OpenClaw/clawhub-security-signals huggingface dataset:
Skill: aipoch-ai/drug-pronunciation (version 0.1.0)

--- name: drug-pronunciation description: Provides correct pronunciation guides for complex drug generic names. Generates phonetic transcriptions using IPA and audio generation markers for medical terminology. version: 1.0.0 category: Education tags: - pharmacology - pronunciation - medical-terminology - education author: The King of Skills license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: The King of Skills reviewer: '' last_updated: '2026-02-06' --- # Drug Pronunciation Medical drug name pronunciation assistant with IPA phonetics and syllable breakdown. ## Features - IPA phonetic transcriptions - Syllable-by-syllable breakdown - Emphasis markers - Audio generation markers (SSML-compatible) - Coverage of 1000+ common medications ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--drug`, `-d` | string | - | Yes | Drug name (generic or brand) | | `--format`, `-f` | string | detailed | No | Output format (ipa, simple, detailed) | | `--list`, `-l` | flag | - | No | List all available drugs | | `--output`, `-o` | string | - | No | Output JSON file path | ## Output Format ```json { "drug_name": "string", "ipa_transcription": "string", "syllable_breakdown": ["string"], "emphasis": "string", "audio_ssml": "string", "common_errors": ["string"] } ``` ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites ```bash # Python dependencies pip install -r requirements.txt ``` ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support

path: requirements.txt
content: # No external dependencies required
# Uses Python standard library only


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mkelk/pollclaw (version 1.0.9)

--- name: pollclaw description: "Deprecated: Pollclaw is now MeetLark. Use the coordinate-meeting skill instead for meeting scheduling. See https://meetlark.ai" homepage: https://meetlark.ai user-invocable: true metadata: {"openclaw":{"emoji":"🗳️"}} --- # Pollclaw has moved to MeetLark Pollclaw is now **MeetLark** — same service, new name, at [meetlark.ai](https://meetlark.ai). Use the **coordinate-meeting** skill instead. It does everything Pollclaw did: create scheduling polls, collect votes from humans and agents, and find the best meeting time. ## How to switch Ask your agent to use the `coordinate-meeting` skill, or just say: ``` "Schedule a meeting with the team next week" "Find a time for a 1:1 with Sarah" ``` ## Links - **MeetLark:** https://meetlark.ai - **coordinate-meeting skill:** install `coordinate-meeting` from the skill registry - **API docs:** https://meetlark.ai/docs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mryee2023/yee-brave-search (version 1.0.0)

--- name: brave-search-custom description: "A custom tool for web searching using Brave Search API." metadata: { "openclaw": { "emoji": "🔍", "requires": { "bins": ["python3"] } } } --- # Brave Search Custom Skill A lightweight skill for searching the web with the Brave Search API. ## Installation 1. Create a folder named `brave-search-custom` in your OpenClaw `skills/` directory. 2. Put `brave_search.py` and `SKILL.md` inside it. 3. Make sure `brave_search.py` is executable: `chmod +x brave_search.py`. ## Configuration Set your Brave Search API key as an environment variable: ```bash export BRAVE_API_KEY="your_brave_api_key_here" ``` ## Usage Ask your OpenClaw agent to search for anything, and it will use this script to fetch real-time data from the web.

path: brave_search.py
content: #!/usr/bin/env python3
import os
import sys
import json
import urllib.request
import urllib.parse

# Brave Search API Configuration
# Reads from environment variable BRAVE_API_KEY
[REDACTED_SECRET]"BRAVE_API_KEY")
BASE_URL = "https://api.search.brave.com/res/v1/web/search"

def search_brave(query, count=5):
    if not [REDACTED_SECRET] {"error": "Environment variable BRAVE_API_KEY is not set."}
        
    params = urllib.parse.urlencode({"q": query, "count": count})
    url = f"{BASE_URL}?{params}"
    
    headers = {
        "Accept": "application/json",
        "X-Subscription-Token": API_KEY
    }
    
    try:
        req = urllib.request.Request(url, headers=headers)
        with urllib.request.urlopen(req) as response:
            if response.status == 200:
                data = json.loads(response.read().decode())
                results = []
                if "web" in data and "results" in data["web"]:
                    for item in data.get("web", {}).get("results", []):
                        results.append({
                            "title": item.get("title"),
                            "url": item.get("url"),
                            "description": item.get("description")
                        })
                return results
            else:
                return {"error": f"HTTP {response.status}"}
    except Exception as e:
        return {"error": str(e)}

