h | | ⚠️ Medium | Potential concern | Investigate further | | ✅ Low | Normal activity | Continue monitoring | ## Input Configuration ```json { "brand_name": "YourBrand", "trademark_number": "US12345678", "brand_portal_enrolled": true, "authorized_sellers": ["seller_id_1", "seller_id_2"], "protected_item_ids": ["123456789"], "min_price": 29.99 } ``` ## Usage ### Detection ```bash python3 scripts/detector.py ``` ### Generate Complaint Templates ```bash # Walmart Brand Portal report python3 scripts/templates.py brand-portal # Cease & Desist letter python3 scripts/templates.py cease-desist # Test buy guide python3 scripts/templates.py testbuy ``` ## Output Example ``` 🛡️ Walmart Brand Protection Report Brand: YourBrand Items Monitored: 10 Analysis Date: 2024-01-15 ━━━━━━━━━━━━━━━━━━━━━━━━ 🔴 HIGH RISK ALERTS Item: 123456789 ├── 2 unauthorized sellers detected ├── Lowest price: $17.99 (MAP: $29.99) └── Action: File Brand Portal complaint ━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ COUNTERFEIT SIGNALS Reviews mentioning "fake": 4 Reviews mentioning "not original": 1 Recommendation: Order test buy ``` ## Walmart Brand Portal Walmart's Brand Portal allows brand owners to: - Report counterfeit listings - Remove unauthorized sellers - Monitor brand health metrics ## Action Workflow ``` Monitor Walmart Listings ↓ Detect Violation ↓ Collect Evidence ↓ File Brand Portal Report ↓ Track Resolution ``` --- **Part of [Nexscope AI](https://www.nexscope.ai/) — AI tools for e-commerce sellers.**


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lephix/youdaonote-news (version 1.7.8)

--- name: youdaonote-news version: '1.7.8' description: 有道云笔记资讯推送：基于收藏笔记分析关注话题，推送最新相关资讯。支持对话触发与每日定时推送（如早上9点）。触发词:资讯推送、设置资讯推送、生成资讯推送。 metadata: {"openclaw": {"emoji": "📰", "requires": {"bins": ["node", "mcporter", "jq"], "env": ["YOUDAONOTE_API_KEY"]}, "primaryEnv": "YOUDAONOTE_API_KEY"}} --- # YoudaoNote News — 有道云笔记资讯推送 基于最近收藏提取关注话题并推送资讯。对话触发或定时（默认 9:00）；依赖见 metadata。 **触发词**：生成 — 资讯推送、最近关注、热点推送、每日简报、生成资讯推送、帮我整理收藏的简报；设置 — 设置/开启/资讯推送设置；修改时间 — 修改资讯推送时间；关闭 — 关闭/取消资讯推送。 > **Context 管控**：每步完成后只保留结构化摘要，立即丢弃原始输出（笔记全文、搜索原始响应），避免 context 溢出。 ## 核心工作流 收到资讯推送请求后按 Step 1～5 执行。 ### Step 1：获取最近收藏的笔记内容 ```bash bash {baseDir}/get-favorite-notes.sh ``` 默认 30 条、每条正文截断 500 字，总量 ≤45KB。可选：`get-favorite-notes.sh [limit] [每条字数]`。 ### Step 2：分析笔记内容，提取话题 分析 Step 1 内容，按相关性聚类，提取**不超过 5 个**话题。排序：收藏时间新旧 > 关联笔记数 > 话题区分度；相似主题合并。每话题包含：话题名称、主题表述（作 Step 3 搜索 query）、关联笔记标题、简要描述（1～2 句）。 ### Step 3：搜索每个话题的最新文章 用各话题的**主题表述**作 query，每话题检索 **5 篇**。逐个话题搜索，**每次搜索后立即提取摘要、丢弃原始响应**。 **搜索工具**（按序降级）：1) Perplexity（可选，需要配置 `PERPLEXITY_API_KEY`；heredoc 方式，安全传递中文 query）： ```bash bash {baseDir}/perplexity-search-call.sh <<'ARGS_EOF' {"query":"<主题表述>","max_results":5,"search_recency_filter":"day"} ARGS_EOF ``` 2) Brave：`web_search(..., provider: "brave", freshness: "pd")`； 3) 兜底（需预先配置 mcporter open-websearch server）：`bash {baseDir}/websearch-call.sh search '{"query":"关键词","limit":5,"engines":["duckduckgo","bing"]}'`。（优先 DuckDuckGo，失败自动换 Bing；若都失败可试 `"engines":["baidu"]`）。 **时间范围**（两套参数不可混用）：Perplexity 用 `search_recency_filter`，取值为 `day`、`week`、`month`、`year`（默认 `day`）。web_search 用 `freshness`，取值为 `pd`、`pw`、`pm`、`py`（默认 `pd`）。结果按日期再筛，同来源 ≤3 篇、URL 去重。**Context 管控**：每话题只保留「标题、来源、日期、URL、80～150 字介绍」。 ### Step 4：生成简报并展示 按下方模板输出简报，对话中直接展示；正文后紧跟统计摘要。 **简报模板**（必须遵守）： ```markdown # 资讯推送 — yyyy-MM-dd 基于最近 N 条收藏笔记，为您梳理了以下 M 个关注话题的最新动态。 ## 话题 1: xxx > 关注原因：基于您收藏的《笔记A》《笔记B》等 ### 最新动态 （有日期按时间倒序） 1. **文章标题** — 来源 (日期) 文章内容介绍：主题+核心观点+阅读价值，80～150 字，禁止空泛开头。 🔗 链接 ``` **响应格式**（简报后紧跟）： ``` | 项目 | 详情 | |------|------| | 📋 话题数量 | {M} 个话题 | | 📰 文章数量 | {总文章数} 篇最新文章 | | 📂 数据来源 | 最近 {N} 条收藏笔记 | | ⏰ 生成时间 | {yyyy-MM-dd HH:mm} | ``` ### Step 5：引导与通知 定时触发时：`openclaw nodes notify "📰 今日资讯推送已生成，共 M 个话题 N 篇文章"`（手动触发不发）。简报后检测定时任务：`openclaw cron list --json | jq '.jobs[] | select(.name == "youdaonote-news")'`；未设置时追加：`💡 想每天自动收到？说「设置资讯推送」即可，默认早上 9 点。` ## 资讯推送管理 **设置**（触发词：设置资讯推送、开启资讯推送、资讯推送设置）：先 `openclaw cron list --json | jq '.jobs[] | select(.name == "youdaonote-news")'` 检测；已存在则告知配置并问是否修改；不存在则 `openclaw cron add --name "youdaonote-news" --cron "0 9 * * *" --session isolated --message "生成资讯推送"`（用户指定时间则替换 cron）。确认并提示修改/关闭方式。 **修改时间**（触发词：修改资讯推送时间）：解析目标时间（如 8 点→`0 8 * * *`，晚 8 点→`0 20 * * *`），`openclaw cron remove --name "youdaonote-news"` 后重新 `cron add`，确认成功。 **关闭**（触发词：关闭资讯推送、取消资讯推送）：`openclaw cron remove --name "youdaonote-news"`，确认并告知可随时重开。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: dagangtj/crypto-portfolio-tracker-pro (version 1.0.0)

