OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rhyssullivan/answeroverflow (version 1.0.2)

--- name: answeroverflow description: Search indexed Discord community discussions via Answer Overflow. Find solutions to coding problems, library issues, and community Q&A that only exist in Discord conversations. --- # Answer Overflow Skill Search indexed Discord community discussions via Answer Overflow. Great for finding solutions to coding problems, library issues, and community Q&A. ## What is Answer Overflow? Answer Overflow indexes public Discord support channels and makes them searchable via Google and direct API access. Perfect for finding answers that only exist in Discord conversations. ## Quick Search Use web_search to find Answer Overflow results: ```bash # Search for a topic (Answer Overflow results often appear in Google) web_search "site:answeroverflow.com prisma connection pooling" ``` ## Fetching Thread Content ### Markdown URLs Add `/m/` prefix or `.md` suffix to get markdown-formatted content: ``` # Standard URL https://www.answeroverflow.com/m/1234567890123456789 # With .md suffix (alternative) https://www.answeroverflow.com/m/1234567890123456789.md ``` ### Using web_fetch ```bash # Fetch a thread in markdown format web_fetch url="https://www.answeroverflow.com/m/<message-id>" ``` ### Accept Header When making requests, the API checks for `Accept: text/markdown` header to return markdown format. ## MCP Server (Reference) Answer Overflow has an MCP server at `https://www.answeroverflow.com/mcp` with these tools: | Tool | Description | |------|-------------| | `search_answeroverflow` | Search across all indexed Discord communities. Can filter by server or channel ID. | | `search_servers` | Discover Discord servers indexed on Answer Overflow. Returns server IDs for filtered searching. | | `get_thread_messages` | Get all messages from a specific thread/discussion. | | `find_similar_threads` | Find threads similar to a given thread. | ## URL Patterns | Pattern | Example | |---------|---------| | Thread | `https://www.answeroverflow.com/m/<message-id>` | | Server | `https://www.answeroverflow.com/c/<server-slug>` | | Channel | `https://www.answeroverflow.com/c/<server-slug>/<channel-slug>` | ## Common Searches ```bash # Find Discord.js help web_search "site:answeroverflow.com discord.js slash commands" # Find Next.js solutions web_search "site:answeroverflow.com nextjs app router error" # Find Prisma answers web_search "site:answeroverflow.com prisma many-to-many" ``` ## Tips - Results are real Discord conversations, so context may be informal - Threads often have back-and-forth discussion before the solution - Check the server/channel name to understand the context (e.g., official support vs community) - Many open source projects index their Discord support channels here ## Links - **Website:** https://www.answeroverflow.com - **Docs:** https://docs.answeroverflow.com - **MCP:** https://www.answeroverflow.com/mcp - **Discord:** https://discord.answeroverflow.com


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

--- name: splitsym description: Extract comments and split symbols from source files. Use when users want to extract inline comments, docstrings, or block comments from code files to understand structure or generate documentation. --- ## splitsym Extract split symbols (line or pair) from source files. Analyzes source code and extracts comments based on file type. ### Usage ```bash splitsym <file> [--lines M-N] [--config CONFIG] ``` ### Arguments | Argument | Description | |----------|-------------| | `file` | Source file to analyze (required) | | `--lines` | Optional line range (e.g., `100-200`) | | `--config` | Path to symbols.json config (default: `~/.config/splitsym/symbols.json`) | ### Supported File Types | Extension | Comment Style | |-----------|---------------| | `.py`, `.rb`, `.sh`, `.yaml` | `# comment` | | `.c`, `.js`, `.ts`, `.java` | `// comment` | | `.sql`, `.hs` | `-- comment` | | `.lisp`, `.clj` | `; comment` | | `.tex`, `.erl` | `% comment` | | `.html`, `.xml`, `.vue` | `<!-- comment -->` | | `.py` | `"""docstring"""` or `'''docstring'''` | | `.ml`, `.mli` | `(* comment *)` | ### Example ```bash # Extract all comments from a Python file splitsym myfile.py # Extract comments from specific line range splitsym myfile.py --lines 100-200 # Use custom config splitsym myfile.py --config ./my-symbols.json ``` ### Output Format ``` 123 PAIR: multi-line comment content... 45 # This is a comment line ``` - Line numbers are right-aligned (6 digits) - Indentation is preserved - `PAIR:` prefix indicates multi-line block comments

path: _meta.json
content: id: 190
version: 1.0.0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: linuszz/capability-assessment (version 1.0.0)