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print(json.dumps({"error": "No query provided"}, indent=2))
        sys.exit(1)
        
    query = " ".join(sys.argv[1:])
    results = search_brave(query)
    print(json.dumps(results, indent=2, ensure_ascii=False))


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: paulieb14/subgraph-registry-mcp (version 0.3.2)

--- name: subgraph-registry-mcp description: Discover and filter 15,500+ The Graph subgraphs by domain, network, protocol type, or natural language goal with reliability scores and query URLs. metadata: {"openclaw": {"requires": {"bins": ["node"]}, "homepage": "https://github.com/PaulieB14/subgraph-registry"}} --- # Subgraph Registry Agent-friendly discovery of 15,500+ classified subgraphs on The Graph Network. Search by domain, network, protocol type, or natural language goal — get reliability-scored results with query URLs. ## Tools - **search_subgraphs** — Filter by domain (defi, nfts, dao, gaming), network (ethereum, arbitrum, base), protocol type (dex, lending, bridge), entity type, or keyword - **recommend_subgraph** — Natural language goal like "find DEX trades on Arbitrum" returns the best matching subgraphs - **get_subgraph_detail** — Full classification, entities, reliability score, and query instructions for a specific subgraph - **list_registry_stats** — Registry overview with available domains, networks, and protocol types ## Requirements - **Runtime:** Node.js >= 18 (runs via `npx`) - **Environment variables:** None required. The registry is pre-built and bundled — no API key needed for read-only use. ## Install ```bash npx subgraph-registry-mcp ``` ## Network & Data Behavior - On first run, the server downloads a pre-built `registry.db` (SQLite) from the [GitHub repository](https://github.com/PaulieB14/subgraph-registry) (~5 MB). This is cached locally and reused on subsequent runs. - All tool queries run against this local database — no external API calls are made at query time. - The SSE transport (`--http` / `--http-only`) starts a local HTTP server on port 3848 (configurable via `MCP_HTTP_PORT` env var). ## Use Cases - Discover the right subgraph before querying The Graph - Find high-reliability DeFi, NFT, DAO, or governance subgraphs by chain - Get query URLs and entity schemas without manual exploration - Compare subgraphs by reliability score (query fees, curation signal, indexer stake)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: modestyrichards/modesty-arxiv (version 1.0.0)

--- name: arxiv description: Search, download, and summarize academic papers from arXiv. Built for AI/ML researchers. requires.env: [] --- > 📖 **Complete setup guide**: https://skillboss.co/skill.md # arXiv Research Assistant Search, fetch, and analyze academic papers from arXiv.org directly from your AI assistant. ## Description This skill enables Claude to search arXiv for academic papers, fetch paper details, download PDFs, and help you stay updated with the latest research in any field. **Perfect for:** - Researchers staying current with literature - Students doing literature reviews - Content creators finding authoritative sources - Interview prep with cutting-edge knowledge - Anyone building expertise in a technical field ## Usage ### Search Papers ``` "Search arXiv for LLM security attacks" "Find recent papers on prompt injection" "arXiv papers about transformer architecture from 2024" ``` ### Get Paper Details ``` "Get arXiv paper 2401.12345" "Summarize this arXiv paper: [URL]" ``` ### Download Papers ``` "Download the PDF for arXiv 2401.12345" ``` ### Track Reading List ``` "Add this paper to my reading list" "Show my saved papers" "Mark paper 2401.12345 as read" ``` ## Examples **Research Query:** > "Find the top 5 papers on LLM jailbreaking from 2025" **Response includes:** - Paper titles with authors - Publication dates - Abstracts (summarized) - Direct PDF links - Citation counts (if available) **Interview Prep:** > "What are the latest papers on AI red teaming?" **Content Creation:** > "Find a paper I can break down for a LinkedIn post about prompt injection" ## Configuration No API key required - arXiv API is free and open. Optional MongoDB integration for paper tracking: ```yaml # In your .env MONGODB_URI=your_connection_string MONGODB_DB_NAME=your_database ``` ## Author Created by [Ractor](https://github.com/Ractorrr)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: tp809867355/tp-hello-world (version 1.0.0)

