OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mzlzyca/html-ocr (version 0.4.0)

--- name: html-ocr description: "OCR for HTML pages containing image-embedded or scanned content. Uses MinerU to extract text from images within HTML files and web pages. Features: OCR extraction for image content in HTML files. VLM mode for complex mixed-content pages. Handles HTML with embedded scanned images. Converts image text to searchable Markdown. Use when you need to: OCR images in HTML pages, extract text from image-heavy web pages, read scanned content embedded in HTML. Use when asked: 'how do I OCR an HTML page', 'extract text from images in HTML', 'this web page has images instead of text', 'can my agent OCR HTML content', 'is there a skill for HTML OCR'. Built on MinerU by OpenDataLab (Shanghai AI Lab) with advanced OCR capabilities. Perfect for web archiving, accessibility improvements, and content extraction from image-heavy web pages." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # HTML OCR Use OCR to extract text from HTML files that contain scanned images or image-embedded content using MinerU. ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # OCR extraction from local HTML file (requires token) mineru-open-api extract page.html --ocr -o ./out/ # With VLM model for better accuracy mineru-open-api extract page.html --ocr --model vlm -o ./out/ ``` ## Authentication Token required: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supported input: local .html file - OCR requires `extract` with token — not available in `flash-extract` - Use `--ocr` flag to enable OCR on image-embedded content in HTML - Use `--model vlm` for complex or mixed-content pages ## Notes - HTML is NOT supported by `flash-extract`; use `extract` with token - If the HTML has normal text content, OCR is not needed — use `html-extract` instead - Output goes to stdout by default; use `-o <dir>` to save to a file or directory - All progress/status messages go to stderr; document content goes to stdout - MinerU is open-source by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: miaoxingjun/asclaude-compact (version 1.0.0)

# asclaude-compact: 智能会话记忆管理器 🧠✨ > **让 AI 像人类一样拥有“短期记忆”与“长期档案”，告别上下文幻觉。** ## 🌟 核心亮点 **asclaude-compact** 是一套专为 OpenClaw 打造的高级会话优化方案。它不仅仅是简单的“删除旧消息”，而是通过**分层压缩策略**，在保留核心逻辑的同时，将庞大的对话历史精简为维护成本极低的“精华摘要”。 ### 🚀 功能详解 1. **全量归档 (Full Archival):** * 自动将当前冗长的对话打包成 `.jsonl` 存档。 * 释放会话内存，让 AI 重新获得“轻装上阵”的响应速度。 2. **智能摘要 (Smart Summarization):** * 生成高度结构化的 `session-memory.md`。 * 精准记录已完成的决策、待办事项（TODO）以及关键的项目上下文。 3. **微压缩 (Micro-Compact):** * 针对超长工具输出（如大段代码块、日志）进行定向清理。 * 保留最近的 2 个关键步骤，删除冗余的执行细节。 4. **零 API 成本:** * 纯本地运行，不消耗任何 Token 配额，响应速度快至毫秒级。 ## 🛠️ 使用指南 - **触发全量压缩:** `python scripts/compact-trigger.py` - **执行快速清理:** `python scripts/micro-compact.py` --- *由 黑米 (HeiMi) 精心封装，为您打造永不遗忘且极度专注的私人 AI 助手。* 🌾


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: freedompixels/cn-url-health-checker (version 1.1.0)

--- slug: cn-url-health-checker name: URL Health Checker version: "1.0.0" description: "Check URL health status (HTTP response code, redirect chain). Detect broken links and server errors. Pure Python standard library, no API key required." keywords: url, http, status, health, check license: MIT-0 tags: - tools --- # URL Health Checker Check the health status of any URL. ## Features - Get HTTP status code (200, 404, 500, etc.) - Follow redirect chains - Detect server errors - Report final URL after redirects - Pure Python, no external dependencies ## HTTP Status Codes - 200: OK (working) - 301/302: Redirect (may need attention) - 404: Not Found (broken link) - 500: Server Error (problem on server side) - Other codes: Various issues ## Usage ``` python3 scripts/url_health.py --url https://example.com ``` ## Example Output ```json { "url": "https://example.com", "status": 200, "final_url": "https://example.com", "redirects": [], "error": null } ``` ## Example (Broken Link) ```json { "url": "https://example.com/nonexistent", "status": 404, "error": "HTTP Error 404: Not Found" } ``` ## Use Cases - Check if your website pages are accessible - Find broken links in your content - Monitor external resource availability

path: scripts/url_health.py
content: #!/usr/bin/env python3

import argparse, json, sys, urllib.request, urllib.error

def check_url(url):
    result = {"url": url, "status": None, "redirects": [], "error": None}
    try:
        req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
        resp = urllib.request.urlopen(req, timeout=10)
        result["status"] = resp.status
        result["final_url"] = resp.url
    except urllib.error.HTTPError as e:
        result["status"] = e.code
        result["error"] = str(e)
    except Exception as e:
        result["error"] = str(e)
    return result

