OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mrgyan/auto-dream-light (version 0.1.1)

--- name: auto-dream-light version: 0.1.1 description: Lightweight, memory-safe Auto Dream workflow for OpenClaw that consolidates recent notes into existing memory files without replacing the user’s current memory structure. --- # Auto Dream Light A lightweight, memory-safe Auto Dream skill for OpenClaw that works with the memory system the user already has. ## What this skill is for Use this skill when the user wants: - on-demand memory consolidation - a lightweight “dream” workflow - a dream log with execution history - a gradual path from manual to semi-automatic memory cleanup - memory consolidation **without** replacing the current `MEMORY.md` structure This skill is intentionally conservative: - it does **not** rebuild the memory architecture - it does **not** introduce dashboards, indexes, or archives by default - it does **not** assume the user's memory follows a template - it prefers small, explicit, high-value updates ## Why install it - works with existing `MEMORY.md` and `memory/` - makes conservative, incremental updates - avoids noisy or fake “productivity” output - keeps runs easy to review and audit - supports a clean path from manual runs to semi-automatic operation ## Core idea Instead of forcing a new memory system, this skill works with the one the user already has. Default approach: 1. scan recent daily logs 2. extract durable, high-value information 3. route items to the correct destination 4. update memory incrementally 5. record the run in a dream log 6. return a concise summary to the user ## Read these references when needed - Read `references/adapted-plan.md` before designing or changing the workflow. - Read `references/manual-run.md` when running the workflow manually. - Read `references/semi-auto.md` when the user wants a fixed trigger-based flow or wants to prepare for cron later. ## Default file roles Typical targets: - `MEMORY.md` → long-term facts, stable preferences, system conclusions, reusable lessons - `memory/projects/**` → project-specific context - `memory/system/auto-dream-log.md` → dream execution history only - `memory/YYYY-MM-DD.md` → raw daily notes; only add a consolidation marker when appropriate ## Operating rules - Preserve the existing memory structure. - Prefer project files over stuffing everything into `MEMORY.md`. - Skip low-value chat, tests, and one-off noise. - Do not invent memory just to make a run look productive. - If there is nothing worth consolidating, explicitly skip and say so. ## Trigger guidance Typical trigger phrases: - "整理记忆" - "dream now" - "跑 dream" - "做一次记忆整理" - "跑一次适配版 auto dream" If the user says `dream details`, show recent dream-log history instead of running a new consolidation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ryan-wuxl/smart-code-reviewer (version 1.0.0)

--- name: code-review-assistant description: 代码审查助手 - 自动分析代码，提供审查意见、性能优化建议、安全漏洞检测。支持多种编程语言，生成详细的代码审查报告。 homepage: https://github.com/openclaw/code-review-assistant metadata: openclaw: emoji: 🔍 requires: bins: ["node"] --- # 代码审查助手 自动代码审查，提升代码质量。 ## 功能特点 - 🔍 **自动审查** - 分析代码结构和风格 - ⚡ **性能优化** - 识别性能瓶颈 - 🔒 **安全检测** - 发现潜在安全漏洞 - 📊 **质量报告** - 生成详细审查报告 - 🛠️ **多语言支持** - 支持 JS/Python/Go/Java 等 ## 使用方法 ```bash node scripts/review.mjs --file ./src/app.js ``` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: modestyrichards/modesty-generate-personal-brand-ad-creative-brief (version 1.0.0)