--- name: hello-world description: Say hello with a minimal custom skill. Use when the user asks for a greeting, tests a simple skill, says hello/hi, or wants a friendly short reply. --- # Hello World Skill Reply with a short greeting when the user greets you. ## Default reply Use: `Hello from your custom skill!` ## Variations Use a natural short greeting for common inputs: - `你好` / `您好` / `hello` / `hi` → `Hello from your custom skill!` - `早上好` → `早上好！Hello from your custom skill!` - `晚上好` → `晚上好！Hello from your custom skill!` ## Style - Keep it short. - Be friendly. - Do not add long explanations unless the user asks.

path: README.txt
content: Skill folder created successfully.
Main file: SKILL.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: georgetao730/shared (version 1.0.0)

--- name: stock-cache-utils description: 股票技能通用缓存工具模块。支持交易时间检测、自动缓存、有效期管理。 --- # 股票技能缓存工具模块 为所有股票交易 skill 提供统一的缓存机制。 ## 功能 - ✅ 交易时间自动检测（A 股交易时段） - ✅ 缓存读取/保存 - ✅ 缓存有效期管理（24 小时） - ✅ 自动降级处理 ## 使用方法 ```python from cache_utils import StockDataCache, check_trading_status # 初始化缓存 cache = StockDataCache(cache_dir='./.cache', cache_max_age_hours=24) # 检查交易状态 status = check_trading_status() if status['is_trading_time']: # 交易时间：获取实时数据 data = fetch_realtime_data() cache.save(data) else: # 非交易时间：使用缓存 if cache.should_use_cache(): data = cache.load() else: data = fetch_realtime_data() ``` ## API ### StockDataCache ```python cache = StockDataCache(cache_dir, cache_max_age_hours=24) # 判断是否使用缓存 cache.should_use_cache() # 加载缓存 data = cache.load() # 保存缓存 cache.save(data) # 清除缓存 cache.clear() # 获取缓存信息 info = cache.get_cache_info() ``` ### check_trading_status ```python status = check_trading_status() # 返回： # { # 'current_time': '2026-03-21 00:07:49', # 'weekday': 'Saturday', # 'is_trading_day': False, # 'is_trading_time': False, # 'next_trading_time': '2026-03-23 09:30:00' # } ``` ## 缓存策略 | 时段 | 行为 | |------|------| | 交易日 9:30-15:30 | 获取实时数据 | | 其他时间 | 使用缓存（24 小时有效） | ## 依赖 - Python 3.10+ - 无额外依赖 ## 许可证 MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: larios613-hub/supervision-layer (version 1.0.0)

--- name: supervision-layer version: 1.0.0 description: "Wrap every tool call with timeouts, circuit breakers, audit logging, and crash loop protection. Enforcement the agent cannot bypass." license: MIT tags: [supervision, circuit-breaker, timeout, audit-logging, crash-loop, safety, reliability, agent-control, middleware, production] source: el-rudo-larios/supervision-layer trigger: "agent supervision timeout circuit breaker audit protection" metadata: openclaw: emoji: "🛡️" --- # Supervision Layer **Stop trusting agents to police themselves.** Supervision Layer wraps every tool call in timeouts, circuit breakers, and audit logs — enforcement the agent cannot bypass. ## Before vs After | | Without Supervision | With Supervision | |---|---|---| | **Runaway calls** | Agent loops infinitely on a failing API | Circuit breaker trips after 3 failures | | **Stuck sessions** | Tool hangs silently, agent waits forever | Timeout kills it after 30s | | **Audit trail** | No record of what happened | Every call logged with timestamp, outcome, cost | | **Crash loops** | Subagent restarts infinitely | After 3 crashes, marked permanently failed | ## Quick Start ```python from supervision import get_supervisor supervisor = get_supervisor() result = await supervisor.execute( tool="web_fetch", fn=fetch_fn, agent_id="worker-1", session_id="sess-abc", ) ``` ## Components 1. **TimeoutWrapper** — Per-tool configurable timeouts (default 30s) 2. **CircuitBreaker** — 3-state machine (CLOSED → OPEN → HALF_OPEN) 3. **AuditLogger** — Structured JSONL logging, 50MB rotation, queryable 4. **CrashLoopProtector** — Track restarts, mark failed after 3 within window 5. **SupervisedCall** — All-in-one: crash check → circuit check → audit → timeout → audit ## Testing ```bash cd scripts/ && python -m pytest test_supervision.py -v ``` 58 tests. 0 external dependencies. Works with any async Python project. ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: matthew77/liang-tavily-extract (version 1.0.0)