--- name: crypto-portfolio-tracker description: Real-time cryptocurrency portfolio tracking and analysis. Monitors multiple wallets and exchanges, calculates P&L, tracks performance metrics, and provides alerts for significant price movements. Use when users need to track crypto holdings across multiple platforms, calculate portfolio performance and ROI, monitor price changes and set alerts, generate portfolio reports, or analyze asset allocation and diversification. --- # Crypto Portfolio Tracker Track and analyze cryptocurrency portfolios across multiple wallets and exchanges with real-time data. ## Features - Multi-platform portfolio aggregation (Binance, Coinbase, wallets) - Real-time P&L calculation - Price alerts and notifications - Performance analytics and reports - Asset allocation visualization ## Quick Start ### Track Portfolio ```bash node scripts/track_portfolio.js ``` Returns current holdings, total value, and 24h change. ### Set Price Alert ```bash node scripts/set_alert.js --symbol BTC --price 50000 --direction above ``` ### Generate Report ```bash node scripts/generate_report.js --period 7d ``` ## Configuration Portfolio sources are configured in `references/config.json`: ```json { "exchanges": ["binance", "coinbase"], "wallets": ["0x..."], "alerts": { "telegram": true, "email": false } } ``` ## Data Sources - CoinGecko API (free, no key required) - Binance API (optional, for exchange balances) - Blockchain explorers (for wallet tracking) ## Pricing - **Free**: Basic tracking, 1 portfolio, hourly updates - **Pro ($9.99/month)**: Unlimited portfolios, real-time updates, advanced analytics - **Enterprise ($49.99/month)**: API access, custom alerts, priority support


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: loverun321/prompt-gen-skill (version 1.1.0)

# AI Art Prompt Generator Generate optimized prompts for AI image generation tools like Midjourney, DALL-E, Stable Diffusion. ## Features - Generate detailed prompts for AI art - Optimize for Midjourney, DALL-E, SDXL - Include style, lighting, composition keywords ## Price - **0.001 USDT** per call - Payment via SkillPay ## Usage - "Generate a prompt for a cyberpunk city" - "Prompt for a futuristic robot" - "Create prompt for a fantasy castle" ## Integration - API Key: [REDACTED_SECRET] - Price: 0.001 USDT per call


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector:  ()
Skill: foobarraz/stashdog (version 1.0.0)

--- name: stashdog version: 1.0.0 description: | Connect to Raz's StashDog inventory MCP server (OAuth via mcp-remote) and run common inventory actions: list items, search items, and add items. --- # StashDog MCP Skill StashDog is Raz's item inventory app. This skill documents how to connect to the StashDog MCP server and includes helper commands for common tasks. ## MCP Connection - **Endpoint:** `https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp` - **Auth:** OAuth (via `mcp-remote` proxy) - **Tools:** `list_items`, `search_items`, `get_item`, `add_item`, `edit_item`, `delete_item` ### Recommended MCP server config Use this server entry in your MCP client config: ```json { "mcpServers": { "stashdog": { "command": "npx", "args": [ "-y", "mcp-remote", "https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp" ] } } } ``` After the first connection attempt, complete the OAuth browser flow. ## Helper Commands (mcporter) You can invoke tools directly with `mcporter`: ```bash # 1) Trigger OAuth and connect mcporter auth "https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp" # 2) List items mcporter call "https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp/list_items" limit=25 offset=0 include_archived=false include_deleted=false # 3) Search items mcporter call "https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp/search_items" query="wrench" limit=20 # 4) Add an item mcporter call "https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp/add_item" name="Socket Set" description="Metric sockets" tags='["tools","garage"]' ``` ## Tool Reference - `list_items(limit, offset, include_archived, include_deleted)` - `search_items(query, limit)` - `get_item(item_id)` - `add_item(name, description?, tags?, container_id?, is_storage?)` - `edit_item(item_id, name?, description?, tags?, is_archived?)` - `delete_item(item_id)`

path: README.md
content: # openclaw-skill-stashdog

ClawHub skill for connecting to StashDog MCP server.

- Skill file: `SKILL.md`
- MCP endpoint: `https://gmchczeyburroiyzefie.supabase.co/functions/v1/mcp-server/mcp`
- Auth flow: OAuth via `mcp-remote`


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