📡 Knowledge & Trends Engine

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.

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

# 📡 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:

# 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:

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)

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📡 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）