--- name: "Generate Personal Brand Ad Creative Brief" description: "Plan campaign visuals and hooks for personal brand promotions. Use when working on paid campaign planning for thought leaders, coaches, personal brand..." homepage: "https://www.skillboss.co/skills/generate-personal-brand-ad-creative-brief" metadata: {"category":"personal-branding","subcategory":"automation","skill_id":[REDACTED_SECRET],"generated_day":"","seo_keywords":["ad creative brief AI","personal brand ad planning","personal brand AI","creator workflow automation"]} --- # Generate Personal Brand Ad Creative Brief Plan campaign visuals and hooks for personal brand promotions. Use when working on paid campaign planning for thought leaders, coaches, personal brand... ## Use When Use this skill for paid campaign planning work for thought leaders, coaches, personal brand builders. ## Workflow 1. Clarify the audience, business goal, constraints, and deliverable. 2. Produce the strongest first draft for the target use case. 3. Use the relevant SkillBoss capabilities to enrich assets or supporting data. 4. Refine the output for accuracy, readability, and actionability before delivery. ## SEO / GEO - Primary keywords: ad creative brief AI, personal brand ad planning, personal brand AI, creator workflow automation - Search intent: paid campaign planning - Canonical slug: `generate-personal-brand-ad-creative-brief` - Install query: Install Generate Personal Brand Ad Creative Brief with SkillBoss ## APIs Used - `chat` - `image_generation` ## Suggested Prompt [Generate Personal Brand Ad Creative Brief] for my team ## Notes - Plan campaign visuals and hooks for personal brand promotions - Review outputs before external publication or automated execution. ## SEO Keywords - ad creative brief AI - personal brand ad planning - personal brand AI - creator workflow automation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yezhaowang888-stack/huimai-doc-process (version 3.0.0)

--- name: 📄 全能文档管家 description: 格式互转、PPT多模板生成、文档摘要、合同分析——文档一条龙。 metadata: {"openclaw":{"emoji":"📄","pricing":"free"}} --- # 📄 全能文档管家 > 文档格式互转、PPT智能生成、文档摘要分析，一个Skill全搞定。 ## 📜 更新日志 ### v3.0.0 (2026-05-08) - **大改版**：从文档摘要升级为全能文档管家 - 🆕 **格式互转**：docx ↔ pdf ↔ txt ↔ md，全能互转 - 🆕 **PPT智能生成**：支持商务/学院/会议/简约4种风格 - 🆕 **自由粘贴复制**：从任意文档提取纯文本 - 保留原文档摘要、合同分析等全部功能 ### v2.0.0 (2026-05-03) - 新增多文档对比摘要 - 新增合同关键条款自动提取 - 输出格式可选 ### v1.0.0 (2026-04-24) - 初版发布（文档摘要） ## 🆕 核心功能 ### 1️⃣ 格式互转（LibreOffice引擎） | 源格式 | 目标格式 | |--------|---------| | Word (.docx) | PDF, TXT, MD, HTML | | PDF | TXT, MD, DOCX | | Markdown (.md) | DOCX, PDF, HTML | | 纯文本 (.txt) | DOCX, PDF, MD | | HTML | DOCX, PDF, TXT | ### 2️⃣ PPT智能生成 | 模板风格 | 适用场景 | |---------|---------| | 🏢 商务风 | 工作汇报、方案演示 | | 🎓 学院风 | 学术报告、作业展示 | | 🏛 会议风 | 内部讨论、项目评审 | | ✨ 简约风 | 通用简洁、快速展示 | **输入方式：** - 上传文档 → 提取内容 → 生成PPT - 直接粘贴文本 → 生成PPT - 文档+指定风格 → 生成PPT ### 3️⃣ 文档摘要（保留原版功能） - URL/粘贴文本 → 全文摘要 - 多文档对比摘要 - 合同关键条款提取 ## 👤 为什么做这个 之前v2.0做文档摘要，有用户反馈说：能不能直接帮我改格式？能不能直接生成PPT？这些是文档处理最刚需的功能。v3.0一次性解决。 ## 👔 适用人群 - **职场人**：Word转PDF、文档生成PPT汇报 - **学生**：论文格式转换、课堂PPT生成 - **教师**：教案格式转换、课件生成 - **所有人都用到** ## 🔧 校准框架 本Skill搭载惠迈校准框架v2.0。 - **温情模式（默认）**：先了解用途再处理，每一步有提示 - **专业模式（可选）**：直出结果，不提问题 ## 💡 使用小贴士 - PDF转Word：复杂排版可能有偏差，建议人工核对 - PPT生成：内容越详细，PPT质量越高 - 格式互转：支持批量文件处理 ## ⚠️ 免责与已知限制 - PDF扫描件无法转换（需要OCR） - PPT模板效果基于标准样式，复杂排版需微调 - 文件大小限制：单个文件≤20MB ## 📬 联系与反馈 - **邮箱**: [REDACTED_SECRET] ## 安装 ```bash clawhub install doc-process ``` ## 更新 ```bash clawhub update doc-process ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dexzopiclone/duel-loop (version 1.0.0)