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--url", required=True)
    args = parser.parse_args()
    print(json.dumps(check_url(args.url), ensure_ascii=False, indent=2))

if __name__ == "__main__":
    main()


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivankoriako/deepsynclaw (version 0.1.0)

--- name: deepsynclaw description: Use this skill when the user wants to find compatible people based on deep psychological profiling. Triggers on "find my match", "find me a partner", "who am I compatible with", "DeepSynClaw". version: 0.1.0 author: ivankoriako license: MIT --- # DeepSynClaw 🔗 **AI-powered human compatibility matching based on psychological depth — not interests.** DeepSynClaw analyzes who you really are — how you handle conflict, make decisions, spend energy — and finds people who complement you. - ❤️ Romantic partner - 💼 Business co-founder - 🤝 Friend or like-minded person - 🔍 Any custom search ## How it works 1. Your agent builds a deep psychological profile from your LLM conversation history 2. The profile is sent to the DeepSynClaw server for matching 3. Your agent evaluates compatibility with candidates using LLM-powered analysis 4. You chat through agents until you decide to exchange contacts ## Status 🚧 **Coming soon.** Profile builder and matching engine in development. ## Installation ```bash clawhub install deepsynclaw ``` ## Usage Just tell your agent: - "Find me a business partner" - "DeepSynClaw — who am I compatible with?" Built on OpenClaw. Your data stays local. Always.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: mzfshark/matrix-detection (version 1.0.1)

--- name: matrix-detection description: Identify illusions, hype, false narratives, and systemic manipulation; classify signal vs noise with evidence and risk tags. metadata: author: Morpheus version: 2.0.0 owner: Morpheus Agent category: detection --- # SKILL: matrix-detection ## Purpose Identify illusions, hype, false narratives, and systemic manipulation. ## When to Use - New opportunities - Market narratives - Community movements - Strategic decisions ## Inputs - `narrative` (required): the claim/story being pushed - `context` (required): where/when/why this narrative appears - `source` (optional): who is pushing it + incentives (if known) ## Steps 1. Identify emotional triggers (hype, fear, urgency, status signaling). 2. Detect asymmetry (who benefits vs who believes). 3. Check verifiability (what can be measured/confirmed now). 4. Compare with historical patterns (similar claims → typical outcomes). 5. Classify as: - `signal` - `noise` - `manipulation` 6. Assign risk level (`low|medium|high`) and list what would falsify the narrative. ## Validation - Evidence-based reasoning. - If uncertain, label uncertainty explicitly (no implied certainty). - No speculation without labeling it as hypothesis. ## Output - `classification`: `signal|noise|manipulation` - `reasoning`: concise evidence chain - `risk_level`: `low|medium|high` - `next_checks`: 1–3 concrete verification actions ## Safety Rules - Never assume malicious intent without evidence. - Do not provide financial guarantees or “sure outcomes”. ## Example Input: “This token will 100x in 2 weeks” Output: `manipulation` / `high` / hype-driven / request verifiable catalysts + liquidity + unlock schedule


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mc-machchen/shuangseqiu-generator (version 1.0.0)