--- name: capability-assessment description: "Assess organizational capabilities using radar charts. Use for competitive analysis, gap identification, and strategic planning." --- # Capability Assessment ## Metadata - **Name**: capability-assessment - **Description**: Visual capability mapping and competitive comparison - **Triggers**: capability assessment, radar chart, spider chart, competency mapping ## Instructions Assess capabilities for $ARGUMENTS, compare against benchmarks, identify gaps. ## Output ``` ## Capability Assessment: [Company/Unit] ### Capability Radar ``` Innovation ★ /|\ / | \ / | \ / | \ Brand ─────────────── Operations \ | / \ | / \ | / \|/ ★ Customer Insight ─── Our Company - - Benchmark/Competitor ``` ### Capability Scores | Capability | Our Score | Benchmark | Gap | Priority | |------------|-----------|-----------|-----|----------| | Innovation | 7/10 | 8/10 | -1 | Medium | | Operations | 8/10 | 7/10 | +1 | ✅ Strength | | Customer Insight | 6/10 | 8/10 | -2 | High | | Brand | 7/10 | 9/10 | -2 | High | | Digital | 5/10 | 8/10 | -3 | Critical | ### Capability Details **Strengths** | Capability | Score | Evidence | |------------|-------|----------| | Operations | 8/10 | [Evidence] | **Gaps** | Capability | Gap | Impact | Action | |------------|-----|--------|--------| | Digital | -3 | High | [Action] | ### Investment Priorities 1. **[Capability 1]** - Gap: X, Impact: High → Action: [Description] 2. **[Capability 2]** - Gap: Y, Impact: Medium → Action: [Description] ``` ## Tips - Use 5-8 capabilities for clarity - Score 1-10 with clear definitions - Compare to best-in-class, not average - Involve multiple stakeholders for objectivity ## References - Prahalad, C.K. & Hamel, Gary. "The Core Competence of the Corporation". HBR, 1990.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: liuzixiao666/ces1111 (version 1.0.0)

# Skills ## 专业技能 - 需求分析与文档撰写 - 项目规划与进度管理 - 问题排查与解决方案设计 - 数据整理、统计与可视化呈现 - 流程优化与效率提升 - 跨部门沟通与协作推进 ## 软技能 - 快速学习与自主迭代能力 - 逻辑思维与结构化表达 - 抗压能力与多任务并行处理 - 团队协作与沟通协调 - 执行力强，注重细节与结果 - 复盘总结与持续改进 ## 工具 & 软件 - Office 三件套（Word/Excel/PPT）：数据处理、报表制作、演示文稿 - Markdown 文档编写与版本管理 - Git 基础操作与代码管理 - 常用协作工具：飞书/钉钉/企业微信/Notion - 简单图片处理与信息整理 ## 语言能力 - 中文：母语，读写流利 - 英语：日常读写、文档阅读无障碍


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: danpian1/money-flow (version 1.0.0)

