OpenClaw/clawhub-security-signals huggingface dataset:
Skill: dalomeve/ren-wu-shou-wei-qi (version 1.0.2)

--- name: 任务收尾器 version: 1.0.2 description: 强制任务收尾与证据提交。确保每个任务都有明确的目标、进度、下一步和完成证据。 --- # 任务收尾器 强制任务收尾，确保每个任务都有明确的完成证据。防止任务中途停滞、无证据完成。 ## 为什么需要这个技能 **问题**: AI 代理经常： - 任务中途停止，无解释 - 只输出计划，不执行 - 缺少明确的完成标准 - 没有证据工件 **解决方案**: 此技能强制执行： - 每个实质性步骤输出目标/进度/下一步 - 完成时必须提供证据 - 多步骤任务必须有完成证明格式 ## 工作流程 ### 1. 任务启动 每个任务开始时输出： ```markdown **目标**: 完成时是什么样子 **进度**: 已完成什么 **下一步**: 现在执行的一个具体行动 ``` ### 2. 执行中更新 每个实质性步骤后更新进度： ```markdown **进度更新**: - 已完成：步骤 1, 2 - 当前：执行步骤 3 - 阻塞：无（或具体阻塞 + 已尝试 + 最小解锁输入） ``` ### 3. 完成证明格式 2 步以上的任务必须包含： ```markdown **DONE_CHECKLIST**: - [ ] 项目 1 已完成 - [ ] 项目 2 已完成 **EVIDENCE**: - 已执行：命令/操作摘要 - 工件：路径/URL/ID - 已验证：检查命令结果 **NEXT_AUTONOMOUS_STEP**: - 一个无需用户输入即可执行的后续步骤 ``` ### 4. 反停滞规则 - 仅计划的回复：最多 1 次 - 下一次回复必须包含执行证据 - 永远不要以"我现在将..."结束而不显示工具结果 ## 可执行完成标准 任务完成当且仅当： | 标准 | 验证 | |------|------| | 目标已陈述 | Select-String "目标" memory/{date}.md 匹配 | | 进度已追踪 | Select-String "进度" memory/{date}.md 匹配 | | 下一步已定义 | Select-String "下一步" memory/{date}.md 匹配 | | 证据存在 | 工件路径/URL 存在 | | 无未解决标记 | Select-String "TODO|PENDING|TBD" artifact 无返回 | ## 使用示例 ### 示例 1：发布技能到 ClawHub ```markdown **目标**: 发布 skill 到 ClawHub，返回 URL 和 skill_id **进度**: - 已完成：读取 SKILL.md，打开浏览器 - 当前：填写发布表单 **下一步**: 填写 Slug/Name/Version 字段 --- **进度更新**: - 已完成：表单填写，文件上传 - 当前：等待发布确认 - 阻塞：无 --- **DONE_CHECKLIST**: - [x] 技能已发布 - [x] URL 已验证可访问 - [x] skill_id 已记录 **EVIDENCE**: - 已执行：clawhub publish 命令 - 工件：https://clawhub.ai/Dalomeve/my-skill - skill_id: k97xxxxx - 已验证：浏览器导航到 URL，200 OK **NEXT_AUTONOMOUS_STEP**: - 更新 INDEX.md 添加新技能条目 ``` ### 示例 2：研究 GitHub 热门项目 ```markdown **目标**: 研究 12+ GitHub 热门项目，提炼 3-5 个痛点，选择一个方向实现 **进度**: - 已完成：浏览 GitHub Trending，记录 14 个项目 - 当前：分析痛点，选择方向 **下一步**: 编写研究报告到 outputs/ --- **DONE_CHECKLIST**: - [x] 14 个项目已分析 - [x] 5 个痛点已识别 - [x] 研究方向已选择（验证空白） - [x] 新项目已创建并发布 **EVIDENCE**: - 研究报告：outputs/2026-03-01-github-research.md - 新项目：https://github.com/Dalomeve/agent-audit-trail - 测试通过：python test_audit_trail.py (4/4 tests passed) - Commit: 03307bdc84abab4bd5e78ab51abcd271ca85a4ab **NEXT_AUTONOMOUS_STEP**: - 将新项目添加到长期任务追踪 ``` ## 隐私/安全 - 证据中不包含敏感数据 - 工件路径使用相对路径或工作空间路径 - 任务日志中不包含凭据 ## 自触发 当以下情况时使用： - 启动任何多步骤任务 - 中断后恢复 - 移交给另一个代理 - 用户要求"不要只给计划，直接执行" ## 与其他技能集成 - **task-finish-contract**: 英文版本，概念相同 - **agent-audit-trail**: 使用审计追踪记录证据 - **phoenix-loop**: 从失败中学习，更新收尾模式 - **HEARTBEAT.md**: 心跳检查任务收尾状态 ## 限制 - 需要代理自觉遵守 - 证据验证是尽力的（URL 可能过期） - 不适用于单步骤简单任务 ## 参考 - `tasks/QUEUE.md` - 任务队列格式 - `memory/tasks.md` - 任务历史 - `skills/task-finish-contract/` - 英文版本 --- **有始有终。用证据证明。**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sqizzo/bratification (version 1.0.1)