--- name: duel-loop description: "彻底断绝主代理长期对话中累积的偏见与情绪对产出的裹挟，确保每次产出忠实于需求本身，不再随对话越长越偏离预期。你只负责提需求和最终验收。传统单agent反复调prompt、人工审核输出的工作流，在这里被架构级消解了。角色隔离的双子代理对抗架构：执行和QA子代理运行在完全架空的环境中，看不到主代理的会话上下文、用户画像和记忆。5维10分制评分，满分才交付，否则自动打回重构，5次不过熔断兜底。需求说一次就行，中间全程AI自主协调，没有半成品到你面前。触发词：走QA、审核、质量检查、评审、review。" --- # Self Evolution 双子代理执行-审核流水线。主代理负责调度和验收，不直接产出敏感内容。 <!-- baseDir = /root/.openclaw/workspace/skills/duel-loop --> ## 触发 用户创建/修改规则、方案、协议、策略，且明确要求走 QA 流程。关键词：`走QA`、`审核`、`QA`、`质量检查`、`评审`、`review`。 ## 核心原则 **主代理禁止直接执行以下类型的内容生成**： - 拟定规则/方案/协议文本 - 总结、归纳、建议类输出 - 任何高度依赖上下文质量的创造性或分析性工作 主代理的唯一职责：**调度、传递上下文、验收、打回**。 ## 流程总览 ``` Dispatch(执行子代理) → Review(QA子代理) → Accept/Reject(主代理) ↓ Reject Dispatch(修订子代理) → Review(QA子代理) → ... ↓ 5次未通过 CIRCUIT BREAK → 报告用户 ``` ### Step 1: Dispatch（派发执行） 将任务委派给执行子代理（`sessions_spawn`，`runtime="subagent"`，`mode="run"`）。 **执行子代理 prompt 要求**： - 包含任务描述（做什么） - 包含必要的上下文（相关文件路径、背景信息、约束条件） - 不包含主代理的完整会话历史（避免污染，只传递任务相关上下文） - 输出写入 `drafts/<name>-v1.md` **上下文传递规则**： - 只传递与任务直接相关的文件内容，不传递完整会话上下文 - 如果需要引用会话中的具体信息，由主代理提取关键片段后注入 prompt - 执行子代理是隔离环境，不应感知到主代理的身份/用户画像/memory ### Step 2: Review（QA 审核） 派 QA 子代理审核执行子代理的产出。QA prompt 模板见 `templates/qa-review-prompt.md`。 **评分维度（满分 10）**： | 维度 | 分值 | 评估标准 | |------|------|----------| | 根因分析深度 | 2 | 是否追溯到问题本质而非仅描述表象；是否识别出因果链条和递进关系 | | 方案完备性 | 3 | 是否覆盖所有已识别的子问题；边界条件、异常路径、默认行为是否明确 | | 领域无关性 | 2 | 规则/方案是否不绑定特定实现细节，具有通用可复用性 | | 可操作性 | 2 | 是否有明确的执行步骤或判断标准，而非模糊原则；执行者能否无歧义地落实 | | 副作用评估 | 1 | 是否评估了方案引入的新风险/开销/兼容性问题，并给出控制措施 | ### Step 3: Accept/Reject（验收） 主代理对 QA 结果进行验收。 **通过条件**：QA 评分 10/10 **打回条件**：QA 评分 < 10/10 打回时，主代理将 QA 反馈（评分 + 扣分点 + 改进建议）作为输入，派发新的执行子代理进行修订。修订子代理必须收到： - 上一版本草稿路径 - QA 审核的完整评分和扣分点 - 明确指令："根据以下 QA 反馈修订方案" 修订后文件版本号递增：`<name>-v2.md`。 ### Step 4: Approve（落地） 10/10 通过后，主代理将草稿内容落地到目标生产文件。 ### 熔断机制 **5 次验收不通过 → 触发熔断**。 熔断后主代理必须： 1. 停止自动修订循环 2. 向用户输出： - 总共经历的轮次和每轮评分趋势 - QA 在最后一轮提出的剩余扣分点 - 最后一版草稿的文件路径 - 建议用户人工介入的原因（持续未通过的共性模式，如维度定义模糊、约束矛盾等） 3. 等待用户决策：接受当前版本 / 手动调整后继续 / 放弃 ## QA Prompt 构造 派发 QA 子代理时，读取模板后填入实际参数： ``` read {baseDir}/templates/qa-review-prompt.md ``` 占位符替换： - `{{DRAFT_PATH}}` → 草稿文件绝对路径 - `{{ROUND}}` → 当前轮次（1-5） - `{{PREV_CONTEXT}}` → 上一轮 QA 评分和扣分点（第 1 轮为空） - `{{TASK_BRIEF}}` → 用户原始需求的简要描述（由主代理从会话中提取，不超过 200 字） ## 与主代理上下文的关系 | 角色 | 职责 | 看到的上下文 | |------|------|-------------| | 主代理 | 调度、验收、打回、落地、熔断处理 | 完整会话上下文 | | 执行子代理 | 产出方案/修订文本 | 仅任务描述 + 相关文件内容 | | QA 子代理 | 审核方案质量 | 仅草稿文件 + 评分维度 + 上一轮反馈 | 执行子代理和 QA 子代理之间**没有直接通信**，全部通过主代理中转。 ## 目录结构 ``` skills/self-evolution/ ├── SKILL.md # 本文件 └── templates/ └── qa-review-prompt.md # QA 子代理 prompt 模板 ``` 草稿存放于 workspace 根目录：`drafts/<name>-v<n>.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: doudou459/cae-skill (version 1.0.0)