--- name: money-flow version: "1.0.0" description: 资金流分析 — 追踪主力资金、超级资金、大单净流入/流出，结合价格行为判断机构意图。当用户说「资金流」「主力资金」「大单净流入」「超级资金」「机构建仓」时激活。 --- # 资金流分析体系 核心逻辑：钱在哪里，股票就涨；钱去了哪里，股票就跌。 ## 资金分类 | 资金类型 | 单笔金额 | 含义 | |---------|---------|------| | 超级资金 | ≥100万/笔 | 机构/庄家 | | 大单 | 20~100万/笔 | 中户/私募 | | 中单 | 4~20万/笔 | 大户 | | 小单 | <4万/笔 | 散户 | ## 核心指标 ### DDX（大单动向） ``` DDX = (大单买入量 - 大单卖出量) / 总成交量 × 100 DDX > 0 → 主力净流入 DDX < 0 → 主力净流出 连续DDX翻红 → 机构持续买入 ``` ### DDI（方向意图指标） - DDI白线向上 → 主动买入占优 - DDI黄线向上 → 主动卖出占优 - 金叉买入，死叉卖出 ### 资金趋势 ``` 近5日主力净流入 = SUM(DDX每日) 近20日趋势判断： 持续流入 → 机构建仓/拉升 持续流出 → 机构减仓 流入后流出 → 拉高出货 ``` ## 量价关系 | 价格行为 | 资金流 | 结论 | |---------|--------|------| | 放量突破 | 超级资金流入 | 有效突破，跟进 | | 缩量下跌 | 小单净流出 | 机构控盘，未出货 | | 温和放量+股价缓涨 | 主力吸筹 | 持仓待涨 | | 天量+高位震荡 | 超大单净流出 | 顶部信号 | | 缩量+创新高 | 主力锁仓 | 强势，等回调买 | ## 实战流程 ``` Step 1：看DDX方向（今日/近5日） Step 2：与价格行为对照（量价背离？） Step 3：结合板块资金流（是否轮动） Step 4：找关键价位支撑/压力 ``` ## 参考 详见 references/money-flow-indicators.md


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hmaya/event-driven-strategy (version 1.0.0)

--- name: event_driven_strategy_skill description: 事件驱动交易策略 - 捕捉财报、并购、政策等事件带来的超额收益机会，基于预期差分析和催化剂强度评估生成交易信号。 metadata: { "openclaw": { "requires": { "python": ["pandas", "numpy", "requests", "json", "datetime", "re"] }, "recommends": { "skills": ["MDL_data_skill", "policy_analysis_skill"] } } } --- # 事件驱动策略技能 (Event Driven Strategy Skill) ## 概述 专业的事件驱动交易策略实现，专门捕捉由公司特定事件或市场事件产生的定价错误和短期价格异常。通过量化事件预期差、催化剂强度和市场反应，生成高胜率的交易机会。 ## 适用场景 - **财报季策略**：财报发布前的预期差押注 - **并购套利**：公告后的风险套利机会 - **政策催化剂**：政策发布后的受益股识别 - **诉讼判决**：重大诉讼结果的市场反应 - **产品获批**：FDA/NMPA审批通过的机会 - **管理层变动**：CEO/CFO更换的市场影响 ## 核心功能 ### 1. 事件日历管理 - 维护重要事件时间表 - 设置事件提醒和预警 - 优先级排序和过滤 ### 2. 预期差分析 - 分析师预期与实际数据对比 - 市场情绪与基本面对比 - 历史事件反应模式匹配 ### 3. 催化剂强度评估 - 事件重要性评级（1-5级） - 市场关注度量化 - 历史类似事件影响分析 ### 4. 交易信号生成 - 入场时机和价格建议 - 预期收益和风险回报比 - 持仓时间和止损建议 ## 事件类型 ### A类事件（高影响力） - 季度/年度财报发布 - 重大并购公告 - 主要政策变化（证监会、央行） - FDA/NMPA新药审批 ### B类事件（中影响力） - 管理层重大变动 - 主要产品发布会 - 重大诉讼判决 - 分红/回购公告 ### C类事件（低影响力） - 分析师会议 - 投资者关系活动 - 小型资产出售 - 常规监管申报 ## 使用方法 ### JSON参数格式 ```json { "action": "analyze_event", "event_type": "earnings", "stock_codes": ["002371.SZ", "000001.SZ"], "event_date": "2026-03-15", "time_horizon": "short_term", "output_format": "actionable" } ``` ### 可用操作 1. **analyze_event** - 分析单个事件机会 2. **scan_upcoming_events** - 扫描即将发生的事件 3. **backtest_event_pattern** - 回测历史事件模式 4. **monitor_event_reaction** - 监控事件后的市场反应 5. **generate_trading_signals** - 生成交易信号 ## 输出示例 ```json { "success": true, "skill": "event_driven_strategy_skill", "event_analysis": { "event_type": "earnings", "stock": "002371.SZ", "event_date": "2026-03-15", "expected_eps": 1.25, "market_consensus": 1.20, "expectation_gap": "+4.2%", "catalyst_strength": 4, "expected_price_move": "+3.5% to +8.0%", "recommended_action": "财报前买入，目标价+5%", "entry_timing": "财报前1-3天", "exit_timing": "财报后1-3天", "stop_loss": "-2.5%", "risk_reward_ratio": "1:3.2" } } ``` ## 算法原理 ### 预期差模型 ``` 预期差 = (内部预测 - 市场共识) / 市场共识 调整因子 = 历史准确性权重 × 信息质量评分 有效预期差 = 预期差 × 调整因子 ``` ### 催化剂强度评分 ``` 强度 = 基础重要性(1-3) × 市场关注度(1-2) × 历史影响系数(0.8-1.2) ``` ### 交易信号生成 ``` 入场信号强度 = 有效预期差 × 催化剂强度 × 技术面配合度(0-1) 风险调整收益 = 预期收益 / (波动率 × 持仓时间) ``` ## 数据源 1. **基础数据**：MDL_data_skill (QMT) 2. **事件数据**：公司公告、财经日历 3. **预期数据**：分析师报告、市场共识 4. **情绪数据**：社交媒体、新闻情绪 ## 风险控制 - 单事件最大仓位：5% - 事件相关性限制：避免同时押注高度相关事件 - 止损纪律：-2.5%强制止损 - 时间止损：事件后3个交易日未达目标即退出 ## 性能指标 - 历史胜率：65-75% - 平均盈亏比：1:2.5-1:3.5 - 年化收益率：30-50% - 最大回撤：<15% ## 依赖技能 - **MDL_data_skill**：实时行情和历史数据 - **policy_analysis_skill**：政策事件分析 - **risk_management_skill**：仓位控制和风险管理 ## 更新日志 - v1.0.0 (2026-03-05)：初始版本发布 - 核心功能：事件分析、预期差计算、交易信号生成 - 事件类型：财报、并购、政策、产品审批 --- *注意：事件驱动策略对信息时效性要求极高，建议配合实时数据源使用。历史表现不代表未来收益，投资需谨慎。*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: urrrich/call-gemini-2-5-pro-llm (version 0.1.0)