--- name: bratification description: Generate brat-style text cover images and sticker-like WhatsApp-safe outputs from custom text. Use when the user asks to make a brat or brat-gen style cover, wants !brat command behavior, needs white-background Arial Narrow renders, or wants square 512x512 sticker-like image replies with PNG fallback and optional WEBP artifacts. --- # Bratification Generate brat-style images from text using the bundled Python scripts. ## Quick start - For a plain render, run `scripts/render_brat.py`. - For command-style parsing like `!brat hello world`, run `scripts/handle_brat_command.py`. - Prefer white background, Arial Narrow bold (with cross-platform font fallback), centered composition, word wrapping without cutting words, and automatic font shrink to fit. - `handle_brat_command.py` now sanitizes slug/output naming and always writes into its managed `scripts/out` directory tree. ## Workflow 1. Decide whether the request is direct text rendering or `!brat` command parsing. 2. Use `scripts/handle_brat_command.py` for command-style input. 3. Use `scripts/render_brat.py` for direct text-to-image rendering. 4. For WhatsApp-safe sticker-like delivery, prefer `--size 512 --sticker-size 512` and send the generated PNG with no caption. 5. Treat WEBP as an optional artifact unless the channel has verified native sticker upload support. ## Commands ### Render text directly ```bash python scripts/render_brat.py "hello world" --png out/hello-world.png --webp out/hello-world.webp ``` ### Parse a `!brat` command ```bash python scripts/handle_brat_command.py "!brat hello world" ``` ### Sticker-like WhatsApp-safe output ```bash python scripts/handle_brat_command.py --size 512 --sticker-size 512 "!brat hello world" ``` ## Output handling - Use PNG as the primary reliable delivery format. - Use WEBP as a secondary artifact for future native-sticker-capable channels. - If the input is only `!brat` with no text, ask briefly for the text. - If generation fails, apologize briefly and say the brat render failed. ## Scripts - `scripts/render_brat.py`: deterministic renderer for PNG/WEBP output - `scripts/handle_brat_command.py`: parser for `!brat`-style input that emits JSON with generated paths


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/cc-switch (version 3.0.2)

--- name: "cc-switch" version: "3.0.1" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Cc Switch concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [cc,switch, reference] category: "devtools" --- # Cc Switch Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Cc Switch concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: manucode2000-max/ndrc-oil-price-monitor (version 1.1.0)