--- name: cae_skill description: Control local CAE applications on Windows including launching apps, opening files, checking status, closing apps, detecting active and running apps, detecting common executable paths, and saving user-provided executable paths. --- # CAE Skill This skill controls supported local CAE applications on Windows. ## Supported apps - abaqus - ansys - ansa - hyperworks ## What this skill can do - launch a CAE app - open a file in a CAE app - check whether a CAE app is running - close a CAE app - get the current active CAE app - get all running supported CAE apps - detect an executable path using saved paths and predefined common install paths - save a user-provided executable path ## Constraints - Do not scan the whole computer. - Do not traverse all files on disk. - Do not read the Windows registry. - Only use: 1. saved paths in `config.json` 2. predefined common install paths in `config.json` - If the executable cannot be found, ask the user to provide the full executable path. ## Execution Use `skill_runner.py` as the execution entrypoint. Payload format: ```json { "skill": "launch_app", "args": { "app": "abaqus" } } ``` # Supported Actions ## launch_app Arguments: - app - config_file (optional) ## open_file_in_app Arguments: - app - file_path - config_file(optional) - auto_launch(optional) - wait_seconds(optional) ## is_app_runing Arguments: - app - config_file(optional) ## close_app Arguments: - app - config_file(optional) - force(optional) ## get_activate_app Arguments: - config_file(optional) ## get_running_apps Arguments: - config_file(optional) ## detect_app_path Arguments: - app - config_file(optional) ## set_app_path Arguments: - app - path - config_file(optional) # Note This skill is designed for **local CAE workstation automation**. It intentionally avoids heavy filesystem scanning to maintain fast response and predictable behavior.

path: README.md
content: # cae_skill

Windows local CAE control skill.