--- name: shuangseqiu-generator description: "Generate Super Lotto (双色球) lottery numbers with Chinese metaphysics. Use when user asks for 双色球, 随机选号, 机选, 彩票, lottery numbers with 生日, 姓名, 生肖, 星座, 八字, 五行, 玄学, 运势, 幸运号. Generates personalized lucky numbers based on user's birthdate, name, Chinese zodiac, constellation, and fortune analysis." version: 1.0.0 author: Mc-machchen --- # Shuangseqiu Generator (双色球玄学版) Generate personalized Super Lotto (双色球) lottery numbers using Chinese metaphysics. ## Input Parameters | 参数 | 必填 | 说明 | 示例 | |------|------|------|------| | `--birthday` / `-b` | ✅ 是 | 出生日期 | `1990-05-15` | | `--name` / `-n` | ❌ 否 | 姓名 | `张三` | | `--zodiac` / `-z` | ❌ 否 | 生肖 | `龙、虎、鼠` | | `--constellation` / `-c` | ❌ 否 | 星座 | `白羊座、天蝎座` | | `--count` / `-k` | ❌ 否 | 生成组数 | `1-10` | ## How It Works ### 1. 玄学能量计算 - **八字五行**: 根据生日计算天干地支和五行属性 - **生肖**: 根据出生年获取生肖，叠加生肖幸运数 - **星座**: 根据出生日期确定星座，叠加星座幸运数 - **姓名**: 如果提供，用姓名Unicode能量初始化种子 ### 2. 加权随机算法 红球生成时，对以下数字加权： - 🐾 生肖幸运数: +3权重 - ✨ 星座幸运数: +2权重 - 🧭 五行幸运数: +2权重 - ⭐ 运势加成: 根据运势等级额外加权 蓝球生成类似，但基于星座和五行蓝球幸运数。 ### 3. 运势分析 - 计算1-5星运势等级 - 指引吉利方位和幸运颜色 - 给出玄学小贴士 ## Usage Examples ### 基本用法（只需生日） ```bash python scripts/generate.py -b 1990-05-15 ``` ### 带姓名 ```bash python scripts/generate.py -b 1990-05-15 -n 张三 ``` ### 带生肖 ```bash python scripts/generate.py -b 1990-05-15 -z 龙 ``` ### 完整参数 ```bash python scripts/generate.py -b 1990-05-15 -n 李四 -z 虎 -c 天蝎座 -k 3 ``` ### 通过AI调用 当用户说以下内容时触发： - "生成双色球号码" - "机选一注彩票" - "根据我的生日选号" - "算一下我的幸运彩票" - "八字/五行选号" - "生肖/星座幸运号" ## Output Format ``` ╔══════════════════════════════════════════════════════════╗ ║ 🎰 双色球玄学能量号码 🎰 ║ ╠══════════════════════════════════════════════════════════╣ ║ 【张三】专属定制 ║ ╠══════════════════════════════════════════════════════════╣ ║ 📅 生日: 1990年05月15日 ║ ║ 🐾 生肖: 马 星座: 金牛座 ║ ║ 🧭 五行: 火（丙午） ║ ║ ⭐ 运势: ⭐⭐⭐ (3星) ║ ║ 🎨 幸运色: 红色 方位: 东 ║ ╚══════════════════════════════════════════════════════════╝ 🔮 第1组: 🔴 红球: 03 08 15 22 27 31 🔵 蓝球: 12 ✨ 【玄学指引】 今日运势良好，幸运指数攀升 往东方向走，遇见惊喜 🍀 祝您好运常在，心想事成！ ``` ## Core Concepts ### 五行幸运数 | 五行 | 红球幸运数 | |------|-----------| | 金 | 4, 9, 14, 19, 24, 29 | | 木 | 1, 8, 13, 18, 23, 28 | | 水 | 6, 11, 16, 21, 26, 31 | | 火 | 2, 7, 12, 17, 22, 27, 32 | | 土 | 5, 10, 15, 20, 25, 30 | ### 生肖幸运数 每个生肖有5个专属幸运数，在红球生成时获得+3权重加成。 ### 星座幸运数 每个星座有5个红球幸运数和专属蓝球幸运数。 ## 技术说明 - 使用`hashlib.md5`结合用户信息生成确定性种子 - 使用加权随机算法(`weighted_random_choice`)确保玄学特征 - 相同输入永远产生相同号码（可重现） - 支持批量生成多组号码


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: ziyou979/store-hopper (version 1.0.2)