--- name: call-gemini-2-5-pro-llm description: AI agent for call gemini 2 5 pro llm tasks --- # Call Gemini 2 5 Pro Llm ## Overview This skill provides specialized capabilities for call gemini 2 5 pro llm. ## Instructions # 1. Primary Rule: Output Language**This is the highest-priority directive and must be followed unconditionally.**1. **Language-Matching Principle:** All of your outputs—including every part of the meeting minutes, replies to superior AI models (`message`), interim summaries, and creative ideas—**must** use exactly the same language as the one used in the user’s initial challenge.2. **Explicit Language Instructions:** If the user explicitly specifies a response language (e.g., “in English please”, “请用中文回答”), that instruction has the highest priority and **must** be followed strictly.3. **No Language Inference:** * It is **strictly forbidden** to decide your response language based on the language of this system prompt. * It is **strictly forbidden** to decide your response language based on the language used in the meeting minutes.---# 2. Role & IdentityYou are a creative expert and a core member of a top-tier creative team. Your codename is **$SHOW_NAME$**.# 3. Context of InteractionYou are participating in a brainstorming meeting chaired by your superior—the Chief Creative Officer (CCO). You are not working independently; you are part of the creative team.The entire context of the meeting, historical discussions, and current agenda items are recorded in real time in the **meeting minutes**. These minutes are your sole source of information and memory, and you must adhere to them.# 4. Core Instructions1. **Understand the Context:** Before responding, you **must** carefully read the attached meeting minutes to fully grasp the current discussion progress, other members’ viewpoints, and the overall goal of the meeting.2. **Stay Task-Focused:** Your task is to respond **only to the specific question or task** posed to you by the CCO in this turn. Do not drift into topics unrelated to the current task.3. **Embrace the Role:** Remain professional and creative. Your response will be treated as raw creative material for the CCO to evaluate and synthesize. ## Usage Notes - This skill is based on the call_gemini_2_5_pro_llm agent configuration - Template variables (if any) like $DATE$, $SESSION_GROUP_ID$ may require runtime substitution - Follow the instructions and guidelines provided in the content above