Supported apps:
- abaqus
- ansys
- ansa
- hyperworks

Capabilities:
- launch_app
- open_file_in_app
- is_app_running
- close_app
- get_active_app
- get_running_apps
- detect_app_path
- set_app_path

Notes:
- does not scan the whole computer
- does not read the Windows registry
- only checks saved paths and predefined common paths
- some software launch commands may need local adjustment depending on version and installation style

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jonathanjing/openclaw-tally (version 0.3.1)

--- name: openclaw-tally description: "Tokens tell you how much you paid. Tasks tell you what you got. Tally tracks every OpenClaw task from start to finish — cost, complexity, and efficiency score." version: "0.3.1" metadata: {"openclaw": {"emoji": "📊", "runtime": "node", "requires": {"anyBins": ["node", "npm"]}}} --- # OpenClaw Tally Reframes AI usage from token-counting to task-completion economics. Instead of "how many tokens?", answer "how much to get X done, and was it worth it?" ## 🛠️ Installation ### 1. Ask OpenClaw (Recommended) Tell OpenClaw: *"Install the openclaw-tally skill."* The agent will handle the installation and configuration automatically. ### 2. Manual Installation (CLI) If you prefer the terminal, run: ```bash clawhub install openclaw-tally ``` ## Security & Privacy Declaration - **Hook**: This skill registers a `message-post` hook and processes **every message**. - **Local only**: All processing is purely local. No data is sent to any external server. - **Message content**: The task detector reads message text to identify task boundaries (start/complete/fail signals) using regex pattern matching. **No message text is stored** — only metadata (token count, model, session_id, complexity score) is persisted to the database. - **Sandboxed storage**: SQLite database defaults to `~/.openclaw/tally/tally.db`. A custom path can be provided for testing. - **Native dependency**: Requires `better-sqlite3` (native Node.js addon). Installation runs `npm install` which triggers a native build step. - **Permissions**: No network access. No exec permissions. Filesystem limited to `~/.openclaw/tally/`. ## What It Does - **Detects tasks** automatically from message streams (Layer 1: Task Detector) - **Attributes costs** across sessions, sub-agents, and cron triggers (Layer 2: Task Ledger) - **Computes TES** (Task Efficiency Score) per task, model, and cron (Layer 3: Analytics Engine) ## Commands - `/tasks list` — Show recent tasks with status, cost, and TES - `/tasks stats` — Summary statistics for a time period - `/tasks this-week` — This week's task summary - `/tasks show <task_id>` — Show task detail - `/tasks report --dimension model` — Model efficiency report - `/tasks cron-health` — Cron efficiency and health check ## Complexity Levels - **L1 (Reflex)**: Single-turn, text-only, no tools - **L2 (Routine)**: Multi-turn or 1–3 tool calls - **L3 (Mission)**: Multiple tools + file I/O + external APIs - **L4 (Campaign)**: Sub-agents + cron + cross-session ## TES (Task Efficiency Score) ``` TES = quality_score / (normalized_cost × complexity_weight) ``` - **> 2.0** 🟢 Excellent - **1.0–2.0** 🟡 Good - **0.5–1.0** 🟠 Below average - **< 0.5** 🔴 Poor - **0.0** ⚫ Failed ## Usage When the skill is installed, it automatically hooks into `message-post`. Use the `/tasks` commands above to query analytics. All data is stored locally in `~/.openclaw/tally/tally.db`. See [PRD.md](./PRD.md) for the full product specification.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ayao99315/opennexum-ts (version 0.1.0)

--- name: opennexum-ts version: 0.1.0 description: Contract-driven multi-agent orchestration with ACP. TypeScript CLI for spawning and tracking coding agents via OpenClaw sessions_spawn. requires: node: ">=20" tools: [pnpm, openclaw] --- # OpenNexum TS Contract-driven coding agent orchestration via OpenClaw ACP. ## When to use - Coordinating multiple AI coding agents on a project - Running generator/evaluator pairs with automatic retry - Tracking parallel ACP sessions with Telegram notifications ## Quick Start 1. `pnpm install && pnpm build` 2. Set env: TELEGRAM_BOT_TOKEN, TELEGRAM_CHAT_ID 3. `nexum init` — initialize project 4. Create Contract YAML in docs/nexum/contracts/ 5. `nexum spawn <taskId>` — get spawn payload 6. Call sessions_spawn with payload 7. `nexum track <taskId> <sessionKey>` — record session 8. `nexum eval <taskId>` + spawn evaluator 9. `nexum complete <taskId> <verdict>` — process result