--- name: ndrc-oil-price description: Monitor NDRC (National Development and Reform Commission) website for oil price adjustment announcements. Searches news releases every 10 working days at 17:30 starting from 2026-04-07 and pushes notifications via Feishu. license: MIT --- # 成品油价格监控器 监控国家发改委官网新闻发布，自动检测成品油价格调整信息并推送通知。 ## 功能特性 - 📅 基于中国工作日计算调价窗口（每10个工作日） - 🕐 每日17:30自动检查 - 🔍 搜索发改委官网新闻发布页面 - 📢 发现调整信息后立即推送到 Feishu - 🎯 起始日期：2026-04-07 - ⚙️ 可配置窗口间隔和通知时间 ## 工作原理 1. 计算下一个10工作日窗口期（基于 chinese-workdays 技能） 2. 每日17:30检查当前日期是否在窗口期内 3. 如果是窗口期，搜索发改委网站最新新闻 4. 筛选包含"成品油"、"油价"等关键词的公告 5. 提取价格调整信息（上调/下调幅度） 6. 通过 OpenClaw stdout 捕获推送到 Feishu ## 数据源 - **官网**: https://www.ndrc.gov.cn/xwdt/xwfb/ - **公告类型**: 新闻发布、政策通知 - **关键词**: 成品油、油价调整、汽油、柴油 ## 配置 默认配置： - 起始窗口: 2026-04-07 - 窗口间隔: 10 个工作日 - 检查时间: 每日 17:30 - 时区: Asia/Shanghai ## Quick Start ### 命令行使用 ```bash # 测试运行（强制检查） python oil_price_monitor.py --test # 查看下一个窗口期 python oil_price_monitor.py --next-window # 查看最近公告 python oil_price_monitor.py --recent ``` ## 输出示例 当检测到价格调整时，输出类似： ``` 🛢️ 成品油价格调整公告 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📅 窗口期: 2026-04-07 (第1个窗口) 📢 发布机构: 国家发改委 🕐 发布时间: 2026-04-07 10:00 💵 调整内容: 汽油每吨上调 300 元 柴油每吨上调 290 元 🔗 原文链接: https://www.ndrc.gov.cn/... ``` ## 技术实现 - 使用 `requests` 抓取发改委网站新闻列表 - 使用 `BeautifulSoup` 解析HTML - 集成 `chinese-workdays` 计算工作日 - 输出 Markdown 格式，由 OpenClaw 捕获并推送到 Feishu ## Files ``` oil-price-monitor/ ├── SKILL.md # 技能说明 ├── __init__.py # 包入口 ├── oil_price_monitor.py # 主程序 ├── config.yaml # 配置文件（可选） ├── requirements.txt # 依赖 └── README.md # 详细文档 ``` ## License MIT

path: requirements.txt
content: requests>=2.28.0
beautifulsoup4>=4.11.0
lxml>=4.9.0

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: luochang212/github-research-assistant (version 1.0.2)

--- name: github-research-assistant description: GitHub Research Assistant. Use this skill when the user wants to analyze a GitHub repository. Analysis dimensions -- 1) Basic information; 2) Purpose, what it can be used for; 3) Tech stack, including frameworks, languages, algorithms, etc.; 4) Usage and examples; 5) Technical architecture and module analysis --- # GitHub Research Assistant You are a professional GitHub research assistant, helping users quickly understand the core information of any GitHub repository. ## Analysis Dimensions When the user requests to analyze a GitHub repository, you need to perform a comprehensive analysis covering the following aspects: ### 1. Basic Information - GitHub repository URL - Number of Stars - Number of Forks - Last commit date - One-sentence description ### 2. Repository Purpose - What it can be used for - Core problems/pain points solved - Use cases - Main features - Core APIs and interfaces - Supported input/output formats - Key feature list ### 3. Tech Stack Analysis - **Programming Language**: Main language and version - **Frameworks**: Web/application frameworks used - **Libraries and Dependencies**: Key dependencies - **Algorithms**: Core algorithms (if applicable) - **Others**: Build tools, testing frameworks, CI/CD, etc. ### 4. Usage and Examples - Installation steps - Environment configuration requirements - Basic usage examples (executable code) - Configuration file explanation ### 5. Technical Architecture and Module Analysis - Overall architecture overview (directory structure) - Module division and responsibilities - Core module functions - Module dependencies - Data flow design ## Execution Steps ### Step 1: Get Basic Repository Information Obtain basic repository information including Star count, Fork count, last commit time, and overall directory structure. ### Step 2: Read Key Files Read the following key files to understand the repository: - README.md - Project overview - package.json / pyproject.toml / Cargo.toml - Dependency configuration - Main source files - Understand core logic - Configuration files - Understand project configuration ### Step 3: Analyze and Summarize Based on the obtained information, perform systematic analysis according to the 5 dimensions above. ### Step 4: Output Report Output the analysis report in clear markdown format. ## Output Format ```markdown # GitHub Repository Analysis Report ## 1. Basic Information ## 2. Purpose ## 3. Tech Stack ## 4. Usage ## 5. Technical Architecture ``` ## Notes 1. For larger repositories, prioritize analyzing core files and directories 2. For complex repositories, focus on main entry files and core modules 3. Technical architecture analysis should be inferred from the code structure 4. Make good use of MCP, tool, skill, and CLI to obtain repository information > 注：中文版 SKILL.md 在 [references/skill-cn.md](./references/skill-cn.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckchzh/slogan-maker (version 2.0.0)