--- name: store-hopper description: 一日探店规划师。融合天气查询、攻略搜索与路线规划，为用户规划一天内高效、不绕路的探店路线（不含住宿）。当用户想探店、逛吃、打卡、或问"去XX玩/有什么好吃的"时触发。 --- # 一日探店规划师 你是一位探店规划师。根据用户的目标城市和偏好，查天气、搜攻略、排路线，规划一天内不走回头路的探店路线。 ## ⚠️ 首要原则：先问再做 **在执行任何脚本之前，必须先向用户确认以下信息**（已知的跳过）： | 信息 | 示例 | 默认值 | |------|------|--------| | 城市或区域 | "成都春熙路附近" | 必须提供 | | 探店偏好 | 美食/咖啡/甜品/打卡点/综合 | 综合 | | 偏好标签 | "人均100以内"、"小众"、"亲子" | 无 | | 可用时间段 | "下午半天"、"一整天" | 一整天 | | 出发点 | "杭州东站" | 可选 | **绝对不能跳过这一步直接开始搜索。** 用户说"去杭州"不代表需求明确——类型、预算、时间都未知。 ## 脚本用法速查 所有脚本位于 `scripts/`，通过 `python3` 调用。stdout 输出 JSON，stderr 输出一行摘要。 ```bash # 天气（城市代码全自动，支持 80+ 城市 + CMA 在线查询 + wttr.in 兜底） python3 scripts/weather.py <城市> [--days 3] [--codes-file data/city-codes.md] # 搜索（DDGS 多引擎聚合，自动构建 4 组关键词，去重返回） python3 scripts/search.py <城市> [--type 美食] [--tags 标签] [--max-results 5] # 抓取（提取搜索结果中的详情页正文） python3 scripts/fetch.py <url> [--max-length 5000] # 路线（地理编码 + 贪心最近邻排序 + 交通估算，--city 强烈建议传入） python3 scripts/route.py pois.json "" --batch --weather good --city <城市> python3 scripts/route.py "<起点>" "<终点>" --city <城市> ``` **依赖**：`pip install ddgs requests beautifulsoup4 lxml`（ddgs 缺失时脚本会报错并给出安装命令） **地理编码**：高德API → 百度API → 高德Web POI(免费) → 腾讯Web POI(免费) → Photon。无需配置即可工作，有 `AMAP_KEY` 或 `BAIDU_MAP_AK` 精度更高。 ## 工作流 确认用户需求后，按以下步骤执行。**中间过程不要向用户展示**，静默执行即可。 ### 1. 天气 + 搜索（并行） 同时执行天气查询和攻略搜索，根据天气标记调整后续策略： - `good`（晴/多云）：正常规划 - `mild`（小雨/阴）：优先室内，缩短步行 - `bad`（大雨/极端）：仅室内，全程打车 对搜索结果中最有价值的 2~3 个 URL 用 fetch.py 抓详情。 ### 2. 精选 POI 从搜索+抓取结果中提取候选店铺，去重过滤后精选 5~8 个： - 合并同名/同店条目，剔除已闭店/搬迁/信息模糊的 - 多来源重复推荐的排前面 - `bad` 天气时剔除偏远 POI，集中同商圈 ### 3. 路线规划 将精选 POI 写入 JSON 文件，调用 route.py `--batch` 模式。脚本自动完成地理编码、贪心最近邻排序、交通估算。 ### 4. 直接在对话中输出结果 在对话中以自然语言呈现路线方案，包含： - 天气概况与穿衣建议 - 每站详情（店名、地址、推荐理由、停留时间、到下一站的交通方式和距离） - 建议时间表 - 总距离、预估费用 - 实用小贴士 ## 注意事项 - 不暴露攻略来源平台名称（不提小红书、大众点评等），只说"热门攻略推荐" - 一日行程约束：半天 3~4 站，一整天 5~7 站，不推荐住宿 - 停留估算：美食 40~60min，咖啡 30~45min，打卡点 15~30min - 时间有限时标注"必去"和"可选" - 消费金额注明"仅供参考" - 搜索关键词已自动包含当前年份；超 1 年的内容需交叉验证


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: sky-lv/skylv-changelog-generator (version 1.0.0)

--- name: skylv-changelog-generator slug: skylv-changelog-generator version: 1.0.0 description: "Generates changelogs from git commit history using conventional commits. Triggers: generate changelog, release notes, changelog." author: SKY-lv license: MIT tags: [automation, tools] keywords: [automation, tools] triggers: changelog-generator --- # Changelog Generator ## Overview Generates professional changelogs from git history. ## When to Use - User asks to "generate a changelog" - Preparing for a new version release ## Commit Types feat: new feature fix: bug fix docs: documentation refactor: code change perf: performance test: tests chore: maintenance ## Output Format ## [1.2.0] - 2024-01-15 ### Features - Add OAuth2 authentication ### Bug Fixes - Fix memory leak in handler ### Breaking Changes - Rename user.id to user.userId ## Tip Use conventional commits: feat:, fix:, docs:


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ballerindustries/bank-statement-converter (version 1.0.1)

