Skill: bustes01/knowledge-and-trends-engine (version 1.0.0)

--- name: knowledge-and-trends-engine description: > Knowledge accumulation and tech trend analysis engine. Periodically summarizes learned concepts from user interactions, parses content from videos/articles/images shared by user, researches latest tech/news trends, and self-iterates. Triggers: "summarize what we discussed", "learn from this article", "analyze this video", "what's new in tech", "create a knowledge summary", or periodic scheduled reviews. version: 1.0.0 metadata: openclaw: emoji: "📡" homepage: https://clawhub.ai/BusTes01/knowledge-and-trends-engine models: - gpt-4 - deepseek-v4-flash - gemini-2.0-flash - claude-4-opus requires: skills: - complex-memory-manager - self-iteration-engine --- # 📡 Knowledge & Trends Engine Knowledge accumulation and tech trend analysis engine. Periodically summarizes learned concepts from user interactions, parses content from shared videos/articles/images, researches latest tech/news trends, and self-iterates via the shared component skills. ## Core Workflows ### Workflow 1: Concept Summarization (On-demand) User says: "summarize what we've discussed recently" or "帮我总结最近聊过的概念" **Step 1: Gather Memory Sources** - Read `memory/tier1-public/` for all skill stats and public knowledge entries - Read `memory/concepts/` for concept files stored from previous sessions - Read recent daily notes: `memory/YYYY-MM-DD.md` (last 7 days) **Step 2: Identify Distinct Concepts** Scan all sources and extract unique concepts. For each concept, determine: - **Category**: AI/ML, Finance, Development, Tools, Business, Science, etc. - **Maturity**: new / explored / mastered - **Related concepts**: cross-links to other learned concepts - **Source**: conversation, article, video, image, or self-discovered **Step 3: Generate Summary** ```markdown # 📡 Knowledge Summary · YYYY-MM-DD ## 🆕 New This Period ### Concept A - Source: conversation about financial modeling - Key points: {3-5 bullet points} - Related: Concept B, Concept C - Status: explored ✓ ## 📚 Concepts in Progress ### Concept D - Last discussed: YYYY-MM-DD - Progress: understand basics, need deeper dive - Suggested next: look into {related topic} ## 🏆 Mastered Concepts ### Concept E - Sessions covered: 5 - Last reviewed: YYYY-MM-DD - Confident: yes ``` **Step 4: Store** Use `complex-memory-manager` to store the summary: - T1: `memory/tier1-public/concepts-summary-YYYY-MM.md` (concept names, relationships, categories) - T2: `memory/tier2-internal/concepts-detail-YYYY-MM.md` (detailed notes, sources, encrypted if personal) ### Workflow 2: Parse External Content (On-demand) User shares content: "watch this video", "read this article", "analyze this image", "这个概念你记住" **Step 1: Content Analysis** - For **articles** (`web_fetch` URL): extract key concepts, arguments, data points - For **videos** (if URL to YouTube/transcript): extract main thesis, examples, conclusions - For **images**: describe visual content, extract any text, identify key concepts - For **direct concept explanation**: parse the user's textual explanation **Step 2: Concept Structuring** For each extracted concept, create a structured note: ```yaml # memory/concepts/<concept-slug>.md concept: name: "<concept name>" category: "<category>" source: type: article | video | image | conversation url: "<source URL if applicable>" date: "<YYYY-MM-DD>" summary: "<2-3 sentence explanation>" key_points: - "<point 1>" - "<point 2>" related_concepts: ["<concept A>", "<concept B>"] practical_applications: "<how this can be used>" ``` **Step 3: Cross-Link** - Check memory for existing related concepts - Add links in both directions - If concept already exists, merge/update rather than duplicate ### Workflow 3: Trend Research (Periodic / On-demand) User says: "what's new in tech" or "调研最新的技术趋势" **Step 1: Define Research Scope** - If user specified: use those keywords - If not: use recent concept categories from memory as seed topics - Always include: AI/ML, developer tools, security, finance tech **Step 2: Search & Gather** - Use `web_search` with targeted queries for each scope - Priority sources: tech blogs (TechCrunch, ArsTechnica), research papers (arXiv), release notes (GitHub), financial news (Bloomberg, Reuters) - Limit to last 7 days of content unless user specifies otherwise **Step 3: Trend Analysis** For each trend found: ```yaml trend: title: "<trend name>" category: "<category>" significance: high | medium | low description: "<1-2 sentence description>" impact: "<who/what this affects>" source: "<URL>" relation_to_existing: "<how this relates to known concepts>" ``` **Step 4: Learn & Store** - Store each significant new concept using Workflow 2 format - Update `memory/tier1-public/trends-DATE.md` with all findings - Use `self-iteration-engine` to log the research activity ### Workflow 4: Periodic Self-Review (Cron-driven) When triggered by schedule (default weekly): 1. **Review accumulated concepts** from `memory/concepts/` 2. **Run trend research** (Workflow 3) on categories where concepts are stored 3. **Generate combined summary** (Workflow 1) including new trends 4. **Identify knowledge gaps** — concepts mentioned in trends that have no existing entry 5. **Log iteration** via `self-iteration-engine` 6. **Propose learning topics** for next week based on gaps ## Memory Structure ``` memory/ ├── tier1-public/ │ ├── concepts-summary-YYYY-MM.md # Monthly concept overview (T1) │ └── trends-YYYY-MM-DD.md # Trend research results (T1) ├── tier2-internal/ │ └── concepts-detail-YYYY-MM.md # Detailed encrypted notes (T2) ├── concepts/ │ ├── <concept-slug>.md # Individual concept files │ └── INDEX.md # Master index of all concepts └── usage-logs/ └── knowledge-and-trends-engine.md # Delegated to self-iteration-engine ``` ## Query Examples ``` "最近我们聊过什么来着？" → Workflow 1 (concept summarization) "看看这篇https://... 帮我提炼核心概念" → Workflow 2 (content parse) "最近AI领域有什么新动向" → Workflow 3 (trend research) "定期总结" → Workflow 4 (periodic review) "这个概念你记住" + explanation → Workflow 2, Step 2-3 (direct store) ``` --- # 📡 知识趋势引擎 知识积累与技术趋势分析引擎。定期总结与用户讨论过的概念，解析用户分享的视频/文章/图片内容，调研最新技术与新闻热点，并通过共享组件技能实现自迭代。 ## 核心工作流 ### 工作流1：概念总结（按需） 用户说："总结最近聊过的概念" **第一步：收集记忆源** - 读取 `memory/tier1-public/` 中的技能统计和公开知识 - 读取 `memory/concepts/` 中的概念文件 - 读取最近7天的每日笔记 **第二步：识别独立概念** 扫描所有源提取唯一概念，判断：类别、成熟度、关联概念、来源 **第三步：生成总结** 按以下结构输出： - 🆕 本期新概念 - 📚 进行中的概念 - 🏆 已掌握的概念 **第四步：存储** 委托 `complex-memory-manager` 存储总结 ### 工作流2：解析外部内容（按需） 用户分享内容时：文章URL、视频URL、图片、或直接概念解释 **第一步：内容分析** - 文章 → `web_fetch` 提取关键概念、论据、数据 - 视频 → 如有文字稿则提取主旨、示例、结论 - 图片 → 描述视觉内容，提取文字，找出关键概念 - 直接解释 → 解析用户的文字说明 **第二步：概念结构化** 每个概念创建结构化笔记，包括名称、类别、来源、摘要、要点、关联概念、实际应用 **第三步：交叉链接** 检查已有概念，双向链接；若已存在则合并/更新而非重复 ### 工作流3：趋势调研（定期/按需） 用户说："最近有什么技术热点" **第一步：确定调研范围** 使用用户指定关键词或已有概念类别作为种子 **第二步：搜索收集** `web_search` 定向搜索，优先来源：TechCrunch、ArsTechnica、arXiv、GitHub、Bloomberg、Reuters **第三步：趋势分析** 对每个趋势记录：标题、类别、重要性、描述、影响、来源、与现有概念的关系 **第四步：学习与存储** 使用工作流2格式存储新概念，更新趋势文件 ### 工作流4：定期自审（Cron驱动） 默认每周执行： 1. 审查 `memory/concepts/` 中的积累概念 2. 在有概念存储的类别上运行趋势调研 3. 生成包含新趋势的合并总结 4. 识别知识盲区 5. 通过 `self-iteration-engine` 记录迭代 6. 基于盲区提出下周学习主题 ## 记忆结构 ``` memory/ ├── tier1-public/ │ ├── concepts-summary-YYYY-MM.md # 月度概念概览（公开） │ └── trends-YYYY-MM-DD.md # 趋势调研结果（公开） ├── tier2-internal/ │ └── concepts-detail-YYYY-MM.md # 详细加密笔记（内部） ├── concepts/ │ ├── <概念slug>.md # 独立概念文件 │ └── INDEX.md # 概念总索引 └── usage-logs/ └── knowledge-and-trends-engine.md # 由self-iteration-engine管理 ``` ## 查询示例 ``` "最近我们聊过什么来着？" → 工作流1（概念总结） "看看这篇https://... 帮我提炼核心概念" → 工作流2（内容解析） "最近AI领域有什么新动向" → 工作流3（趋势调研） "定期总结" → 工作流4（定期自审） "这个概念你记住" + 解释 → 工作流2（直接存储）


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