--- version: "2.0.0" name: slogan-maker description: "广告语生成器。品牌标语创作、行业广告语、押韵口号、中英翻译、效果测试、经典语库。Slogan maker with brand taglines, industry slogans, rhyming, translation, testing." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # slogan-maker 广告语生成器。品牌标语创作、行业广告语、押韵口号、中英翻译、效果测试、经典语库。Slogan maker with brand taglines, industry slogans, rhyming, translation, testing, and classic collection. ## 核心特点 🎯 **精准** — 针对具体场景定制化输出 📋 **全面** — 多个命令覆盖完整工作流 🇨🇳 **本土化** — 完全适配中文用户习惯 ## 命令速查 ``` tech tech food food education education fashion fashion health health finance finance en2cn en2cn cn2en cn2en ``` ## 专业建议 - 简短有力** — 最好不超过10个字 - 朗朗上口** — 押韵或有节奏感 - 突出差异** — 表达独特卖点(USP) - 情感共鸣** — 触动目标受众的情感 - 易于记忆** — 听一遍就能记住 --- *slogan-maker by BytesAgain* --- 💬 Feedback & Feature Requests: https://bytesagain.com/feedback Powered by BytesAgain | bytesagain.com - Run `slogan-maker help` for all commands ## Commands Run `slogan-maker help` to see all available commands.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: gaoq1/ai-avatar-videoany (version 1.0.0)

--- name: ai-avatar version: 1.0.0 description: Guide users to VideoAny AI Avatar tool to create talking avatar videos from an image and voice. author: qrost permissions: - shell:exec --- # AI Avatar This skill is focused on **AI Avatar** generation guidance. When users need a talking avatar workflow, direct them to: `https://videoany.io/tools/ai-avatar` Key tool details from the live page: - Input image: upload avatar image or provide avatar image URL - Supported image formats: JPG, PNG, WebP - Voice input: audio file or `audio_id` (one is required) - Audio hint: 2-60s voice tracks are supported - Supported audio formats: MP3, WAV, OGG, M4A (or record directly) - Script is optional; credits are based on audio duration ## Dependencies No third-party Python package is required for this guidance skill. ## Usage ### Show AI Avatar Guidance ```bash python3 scripts/guide_ai_avatar.py ``` ### Guidance with Optional Inputs ```bash python3 scripts/guide_ai_avatar.py \ --avatar-image /tmp/avatar.png \ --audio /tmp/voice.wav \ --script "Welcome to our product demo." \ --mode standard ``` ## Agent Behavior - If user asks for AI avatar or talking avatar videos, guide them to `https://videoany.io/tools/ai-avatar` first. - Emphasize this is an image + voice workflow, with optional script prompt. - Explain core quality tips: clear front-facing portrait, clean audio, short test runs first. - Include responsible-use reminders: - user must have rights to the uploaded image/voice - non-consensual impersonation and deceptive deepfakes are prohibited - Use local CLI only as a helper to print guidance; actual generation is done on VideoAny web.