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: neotize/aliyun-unified-search (version 1.0.0)

--- name: aliyun-unified-search description: Perform web searches using Alibaba Cloud Unified Search API. Returns relevant results with content snippets, scores, and metadata. Use this skill when the user needs to search the web for information, especially for Chinese content or when requiring time-filtered search results. homepage: https://help.aliyun.com/zh/document_detail/2883041.html?spm=a2c4g.11186623.help-menu-2837261.d_4_0_2_3_0.153cda16tmvRJQ --- # Aliyun Unified Search 使用阿里云统一搜索API进行网页搜索，获取相关结果、内容摘要和元数据。 ## Authentication 设置环境变量： ```bash export ALIYUN_IQS_API_KEY="your-api-key-here" ``` 获取API-KEY：[创建并查看凭证](https://help.aliyun.com/zh/document_detail/2872258.html) ## Quick Start ### Using the Script ```bash node {baseDir}/scripts/search.mjs "搜索问题" node {baseDir}/scripts/search.mjs "搜索问题" --time-range OneWeek node {baseDir}/scripts/search.mjs "搜索问题" --category finance node {baseDir}/scripts/search.mjs "搜索问题" --time-range OneDay --category news ``` ### Examples ```bash # 基本搜索 node {baseDir}/scripts/search.mjs "云栖大会" # 搜索最近一周的内容 node {baseDir}/scripts/search.mjs "AI人工智能" --time-range OneWeek # 搜索金融类内容 node {baseDir}/scripts/search.mjs "股票市场" --category finance # 搜索最近一天的新闻 node {baseDir}/scripts/search.mjs "科技新闻" --time-range OneDay --category news # 输出原始JSON node {baseDir}/scripts/search.mjs "杭州美食" --json ``` ## Options | Option | Description | Default | |--------|-------------|---------| | `--time-range <range>` | 时间范围: `OneDay`, `OneWeek`, `OneMonth`, `OneYear`, `NoLimit` | `NoLimit` | | `--category <category>` | 查询分类: `finance`, `law`, `medical`, `internet`, `tax`, `news_province`, `news_center` | - | | `--json` | 输出原始JSON | false | ## Time Range Options | Value | Description | |-------|-------------| | `OneDay` | 1天内 | | `OneWeek` | 1周内 | | `OneMonth` | 1月内 | | `OneYear` | 1年内 | | `NoLimit` | 无限制（默认） | ## Category Options | Value | Description | |-------|-------------| | `finance` | 金融 | | `law` | 法律 | | `medical` | 医疗 | | `internet` | 互联网（精选） | | `tax` | 税务 | | `news_province` | 新闻省级 | | `news_center` | 新闻中央 | ## Tips - **尽可能将query限制在30个字符以内**，可以在调用接口前通过模型做Query改写 - **一般通用场景不要指定category**，会影响召回效果 - **engineType固定为Generic**，返回10条结果 ## API Document Reference 详见参考文档：`https://help.aliyun.com/zh/document_detail/2883041.html?spm=a2c4g.11186623.help-menu-2837261.d_4_0_2_3_0.153cda16tmvRJQ`


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: phy041/phy-competitor-watch (version 1.0.3)