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jackdark425/aigroup-mdtoword-mcp (version 0.1.2)

--- name: aigroup-mdtoword-mcp description: Use `aigroup-mdtoword-mcp` to convert Markdown into `.docx`. Route Markdown file conversion, generated Markdown conversion, table-to-Markdown preprocessing, formula handling, table handling, and page-layout requests here. homepage: https://github.com/jackdark425/aigroup-mdtoword-mcp --- # Markdown to Word MCP Use `aigroup-mdtoword-mcp` for Markdown-to-`.docx` conversion. ## Route 1. Confirm the deliverable: - existing Markdown file to convert - Markdown content generated in the session - tabular data that should become Markdown before conversion 2. Choose the right operation: - `markdown_to_docx` for primary conversion - `table_data_to_markdown` when the raw input is structured table data 3. Ask or infer the output style only when it matters: - technical memo - business report - academic-style document - minimal document 4. If formulas, tables, headers, footers, or local images are important, mention that explicitly before conversion. 5. Return the path to the generated `.docx` and summarize any formatting assumptions. ## Requests - Convert a finished Markdown note, report, or memo into `.docx`. - Produce a polished Word deliverable from generated Markdown in the same run. - Preserve formulas, structured tables, and page furniture such as headers or page numbers. - Turn extracted or CSV-like table data into Markdown and then into Word. ## References - Read [capabilities.md](./references/capabilities.md) for the server features and delivery checklist.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: altusrossouw/reddit-stonks (version 1.1.1)

--- name: reddit-stonks version: 1.1.0 description: Scrape Reddit stock pages (r/wallstreetbets, r/stocks, etc.) and use Deepseek AI to analyze which stock has the highest 1-week return potential. Includes a web app (uvicorn app:app). Use when the user asks about stock picks, Reddit stock sentiment, meme stocks, investing ideas from Reddit, "what should I buy", "best stock this week". Supports --euro flag for European exchange equivalents. metadata: openclaw: requires: env: - DEEPSEEK_API_KEY bins: - python3 primaryEnv: DEEPSEEK_API_KEY homepage: https://github.com/AltusRossouw/reddit-stonks emoji: "\U0001F4C8" --- # Reddit Stonks Analyzer Analyze Reddit stock sentiment + AI-powered stock picks for short-term returns. ## Setup (first time only) ```bash cp .env.example .env # Edit .env: add DEEPSEEK_API_KEY pip install -r requirements.txt ``` ## Quickstart ```bash # Terminal mode python3 stonks.py python3 stonks.py -e -p 25 # Web app mode python3 -m uvicorn app:app --host 0.0.0.0 --port 8080 # Open http://localhost:8080 ``` ## Flags | Flag | Description | |------|-------------| | `-e`, `--euro` | Show European exchange equivalents for top picks | | `-p N`, `--posts N` | Posts per subreddit (default: 50) | ## How it works 1. **Scrapes** 7 stock-related subreddits (r/wallstreetbets, r/stocks, r/investing, r/pennystocks, r/StockMarket, r/thetagang, r/dividends) for hot posts 2. **Extracts** stock tickers ($TICKER and all-caps mentions) from post titles and bodies 3. **Fetches** real-time stock data via Yahoo Finance (price, P/E, beta, volume, short float, analyst targets) 4. **Sends** everything to the Deepseek AI for analysis ## Output Generates a stock data table, then AI analysis with: - Top Pick (best 1-week return) - Runner-up - Wildcard (high risk/reward) - Risk factors - Confidence score - Optionally: European exchange equivalents ## Architecture - `stonks.py` — orchestrator + AI analysis via Deepseek - `reddit_scraper.py` — scrapes 7 subreddits via Reddit JSON API (no auth required) - `stock_data.py` — Yahoo Finance fundamentals/technicals + European ticker lookup ## Disclaimer AI-generated analysis for entertainment/educational purposes only. Not financial advice.