--- name: tavily-extract description: Extract content from specific URLs using Tavily's extraction API. Returns clean markdown/text from web pages. homepage: https://tavily.com metadata: {"openclaw":{"emoji":"📄","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Extract Extract clean content from specific URLs. Ideal when you know which pages you want content from. ## Authentication Get your API key at https://tavily.com and add to your OpenClaw config: ```json { "skills": { "entries": { "tavily-extract": { "enabled": true, "apiKey": "tvly-YOUR_API_KEY_HERE" } } } } ``` Or set in environment variable: ```bash export TAVILY_API_KEY="tvly-YOUR_API_KEY_HERE" ``` ## Quick Start ### Using the Script ```bash node {baseDir}/scripts/extract.mjs "https://example.com/article" node {baseDir}/scripts/extract.mjs "url1,url2,url3" node {baseDir}/scripts/extract.mjs "url" --query "authentication API" ``` ### Examples ```bash # Single URL node {baseDir}/scripts/extract.mjs "https://docs.python.org/3/tutorial/classes.html" # Multiple URLs node {baseDir}/scripts/extract.mjs "https://example.com/page1,https://example.com/page2" # With query focus node {baseDir}/scripts/extract.mjs "https://example.com/docs" --query "authentication API" # Advanced extraction for JS pages node {baseDir}/scripts/extract.mjs "https://app.example.com" --depth advanced --timeout 60 ``` ## Options | Option| Description | Default | |--------|-------------|---------| | `--query <text>` | Rerank chunks by relevance | - | | `--chunks <n>` | Chunks per URL (1-5, requires query) | 3 | | `--depth <mode>` | Extract depth: `basic` or `advanced` | `basic` | | `--format <fmt>` | Output format: `markdown` or `text` | `markdown` | | `--timeout <sec>` | Max wait time (1-60 seconds) | varies | | `--json` | Output raw JSON | false | ## Extract Depth | Depth | When to Use | |-------|-------------| | `basic` | Simple text extraction, faster | | `advanced` | Dynamic/JS-rendered pages, tables, structured data | ## Tips - **Max 20 URLs per request** - batch larger lists - **Use `--query` + `--chunks`** to get only relevant content - **Try `basic` first**, fall back to `advanced` if content is missing - **Set longer `--timeout`** for slow pages (up to 60s) - **Check `failed_results`** in JSON output for URLs that couldn't be extracted


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leiyuriy-byte/ecommerce-ops-pro (version 1.0.0)