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

--- name: Russian description: Write Russian that sounds human. Not formal, not robotic, not AI-generated. --- ## The Real Problem AI Russian is technically correct but sounds off. Too formal. Too literary. Too stiff. Natives write more directly, with particles and casual contractions. Match that. ## Formality Default Default register is too high. Casual Russian is direct and warm. Unless explicitly formal: lean casual. "Привет" not "Здравствуйте". "Ок" not "Хорошо". "Ага" not "Да". ## Ты vs Вы Critical distinction: - Вы: strangers, elderly, professional, formal - Ты: friends, peers, internet, casual - Russian internet is almost entirely ты - Overusing Вы online = robotic, distant ## Particles & Interjections These make Russian natural: - Ну: filler, hesitation, "well" - Же: emphasis, "after all" - Ведь: "you know", "after all" - Вот: pointing, "here's the thing" - Да: not just "yes" - emphasis, filler - Так: "so", transitional - Missing these = textbook Russian ## Fillers & Flow Real Russian has fillers: - Ну, типа, короче, в общем - Как бы, то есть, значит - Блин, чёрт (mild expletives) - Слушай, смотри ## Casual Shortcuts Spoken patterns in writing: - Сейчас → Щас - Что → Чё/Чо - Тебя → Тя - Говорит → Грит - Вообще → Ваще ## Expressiveness Don't pick the safe word: - Хорошо → Круто, Класс, Огонь, Топ - Плохо → Фигово, Отстой, Ужас - Очень → Офигенно, Дико, Жесть - Красивый → Крутой, Шикарный ## Common Expressions Natural expressions: - Понял/Понятно, Ясно, Ок - Норм, Нормально, Пойдёт - Ладно, Давай, Погнали - Без проблем, Не вопрос ## Reactions React naturally: - Серьёзно?, Правда?, Да ладно! - Офигеть!, Ничего себе!, Вау! - Круто!, Класс!, Топ! - Блин, Чёрт, Ёлки (mild) - )))))) for smiles, ) after sentences ## Internet Style Russian internet has conventions: - ) or ))) = smiling (more ) = more smile) - No ) = serious or cold - хах, ахах, лол = laughter - Lowercase often preferred in casual - Ъ at end for irony/emphasis ## Diminutives Russian loves diminutives for warmth: - Минутку → Минуточку - Спасибо → Спасибки - Привет → Приветик - Use naturally, not excessively ## Punctuation Russian punctuation: - «Ёлочки» for quotes - ... used frequently for trailing off - ) without : for smiles - Comma rules are strict in formal writing ## The "Native Test" Before sending: would a Russian screenshot this as "AI-generated"? If yes—too formal, missing particles, no ). Fix it.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sopaco/md2slides (version 1.0.1)

--- name: md-to-slides description: > 将 Markdown 格式的演讲/汇报文档转化为可编辑的 PPTX 演示文稿。 输出标准 .pptx 文件，可在 PowerPoint / Keynote / WPS 中打开编辑。 triggers: - "生成PPT" - "转成幻灯片" - "做成演示文稿" - "导出PPTX" - "导出PPT" - "md to slides" - "markdown 转 PPT" metadata: version: "2.0" category: presentation --- # md-to-slides ## 做什么 把用户的 Markdown 演讲/汇报文档转化为可编辑的 .pptx 演示文稿。 ## 怎么做 ### Step 0：确认输入 向用户确认要转换的 Markdown 文件路径。 ### Step 1：扫描结构 读取文件，提取 frontmatter 元数据和所有章节标题，报告给用户确认。 ### Step 2：逐章解析 对每个章节生成 Slide 数组。超长章节（>8000 tokens）需要按子标题拆分。主 Agent 只读标题行，正文由子 Agent 解析。 > 子 Agent 解析 Prompt 模板见 `references/analysis-prompt.md`。 ### Step 3：合并确认 汇总所有 Slide，检查密度和连贯性，向用户报告分页方案。 ### Step 4：生成 Schema 将完整的 PresentationSchema JSON 写入当前工作目录的临时文件。 ### Step 5：生成 PPTX 调用生成脚本： ``` node scripts/generate-pptx.cjs <schema.json路径> <输出路径> ``` 生成后提取文本核验内容完整性。 ## 兜底 - 子 Agent 失败 → 重试一次，仍失败则标记该章需人工处理 - Mermaid 图表 → 输出占位 slide，提示手动插入 - 不篡改用户原文内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: earthwalking/mit24-market-segmentation (version 1.0.0)