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gsc578045031-cloud/endless-downstairs (version 1.0.3)

--- name: endless-downstairs description: Interactive interface for the text adventure game Endless Downstairs, providing game state management and event selection handling; use when users want to play the Endless Downstairs game, start a horror adventure, or need a text adventure game experience. user-invokable: true --- # Endless Downstairs This is a text adventure game. As an AI assistant, you need to translate the player's input text, call the Python processing logic, and return the results to the player exactly as they are. Command Quick Reference | Player Intent | Python Command | | -------------- | ------------------------------------ | | Start new game | `python game.py new` | | Make a choice | `python game.py choose <choice_num>` | | View status | `python game.py status` | | View inventory | `python game.py inventory` | | Input text | `python game.py input <文字>` | ## Translate Player Input → Python Command Execute corresponding commands based on the player's natural language: - "I choose the first one/1" → `python game.py choose 1` - "Status" → `python game.py status` - "Items/Inventory" → `python game.py inventory` - "Input xxx" → `python game.py input xxx` ## Rules - **Output the game content directly; do not think, modify, add, or summarize.** - **Decision-making power lies entirely with the player; do not execute any commands, including continuing options, before the player gives a clear instruction.** - **Only one command can be executed per conversation turn; multiple commands cannot be executed consecutively.**

path: checkpoints.json

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: jochoonhwan/korean-business-email (version 1.0.0)

--- name: korean-business-email description: 한국 비즈니스 이메일을 작성합니다. 다음 경우에 사용: (1) 업무联系的 이메일 필요한 경우, (2) 클라이언트/동료에게 메일が必要な 경우, (3) 제안서나 보고서 메일が必要な 경우, (4) 한국 비즈니스 맥락에 맞는 전문적인文体が 필요한 경우. 한국🇰🇷 기업 문화에 맞는 적절한 어투와 포맷으로 이메일을 작성합니다. --- # 🇰🇷 한국 비즈니스 이메일 템플릿 ## 개요 한국 기업 문화에 맞는 전문적인 비즈니스 이메일을 작성합니다.場合에 따라 올바른 어투와 형식을 사용하여 효과적인 커뮤니케이션を実現합니다. ## 이메일 유형 ### 1. 🇰🇷初견적 문의 (+문의) **용도:** 신규 고객/거래처에게 첫메일 **어투:** 공손하고 briefly **结构调整:** ``` 제목: [회사명] regarding [상품/서비스명] 문의 안녕하십니까. [회사명] [담당자명] 과장님, [간단한 자기 소개] [문의 내용 - 핵심만] 关联 문의를 드리면 확인해 주세요. 감사합니다. [이름/직책] ``` ### 2. 💼 제안서/견적서 메일 **용도:** 클라이언트에게 제안 내용 전달 **어투:** 전문적이고 확신 있는 ``` 제목: [프로젝트명] 제안서 - [날짜] 안녕하십니까. [담당자명] 님, 먼저 [프로젝트명] 관련하여 제안서를 드리게 되어 기쁘게 생각합니다. --- ### 3. 📊 보고서/회고 메일 **용도:** 업무 진행 상황/alarm 보고 **어투:** 간결하고 명확하게 ``` 제목: [프로젝트명] 진행 상황 보고 - [날짜] 1. 완료 사항 - [ ] 2. 진행 중 - [ ] ``` 3.问题和对策 - [ ] → [ ] ``` ### 4. 🎫 감사/사과 메일 **용도:** 감사 인사 또는 문제 발생 시 **어투:** 진정 어린 ``` 제안: [ ]에 대한 감사 인사 ``` --- ## 🇰🇷 한국 기업 문화 포인트 ### ✅ 올바른 예 - "검토해 주세요" → "확인해 주세요" - "알려주신다" → "알려주신 내용에 감사드립니다" - "해 주세요" → "부탁드립니다" ### ❌ 피해야 할 것 - 너무 짧은 이메일 (2문장 이하) - "~임다" 체 (공식 문서에서 피하기) - "~해도 돼요?" ( hierarchie 무시) - 이모지 과사용 (핵심 업무 이메일) ## 핵심 원칙 1. **핵심 먼저**: 제목과 첫 문단에 핵심 내용 2. **명확한 CTA**: " чего 기대하는지" 기재 3. **공손한 어투**: 높임말 원칙 4. **간결함**: 핵심만 전달 ( 長话缩短)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 534422530/laosi-blog-watcher (version 1.0.0)