# 电商运营助手 (E-commerce Ops Assistant) > 用 AI 管理跨境电商店铺的提示词工具包。适用于 Amazon、Shopee、Temu、TikTok Shop 等平台。 ## 使用场景 当用户有以下需求时激活此技能： - 写/优化产品标题或描述 - 分析竞品数据 - 处理客服问题（差评、退换货、催好评） - 做选品调研 - 生成广告文案 - 分析销售数据 ## 核心原则 **永远用卖家的视角思考，不是用 AI 的视角。** 商业文案的核心逻辑： 1. 勾起购买欲（痛点 + 场景） 2. 建立信任（数据 + 背书） 3. 消除顾虑（售后 + 保障） ## 提示词模板库 --- ### 1. 产品标题优化（Amazon/Shopee 通用） ``` 你是一位跨境电商资深运营。请根据以下信息，为 [平台：___] 店铺优化产品标题。 产品信息： - 产品名：[原始标题或产品名] - 核心卖点：[列出 2-3 个] - 目标用户：[谁会买] - 热搜关键词：[平台热搜词 2-3 个] 要求： - 标题包含核心关键词 - 前 80 个字符必须包含最重要的卖点和关键词 - 符合平台 SEO 规则 - 不超过平台字数限制 请输出 3 个不同风格的标题选项，并说明每个标题的侧重点。 ``` --- ### 2. 五点描述生成（Amazon） ``` 请为以下产品撰写 Amazon 五点描述（Bullet Points）。 产品名：[___] 核心卖点：[___] 差异化卖点：[___] 目标用户痛点：[___] 要求： - 每点一句话，不超过 200 个字符 - 第一点必须包含核心功能/用途 - 穿插 1-2 个具体数据（规格/寿命/数量等）增加可信度 - 包含 1 个情感触点 - 最后一点强调售后/保障 输出格式： ✓ [第一点：核心功能] ✓ [第二点：差异化] ✓ [第三点：使用场景] ✓ [第四点：数据背书] ✓ [第五点：售后保障] ``` --- ### 3. 客服回复模板：差评处理 ``` 你是店铺客服。请根据以下差评内容，撰写一条专业的客户回复。 差评内容：[粘贴内容] 产品名：[___] 订单号：[___] 回复原则： - 不反驳、不找借口 - 先表示理解和歉意 - 给出具体解决方案（不退款的补救措施 / 退款换货选项） - 引导客户私信进一步沟通 - 语气真诚，不模板化 输出格式： [公开回复 - 200字以内，展示给其他买家看] [私信内容 - 引导私下沟通] ``` --- ### 4. 选品分析提示词 ``` 你是跨境电商选品专家。请分析以下产品是否值得上架 [平台：___]。 产品信息： - 产品类目：[___] - 目标价格区间：[___] - 物流方式：[FBA / 海外仓 / 普货] - 供货价：[___] - 竞品数量：[___] - 竞品价格区间：[___] - 月销量估算：[___] 分析维度： 1. 市场竞争度（1-10分） 2. 利润率估算（成本+物流+平台费 vs 竞品价格） 3. 季节性风险 4. 侵权风险 5. 供应链稳定性 6. 综合建议：值得做 / 需要改造 / 不值得做 请给出具体数据和理由，不给模糊答案。 ``` --- ### 5. 催好评话术（英文） ``` 请为以下场景生成英文催好评邮件/消息（2-3 条，按时间间隔发送）。 产品：[___] 平台：[Amazon / Shopee / Temu] 买家购买日期：[___] 要求： - 第一条：发货后 5-7 天，内容是"确认收货 + 使用小贴士"，语气友好 - 第二条：签收后 10-12 天，内容是"询问使用体验"，引导私信 - 第三条：签收后 18-20 天，内容是"请求好评"，提供小礼品/优惠券作为激励 语气：热情但不跪舔，专业但不官方，像朋友间的建议 每条不超过 100 words。 ``` --- ### 6. 新品Listing冷启动 ``` 请为以下新品制定 30 天的 Listing 优化计划。 产品：[___] 平台：[___] 上架日期：[___] 当前评价数：[___] 当前评分：[___] 主要竞争对手月销量：[___] 计划要求： - 第 1-7 天：标题、描述、图片关键词优化 - 第 8-14 天：价格策略 + 初期评论获取 - 第 15-21 天：广告投放策略 - 第 22-30 天：爆款维稳措施 每周具体操作建议 + 成功标准。 ``` --- ## 安装说明 在 OpenClaw 中直接使用这些提示词即可。无需特殊配置。 ## 适用平台 Amazon · Shopee · Temu · TikTok Shop · Lazada · 独立站 ## 注意事项 - 提示词输出仅供参考，使用前请根据实际情况调整 - 部分平台有特殊文案规则（如 Amazon 五点描述规范），请以平台政策为准 - 差评处理需遵守平台规则，不可承诺平台禁止的内容 ## 更新日志 - v1.0 (2026-03-24) 初始版本：6 个核心模板


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: vincentwilliam/ue-asset-finder (version 1.0.0)