--- name: Competitor Watch description: Competitor social media content strategy analyzer. Feed it a JSON file of any competitor's posts and get a full strategy breakdown — topic distribution, content style analysis (listicle/story/contrarian/how-to/data-driven/thread), posting schedule, top-performing content patterns, and exploitable gaps (topics and styles they DON'T cover). Works with any platform's exported data. No API keys needed. Zero external dependencies. license: Apache-2.0 homepage: https://canlah.ai metadata: author: Canlah AI version: "1.0.3" tags: - social-media - competitive-intelligence - content-strategy - analytics - marketing --- # phy-competitor-watch — Competitor Content Strategy Analyzer Feed it a competitor's posts, get their entire content strategy decoded. ```bash python3 ~/.claude/skills/phy-competitor-watch/scripts/competitor_watch.py \ --file competitor_posts.json --name "Dan Koe" ``` ## What It Analyzes - **Topic distribution** — what they write about most (AI/ML, DevTools, StartupGrowth, etc.) - **Content styles** — listicle, story, contrarian, how-to, data-driven, question-led, announcement, thread - **Posting schedule** — which days they post, frequency per week - **Top performers** — their best posts by engagement + what made them work - **Gaps you can exploit** — topics and styles they DON'T cover ## Input Format ```json [ {"text": "Post content...", "platform": "linkedin", "date": "2026-03-01", "engagement": 5.2}, {"text": "Another post...", "date": "2026-02-28", "engagement": 12.1} ] ``` ## Collecting Competitor Data Use `phy-twitter-scrape`, Reddit API exports, or manual collection. The tool works with any JSON array of posts. --- ## Author **[Canlah AI](https://canlah.ai)** — Run performance marketing without breaking your brand. - GitHub: [github.com/PHY041](https://github.com/PHY041) - All Skills: [clawhub.ai/PHY041](https://clawhub.ai/PHY041)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wuyuyang001-oss/ai-trend-monitor (version 1.0.0)

# AI Trend Monitor 监控 AI 市场趋势和技术前沿，支持实时重大新闻推送 + 定时汇总推送。 ## 功能 - **多渠道监控**：GitHub、Reddit、Twitter/X、小红书、新闻 - **智能 URL 提取**：自动提取具体帖子/文章 URL（非首页） - **重大新闻实时推送**：检测到重大新闻立即推送 - **定时汇总**：每天 9:00、13:00、19:00 定时推送 - **飞书卡片格式**：分渠道表格，完整文本，可跳转链接 ## 安装 ```bash # 克隆到 skills 目录 git clone <repo-url> ~/.openclaw/skills/ai_trend_monitor # 安装依赖 cd ~/.openclaw/skills/ai_trend_monitor npm install ``` ## 配置 编辑 `config.js`： ```javascript module.exports = { // 飞书 Webhook webhooks: { market: 'https://open.feishu.cn/open-apis/bot/v2/hook/xxx', tech: 'https://open.feishu.cn/open-apis/bot/v2/hook/xxx' }, // 监控渠道 channels: ['GitHub', 'Reddit', 'Twitter', '小红书', '新闻'], // 推送时间 schedule: { morning: '0 9 * * *', // 9:00 noon: '0 13 * * *', // 13:00 evening: '0 19 * * *' // 19:00 }, // 重大新闻判定标准 majorNewsCriteria: { funding: 100000000, // 1亿美元 stockChange: 5, // 股价涨跌5% keyPeople: ['Sam Altman', 'Elon Musk', 'Andrej Karpathy'], keyCompanies: ['OpenAI', 'Google', 'Anthropic', 'Meta', 'xAI'] } }; ``` ## 使用 ### 手动运行 ```bash node ai_trend_monitor.js ``` ### 定时任务 使用 OpenClaw cron： ```json { "jobs": [ { "name": "ai-trend-morning", "schedule": "0 9 * * *", "command": "node ~/.openclaw/skills/ai_trend_monitor/ai_trend_monitor.js" }, { "name": "ai-trend-noon", "schedule": "0 13 * * *", "command": "node ~/.openclaw/skills/ai_trend_monitor/ai_trend_monitor.js" }, { "name": "ai-trend-evening", "schedule": "0 19 * * *", "command": "node ~/.openclaw/skills/ai_trend_monitor/ai_trend_monitor.js" } ] } ``` ## 消息格式 飞书卡片消息，按渠道分组： | 渠道 | 标题 | 概述 | 时间 | 影响分析 | 链接 | |------|------|------|------|----------|------| | GitHub | xxx | xxx | 2026-02-19 08:00 | xxx | [查看] | | Reddit | xxx | xxx | 2026-02-19 10:30 | xxx | [查看] | ## 依赖 - Node.js >= 18 - OpenClaw CLI（用于定时任务） - kimi_search API（用于实时搜索） ## 版本 v1.0.0 ## 作者 小赵 ## License MIT


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