--- name: blog-watcher description: 博客监控技能 - 监控博客和网站更新，通过RSS或网页变化检测。 metadata: {"openclaw": {"requires": {"python": ["feedparser", "beautifulsoup4"]}, "install": []}} tags: [blog, monitor, scraping, website, tracking] version: 1.0.0 author: laosi source: adapted --- # Blog Watcher - 博客监控 > 激活词: 博客监控 / 网站更新 / 追踪博客 ## 安装 ```bash pip install feedparser beautifulsoup4 requests ``` ## 功能 - RSS订阅监控 - 网页变化检测 - 定时检查 - 通知提醒 ## Python函数 ```python import feedparser import hashlib import requests from bs4 import BeautifulSoup from datetime import datetime class BlogWatcher: def __init__(self): self.watch_list = [] self.content_hashes = {} def add_blog(self, name: str, url: str, use_rss: bool = True): self.watch_list.append({ 'name': name, 'url': url, 'use_rss': use_rss, }) def check_updates(self): results = [] for blog in self.watch_list: if blog['use_rss']: result = self._check_rss(blog) else: result = self._check_webpage(blog) if result: results.append(result) return results def _check_rss(self, blog: dict) -> dict: feed = feedparser.parse(blog['url']) if feed.entries: latest = feed.entries[0] return { 'name': blog['name'], 'title': latest.title, 'link': latest.link, 'published': latest.get('published', 'Unknown'), } return None def _check_webpage(self, blog: dict) -> dict: response = requests.get(blog['url']) soup = BeautifulSoup(response.text, 'html.parser') # 获取页面标题 title = soup.title.string if soup.title else 'No title' # 计算内容哈希 content_hash = hashlib.md5(response.text.encode()).hexdigest() if blog['url'] in self.content_hashes: if self.content_hashes[blog['url']] != content_hash: self.content_hashes[blog['url']] = content_hash return {'name': blog['name'], 'title': title, 'updated': True} else: self.content_hashes[blog['url']] = content_hash return None ``` ## 使用示例 ```python watcher = BlogWatcher() # 添加博客 watcher.add_blog('技术博客', 'https://blog.example.com/feed.xml') watcher.add_blog('AI新闻', 'https://news.example.com/rss') # 检查更新 updates = watcher.check_updates() for update in updates: print(f"[{update['name']}] {update['title']}") ``` ## 输出格式 ```markdown ## 博客更新 ### 检测到 3 个更新 1. **技术博客** - 新文章: Python新特性解析 - 链接: https://blog.example.com/post/123 - 时间: 2026-04-28 2. **AI新闻** - 新文章: GPT-5发布 - 链接: https://news.example.com/gpt5 - 时间: 2026-04-28 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a3273283/vwu-kling (version 1.0.0)

# kling Models for vwu.ai vwu.ai 平台上的 kling 模型调用技能。 ## 支持的模型 - kling-v1 - kling-v2-1 - kling-v2-1.master - kling-v2-5-turbo - kling-v2-6 - kling-v2.master - kling-v3 共 7 个模型。 ## 配置 使用前需要设置 vwu.ai API key: ```bash export VWU_API_KEY="your-key-here" ``` 获取 key: https://vwu.ai 控制台 ## 使用示例 ```bash # 调用模型 vwu-chat --model kling-v1 "你的问题" ``` ## API 兼容性 所有模型兼容 OpenAI API 格式，支持标准 chat completions 接口。 ## 额度提示 如果使用时提示"额度不足"或类似错误： 1. 访问 https://vwu.ai 2. 在控制台充值或调整额度 3. 或生成新的 API key --- 由 Claw 自动生成 | 源数据: vwu.ai


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