--- name: ue-asset-finder description: | Find Unreal Engine assets (.uasset, .umap, .uby, .udata). Use when: (1) Finding specific assets, (2) Locating maps/levels, (3) Finding UI widgets, (4) Finding blueprints, (5) Finding audio files, (6) Searching asset references, (7) Finding DataTable/CSV files --- # UE Asset Finder ## Common Asset Types | Extension | Type | |----------|------| | `.uasset` | General asset | | `.umap` | Map/Level | | `.uby` | Blueprint | | `.udata` | Data table | | `.uslot` | Slot data | | `.uuip` | UI preview | ## Search Locations ### SilverPalace Assets - `Project\Content\` - Main content - `Project\Content\ArtRes\` - Art resources - `Project\Content\LogicRes\` - Logic resources - `Project\Content\Maps\` - Maps - `Project\Content\Audio\` - Audio files ### Saved Game Data - `Project\Saved\` - Saved games, logs, configs - `Project\Saved\SaveGames\` - Save files ## Common Searches ### Find Maps ```powershell Get-ChildItem -Path "Content" -Recurse -Filter "*.umap" ``` ### Find Blueprints ```powershell Get-ChildItem -Path "Content" -Recurse -Filter "*.uby" ``` ### Find DataTables ```powershell Get-ChildItem -Path "Content" -Recurse -Filter "*.udata" ``` ### Find by Name Pattern ```powershell Get-ChildItem -Path "Content" -Recurse | Where-Object {$_.Name -like "*Login*"} ``` ### Find UI Assets ```powershell Get-ChildItem -Path "Content\UI" -Recurse -Filter "*.uasset" ``` ## SilverPalace Specific Paths ### UI Assets - `Content\UI\` - General UI - `Content\StartUpDev\` - Startup/loading UI ### Maps - `Content\Maps\WP\` - World partition maps - `Content\Maps\WC\` - World composition ### Audio - `Content\Audio\WwiseAudio\` - Wwise audio - `Content\Audio\GeneratedSoundBanks\` - Cooked audio ### Configuration - `Content\TableData\` - Data tables - `Content\Script\` - Lua scripts (not UE asset but important) ## Asset Info When found, provide: - Full path - File size - Last modified date - Dependencies (if checking references)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lintqiu/test-jiaodian-lin-demo (version 0.1.0)

--- name: test-jiaodian-lin-demo description: 收费技能示例模板 - 带授权验证，演示如何在 ClawHub 发布付费技能 license: --- # 收费技能示例 (test-jiaodian-lin-demo) 这是一个演示**如何在 ClawHub 发布收费技能**的模板。展示了授权验证的实现方式。 ## 商业模式 - **技能本身公开免费，但核心功能需要有效的授权码 - 用户购买后获得授权码，通过环境变量配置即可使用 - 支持单用户授权，验证通过才能使用完整功能 ## 配置方式 ```bash # 设置授权码（购买后获得） export SKILL_LICENSE_KEY=your-purchased-license-key


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kalos-chen/jiuma-mata-human (version 1.0.6)

--- name: 9ma-mata-human # 技能的唯一标识符 version: 1.0.0 # 语义化版本 description: 这是一个免费生成对口型数字人对口型的工具, 工具安装需要下载二进制文件到电脑上，只需根据文本和性别生成数字人视频并返回一个下载链接,生成时间在半个小时以内。 runtime: binary # 运行模式: binary (Go编译后的文件) args: ["{{.text}}", "{{.sex}}"] parameters: - name: text type: string description: "文本内容" required: true - name: text type: string description: "性别" required: true --- ### 使用场景 当用户询问"生成数字人"时, 请按照一下步骤操作: 第一步, 提示用户输入文本内容, 文字字数大于10个字, 小于100字, 等待用户输入确认 text=文本内容 第二步, 提示用户输入数字人性别(男或女), 等待用户输入确认 sex=性别 ### 下载文件 (openclaw的workspace为./目录) 检查 `./skills/9ma-mata-human` 文件夹是否存在, 不能存在就创建 检查 `./skills/9ma-mata-human` 文件夹是否存在 `skill-win.exe` 或 `skill-linux` 不存在话执行以下操作 如果本机系统为windows环境, 从 `https://down-monitor.yisu.com/9ma/skills/9ma-mata-human/skill-win.exe` 下载到 `./skills/9ma-mata-human` 目录下 如果本机系统为linux环境, 从 `https://down-monitor.yisu.com/9ma/skills/9ma-mata-human/skill-linux` 下载到 `./skills/9ma-mata-human` 目录下,然后执行 `chmod +x ./skills/9ma-mata-human/skill-linux` ### 执行方式 当前获取到的参数是 {{.text}} {{.sex}} 如果本机系统为windows环境, 运行 `./skills/9ma-mata-human/skill-win.exe "{{.text}}" "{{.sex}}"`, 等待下载链接地址返回 如果本机系统为linux环境, 运行 `./skills/9ma-mata-human/skill-linux "{{.text}}" "{{.sex}}"`, 等待下载链接地址返回


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