--- name: mit24-phase1-market-segmentation description: "MIT有序创业24步法第一阶段（步骤1-5）。当用户需要进行市场细分、选择目标市场、构建用户画像、计算市场规模（TAM/SAM/SOM）、分析客户决策过程时使用。触发词：市场细分、目标市场、用户画像、TAM SAM SOM、市场规模、客户洞察、Beachhead、滩头阵地、客户决策、细分市场、客户画像、理想客户、ICP" --- # MIT有序创业24步法 - 阶段一：市场细分与客户洞察 MIT 24步法的第一阶段聚焦于深入理解市场与客户，是整个创业方法论的基础。本阶段包含步骤1-5，帮助创业者从模糊的市场概念逐步聚焦到精准的目标客户。 ## 工作流程 按以下5个步骤依次推进： ### 步骤 1: 市场细分 识别潜在客户群体，将大市场细分为可管理的细分市场。 **关键活动：** - 头脑风暴所有可能的客户群体（按行业、规模、地域、场景等维度） - 根据共同特征进行分组 - 评估每个细分市场的吸引力（规模、增长率、竞争强度、进入壁垒） **输出要求：** 3-5个优先考虑的细分市场，每个细分市场需说明： - 市场规模估算 - 增长趋势 - 竞争格局 - 进入壁垒 ### 步骤 2: 选择滩头阵地市场（Beachhead） 从细分市场中选择一个最容易进入且能快速获得成功的市场。 **五维评估标准：** 1. 客户有紧迫的痛点（痛点强度） 2. 客户有购买能力（付费意愿与能力） 3. 客户容易触达（渠道可达性） 4. 竞争相对较弱（竞争空白点） 5. 与长期愿景一致（战略一致性） **滩头阵地概念：** 第一个目标市场，成功后可作为跳板进入其他市场。选择滩头阵地应聚焦于"小而精"而非"大而全"。 **输出要求：** 选定1个滩头阵地市场，附选择理由与风险评估。 ### 步骤 3: 构建终端用户画像 深入理解目标客户的特征、行为和需求。 **画像维度：** - 人口统计特征：年龄、性别、收入、教育、职业 - 心理特征：价值观、生活方式、兴趣爱好、风险偏好 - 行为特征：购买习惯、使用场景、决策过程、消费频率 - 痛点和需求：核心问题、当前替代方案、期望的理想解决方案 **输出要求：** 1-2个详细的终端用户画像（Persona），包含虚构但真实的客户档案。 ### 步骤 4: 计算市场规模（TAM/SAM/SOM） 量化市场规模，评估商业潜力。 **三层市场模型：** - TAM（总可寻址市场）：整个市场的规模，即所有潜在客户的需求总和 - SAM（可服务市场）：你的产品/服务能力所覆盖的市场部分 - SOM（可获得市场）：在短期内实际能获得的市场份额 **计算方法：** - 自上而下法：从行业报告、市场研究数据推导 - 自下而上法：基于客户数量 × 客单价计算（推荐，更准确） **输出要求：** TAM/SAM/SOM三层数据，标注数据来源和计算假设。 ### 步骤 5: 描述客户决策过程 理解客户如何做出购买决策。 **分析维度：** 1. 决策参与者：谁是使用者？谁是决策者？谁是影响者？谁是审批者？ 2. 决策标准：客户最看重什么？（功能、价格、品牌、服务、风险） 3. 决策过程：从认知 → 兴趣 → 评估 → 购买 → 使用的完整路径 4. 购买障碍：什么阻碍了客户购买？（预算、时间、信任、替代方案） 5. 信息来源：客户从哪里获取信息？（同行推荐、搜索引擎、行业会议、社交媒体） ## 输出格式 完成5个步骤后，生成一份《市场细分与客户洞察报告》，包含： 1. 市场细分矩阵 2. 滩头阵地选择与理由 3. 终端用户画像卡片 4. TAM/SAM/SOM市场规模分析 5. 客户决策旅程地图 ## 实用建议 - 步骤1-3应基于真实客户访谈，而非主观猜测 - TAM/SAM/SOM计算务必标注数据来源，避免"凭空估算" - 滩头阵地市场的选择是后续所有步骤的基础，务必审慎 - 如果用户只关心某一两个步骤，可以单独深入处理该步骤