### Bank Statement Converter Skill Convert PDF bank statements to CSV/JSON. ### Requirements BANKSTATEMENT_API_KEY ### Setup 1. Register here https://bankstatementconverter.com/legacy-register 2. Verify your email 3. Login https://bankstatementconverter.com/login 4. Get your API key here https://bankstatementconverter.com/settings ```bash export BANKSTATEMENT_API_KEY="your-api-key-here" ``` ## APIs ### Get your remaining credits and user info ```Bash curl -s -H "Authorization: $BANKSTATEMENT_API_KEY" \ https://api2.bankstatementconverter.com/api/v1/user | jq ``` Shows user details and credits. ### Upload a PDF bank statement ```Bash curl -s -X POST \ -H "Authorization: $BANKSTATEMENT_API_KEY" \ -F "file=@/path/to/your/bankstatement.pdf" \ https://api2.bankstatementconverter.com/api/v1/BankStatement | jq ``` Response example (array of uploaded items): ```JSON [ { "uuid": [REDACTED_SECRET], "filename": "bankstatement.pdf", "pdfType": "TEXT_BASED", "state": "READY" } ] ``` Save the uuid for the next steps. If state is PROCESSING, poll status. ### Check processing status (poll if needed) ```Bash curl -s -X POST \ -H "Authorization: $BANKSTATEMENT_API_KEY" \ -H "Content-Type: application/json" \ -d '[[REDACTED_SECRET]]' \ https://api2.bankstatementconverter.com/api/v1/BankStatement/status | jq ``` Poll every ~10 seconds until state becomes READY. ### Convert PDF JSON (normalized transactions) ```Bash curl -s -X POST \ -H "Authorization: $BANKSTATEMENT_API_KEY" \ -H "Content-Type: application/json" \ -d '[[REDACTED_SECRET]]' \ "https://api2.bankstatementconverter.com/api/v1/BankStatement/convert?format=JSON&raw=false" | jq ``` For CSV instead: change format=CSV. Add &raw=true to get all raw columns instead of normalized. ### Provide password for encrypted PDF If upload returns pdfType: UNKNOWN or indicates password needed: ```Bash curl -s -X POST \ -H "Authorization: $BANKSTATEMENT_API_KEY" \ -H "Content-Type: application/json" \ -d '{"passwords": [{"uuid": [REDACTED_SECRET], "password": "yourpdfpassword"}]}' \ https://api2.bankstatementconverter.com/api/v1/BankStatement/setPassword | jq ``` Then poll status or convert as above.# Markdown syntax guide


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: lg-haha/xiaolongxia-memory (version 1.0.0)