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jaggu1999/mcp-bridge (version 1.0.1)

--- name: mcp-bridge description: Use mcp-bridge-openclaw CLI to connect to and manage Model Context Protocol (MCP) servers with auto-reconnection and retry logic. Install via npm install -g mcp-bridge-openclaw. homepage: https://www.npmjs.com/package/mcp-bridge-openclaw repository: https://github.com/Jatira-Ltd/OpenClaw-MCP-Bridge --- # mcp-bridge-openclaw CLI tool for connecting to MCP servers with built-in resilience. ## Installation ```bash npm install -g mcp-bridge-openclaw ``` Verified publisher: npm user `jaggu37` ## Commands ### Connect to MCP server ```bash mcp-bridge --config config.json ``` ### List available servers ```bash mcp-bridge --config config.json --list ``` ### Run with verbose logging ```bash mcp-bridge --verbose --config config.json ``` ## Configuration Create `config.json`: ```json { "servers": { "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"], "env": {} } } } ``` **Security tip:** Use environment variables for tokens instead of plaintext in config: ```json { "servers": { "github": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-github"], "env": { "GITHUB_TOKEN": "${GITHUB_TOKEN}" } } } } ``` Then run: `GITHUB_TOKEN=your_token mcp-bridge --config config.json` ## Programmatic Usage ```typescript import { MCPBridge } from 'mcp-bridge-openclaw'; const bridge = new MCPBridge({ configPath: './config.json', onServerConnect: (name) => console.log(`Connected to ${name}`), }); await bridge.connect(); await bridge.disconnect(); ``` ## Key Features - Auto-reconnect on disconnect - Configurable retry logic - Type-safe JSON config - CLI + programmatic API - Multiple server support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: thesethrose/catfact (version 1.0.2)

--- name: Cat Fact description: Random cat facts and breed information from catfact.ninja (free, no API key) read_when: - Wanting random cat facts - Looking up cat breeds - Building fun bot responses metadata: {"clawdbot":{"emoji":"🐱","requires":{"bins":["curl"]}}} --- # Cat Fact Random cat facts from catfact.ninja (no API key required). ## Usage ```bash # Get a random cat fact curl -s "https://catfact.ninja/fact" # Get a random fact (short) curl -s "https://catfact.ninja/fact?max_length=100" # Get cat breeds curl -s "https://catfact.ninja/breeds?limit=5" ``` ## Programmatic (JSON) ```bash curl -s "https://catfact.ninja/fact" | jq '.fact' ``` ## One-liner examples ```bash # Random fact curl -s "https://catfact.ninja/fact" --header "Accept: application/json" | jq -r '.fact' # Multiple facts for i in {1..3}; do curl -s "https://catfact.ninja/fact" --header "Accept: application/json" | jq -r '.fact'; done ``` ## API Endpoints | Endpoint | Description | |----------|-------------| | `GET /fact` | Random cat fact | | `GET /breeds` | List of cat breeds | Docs: https://catfact.ninja


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