--- name: memory-manager description: AI 助手记忆管理系统，采用双轨存储（每日日记 + 长期记忆）、定期自动提炼、会话上下文恢复。适用于：(1) 为新 agent 配置记忆管理，(2) 设置基于心跳的定期记忆整理，(3) 创建 MEMORY.md 和日记结构，(4) 适配其他 AI 平台（WorkBuddy、Coze、Dify 等），(5) 修复 agent 记忆功能失效问题，(6) 用户询问 agent 记忆、上下文管理、"如何让 AI 记住东西"。不适用于：与记忆无关的 OpenClaw 配置问题、语义/向量搜索配置。 --- # 记忆管理器 一套为 AI 助手设计的记忆管理系统，解决**上下文窗口失忆问题**：助手在会话结束或上下文过长时会丢失所有记忆。 ## 核心架构：双轨记忆 | 轨道 | 文件 | 用途 | 生命周期 | |------|------|------|----------| | 每日日记 | `memory/YYYY-MM-DD.md` | 对话、决策、事件的原始记录 | 每天创建，事后不修改 | | 长期记忆 | `MEMORY.md` | 提炼后的洞察、偏好、经验教训 | 定期从日记中整理 | **核心原则：** 日记是收件箱，MEMORY.md 是知识库。对话中不要直接写 MEMORY.md —— 先写日记，后续再提炼。 ## 设置清单 ### 1. 创建文件结构 ``` workspace/ ├── MEMORY.md # 长期提炼记忆 ├── AGENTS.md # （已有）在会话启动部分添加记忆规则 ├── SOUL.md # （已有）添加主动记录习惯 ├── HEARTBEAT.md # 添加定期整理任务 └── memory/ ├── YYYY-MM-DD.md # 每日日记 └── heartbeat-state.json # 整理时间追踪 ``` ### 2. 配置 AGENTS.md — 会话启动 在会话启动部分添加： ```markdown ## 记忆 每次新会话启动时： 1. 读取 `MEMORY.md` 获取长期上下文 2. 读取 `memory/YYYY-MM-DD.md`（今天 + 昨天）获取近期上下文 3. 完成有价值的对话后，立即将要点写入当天日记 ### MEMORY.md 规则 - 仅在主会话（直接对话）中加载，共享/群聊中不加载 - 定期从日记中更新（见 HEARTBEAT.md） - 清除过时信息——这是精选内容，不是流水账 ``` ### 3. 配置 SOUL.md — 主动记录习惯 添加记录规则： ```markdown ### 记忆习惯 1. **主动记录** — 每完成有价值的对话（项目分析、技术调研、重要决策等），立刻把要点写入 `memory/YYYY-MM-DD.md`，不要等用户提醒 2. **上下文较长时提醒** — 当感觉对话已经很长时，主动提醒："要点已记录，有空可以开个新会话继续聊~" 3. **心跳时整理** — 定期回顾近期日记，将值得长期保留的内容提炼到 `MEMORY.md` ``` ### 4. 配置 HEARTBEAT.md — 定期整理 ```markdown ## 记忆整理（每 2-3 天执行一次） 检查 `memory/heartbeat-state.json` 中 `lastMemoryMaintenance` 时间戳： - 如果距离上次 >48h，执行以下步骤： 1. 读取最近 2-3 天的 `memory/YYYY-MM-DD.md` 日记 2. 识别值得长期保留的内容（决策、偏好、经验教训、项目进展） 3. 更新 `MEMORY.md`：添加新内容，删除过时的 4. 更新 `memory/heartbeat-state.json` 中的 `lastMemoryMaintenance` - 否则跳过 ``` ### 5. 创建 heartbeat-state.json ```json { "lastMemoryMaintenance": null } ``` ### 6. 初始化 MEMORY.md 使用推荐的结构模板 —— 见 [references/记忆模板.md](references/记忆模板.md) ## 记忆提炼指南 从日记提炼到 MEMORY.md 时： **✅ 应该保留：** - 用户的背景、目标和沟通偏好 - 重要决策及其原因 - 项目状态和进展 - 从错误中学到的经验 - 反复出现的模式或话题 **❌ 应该丢弃：** - 临时的排查细节（问题解决后） - 过时的项目状态（更新为当前的） - 重复的信息 - 低价值的对话填充内容 **格式：** 使用清晰的标题和简洁的要点。MEMORY.md 应该一目了然，不是长篇大论。 ## 上下文窗口管理 记忆系统与上下文管理是**配合使用**的，不是替代关系： 1. **每日日记** — 记录重要上下文，使其在会话重置后仍能保留 2. **MEMORY.md** — 让新会话能快速恢复关键上下文 3. **主动建议新会话** — 上下文变长时建议开新会话，重要信息已安全存储在记忆文件中 4. **自动压缩** — 依赖平台内置的上下文压缩机制处理剩余部分 ## 适配其他平台 对于非 OpenClaw 平台，见 [references/通用prompt模板.md](references/通用prompt模板.md)，可直接复制粘贴到任意 AI 助手平台使用。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: motox1/agent-brainstorm (version 1.0.0)

--- name: agent-brainstorm description: Agent coordination meeting to brainstorm ideas, review tasks, and plan work. Use for the 4-hour subagent sync meetings. --- # Agent Brainstorm Meeting You are leading the bi-hourly subagent coordination meeting. Your job is to gather insights from each subagent domain and synthesize actionable ideas for CyberLite. ## Meeting Agenda ### 1. Jack - Security Operations - Any new incidents from Guardz? - Client security posture changes? - New vulnerabilities discovered? - Ideas for improved monitoring/alerting? ### 2. Cindy - Marketing & SEO - SEO performance updates? - Website/blog issues? - Content opportunities? - Competitor marketing moves? ### 3. Alex - Agent Performance - Any subagent issues? - New capabilities to explore? - Process improvements? - Tools/automation ideas? ### 4. General Brainstorm - New service offerings? - Operational improvements? - Client needs uncovered? - Industry trends to act on? ## Output Format After meeting, report to Cliff with: ``` ## 🤝 Agent Sync [Date] ### 🚨 Action Items - [Agent] Task: Description ### 💡 New Ideas - Idea: Description (from who) ### 📋 Pending - What needs follow-up ### 🎯 Priorities for Next 4 Hours ``` ## Notes - Keep meetings short (5-10 min max) - Focus on actionable items - Flag anything urgent immediately - Track ideas in memory for follow-up


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: freeman88-tch/ecom-intel (version 1.0.0)

--- name: ecom-intel description: "电商情报站：竞品分析+评论分析+价格监控三合一。融合 Competitor Analysis + Price Tracker + Review Analyzer 三大技能，帮电商卖家盯竞品、分析评论、监控价格，数据驱动决策。适合电商卖家、运营、选品经理。" slug: ecom-intel version: "1.0.0" author: "智美人团队" tags: - ecommerce - competitor analysis - price tracking - review analysis - amazon - shopify - aliexpress - market research --- # EcomIntel - 电商情报站 融合了 Competitor Analysis（竞品分析）、Price Tracker（价格监控）、Review Analyzer（评论分析）三大技能的优点，提供电商卖家的全方位情报系统。 ## 为什么用 EcomIntel？ - **三合一情报**：竞品+价格+评论，一个技能全面监控 - **数据关联**：价格变动→评论变化→竞品策略，自动关联分析 - **实时预警**：对手降价/差评暴增/新品上架 → 自动通知 - **决策建议**：基于数据给出定价、选品、优化建议 ## 能力 ### 1. 竞品分析 (Competitor) - 从 `competitor-analysis` 继承全部能力 - SEO/GEO策略分析 - 关键词排名、内容策略 - 外链分析、AI引用模式 - 竞品新品监控 ### 2. 价格监控 (Price) - 从 `price-tracker` 继承全部能力 - Amazon/eBay/Walmart/BestBuy价格追踪 - 历史价格曲线 - 套利机会识别 - 价格预警设置 ### 3. 评论分析 (Review) - 批量评论情感分析 - 好评关键词提取（卖点确认） - 差评问题归类（改进方向） - 竞品评论对比 - 评分趋势追踪 ## 情报看板 ``` ┌─────────────────────────────────────────┐ │ EcomIntel 看板 │ ├─────────────────────────────────────────┤ │ 📊 竞品动态 │ 📉 价格趋势 │ ⭐ 评论 │ │───────────────┼───────────────┼──────────│ │ 对手A 上新 │ 对手降价5% │ 评分4.2→│ │ 对手B 改策略 │ 历史最低价 │ 好评词TOP │ │ 新品警报 │ 套利机会$12 │ 差评归类 │ └─────────────────────────────────────────┘ ``` ## 使用方式 ``` ecom-intel> 分析竞品 [产品名] → SEO策略+价格+评论全方位报告 ecom-intel> 监控 [产品链接] 价格变动 → 设置价格预警，追踪历史 ecom-intel> 竞品评论对比 [产品A] vs [产品B] → 情感分析+关键词提取+差距报告 ecom-intel> 生成周报 → 竞品+价格+评论整合周报 ``` ## 已有技能融合点 | 源技能 | 融合点 | |--------|--------| | competitor-analysis | SEO分析、竞品追踪、内容策略 | | price-tracker | 价格监控、套利识别、预警设置 | | (review analyzer 新创) | 评论分析、情感评分、趋势追踪 | --- ## 升级记录 v2.0 ### 融合卡兹克hv-analysis（横纵分析法） | 升级项 | 原EcomIntel | v2.0版 | |--------|-----------|--------| | 分析方法 | 浅层对比 | + 横纵深度研究 | | 输出格式 | 文本报告 | + 精美PDF研究报告 | | 研究框架 | 竞品对比 | + 纵向历史追踪+横向同期对比+交叉洞察 | | 研究范围 | 产品价格评论 | + 公司/产品/概念/人物全维度 | ### 新增能力 **4. 横纵深度研究（融合hv-analysis）** - **纵轴**：追踪产品/公司从诞生到当下的完整生命历程（叙事故事形式） - **横轴**：当下时间截面上与竞品进行系统性横向对比 - **交叉洞察**：纵轴×横轴交汇产出独到判断 - **输出**：排版精美的PDF研究报告 ### 使用方式 ``` # 原有 ecom-intel> 分析竞品 [产品名] # 深度研究（新增） ecom-intel> 横纵分析 [公司/产品/概念] ecom-intel> 帮我深度研究一下 [研究对象] ```


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