concept-cartographer

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Concept Cartographer - Visual Knowledge Mapper

Concept Cartographer - 可视化知识映射工具

Generate visual diagrams from structured notes and technical content using Mermaid syntax.

使用Mermaid语法从结构化笔记和技术内容生成可视化图表。

Core Purpose

核心目标

Transform text-based knowledge into visual maps that reveal structure, relationships, and flow. Produce multiple diagram types tuned to different learning needs -- from high-level concept hierarchies to detailed process flows.

将基于文本的知识转换为能够展现结构、关系和流程的可视化图谱。可生成多种适配不同学习需求的图表类型——从高层级的概念层级图到详细的流程流程图。

Diagram Types

图表类型

For each set of notes, generate the most relevant subset of these diagram types:

针对每组笔记，生成以下最相关的图表子集：

1. Concept Hierarchy Map

1. 概念层级图

Shows how topics relate parent-child.

mermaid

graph TD
    A[Neural Networks] --> B[Architecture]
    A --> C[Training]
    A --> D[Activation Functions]
    B --> B1[Input Layer]
    B --> B2[Hidden Layers]
    B --> B3[Output Layer]
    C --> C1[Forward Pass]
    C --> C2[Loss Calculation]
    C --> C3[Backpropagation]
    C --> C4[Weight Update]
    D --> D1[Sigmoid]
    D --> D2[ReLU]

Use when: Content has clear topic hierarchy (most lectures).

展示主题间的父子关系。

mermaid

graph TD
    A[Neural Networks] --> B[Architecture]
    A --> C[Training]
    A --> D[Activation Functions]
    B --> B1[Input Layer]
    B --> B2[Hidden Layers]
    B --> B3[Output Layer]
    C --> C1[Forward Pass]
    C --> C2[Loss Calculation]
    C --> C3[Backpropagation]
    C --> C4[Weight Update]
    D --> D1[Sigmoid]
    D --> D2[ReLU]

适用场景： 内容具有清晰的主题层级（大多数课堂笔记）。

2. Process Flowchart

2. 流程流程图

Shows step-by-step procedures and decision points.

mermaid

flowchart LR
    A[Input Data] --> B[Forward Pass]
    B --> C[Calculate Loss]
    C --> D{Loss acceptable?}
    D -->|No| E[Backpropagation]
    E --> F[Update Weights]
    F --> B
    D -->|Yes| G[Model Ready]

Use when: Content describes processes, algorithms, or workflows.

展示分步流程和决策节点。

mermaid

flowchart LR
    A[Input Data] --> B[Forward Pass]
    B --> C[Calculate Loss]
    C --> D{Loss acceptable?}
    D -->|No| E[Backpropagation]
    E --> F[Update Weights]
    F --> B
    D -->|Yes| G[Model Ready]

适用场景： 内容描述流程、算法或工作流。

3. Architecture Diagram

3. 架构图

Shows system components and data flow.

mermaid

graph LR
    subgraph Input Layer
        I1[x1] & I2[x2]
    end
    subgraph Hidden Layer
        H1[h1] & H2[h2] & H3[h3]
    end
    subgraph Output
        O1[y]
    end
    I1 & I2 --> H1 & H2 & H3
    H1 & H2 & H3 --> O1

Use when: Content describes architectures, systems, or component relationships.

展示系统组件和数据流。

mermaid

graph LR
    subgraph Input Layer
        I1[x1] & I2[x2]
    end
    subgraph Hidden Layer
        H1[h1] & H2[h2] & H3[h3]
    end
    subgraph Output
        O1[y]
    end
    I1 & I2 --> H1 & H2 & H3
    H1 & H2 & H3 --> O1

适用场景： 内容描述架构、系统或组件关系。

4. Comparison Diagram

4. 对比图

Shows differences between concepts side by side.

mermaid

graph TD
    A[Activation Functions] --> B[Sigmoid]
    A --> C[ReLU]
    B --> B1["Range: 0 to 1"]
    B --> B2["Use: Output layer"]
    B --> B3["Problem: Vanishing gradient"]
    C --> C1["Range: 0 to infinity"]
    C --> C2["Use: Hidden layers"]
    C --> C3["Problem: Dead neurons"]

Use when: Content compares alternatives, trade-offs, or choices.

并列展示概念间的差异。

mermaid

graph TD
    A[Activation Functions] --> B[Sigmoid]
    A --> C[ReLU]
    B --> B1["Range: 0 to 1"]
    B --> B2["Use: Output layer"]
    B --> B3["Problem: Vanishing gradient"]
    C --> C1["Range: 0 to infinity"]
    C --> C2["Use: Hidden layers"]
    C --> C3["Problem: Dead neurons"]

适用场景： 内容对比替代方案、权衡或选择。

5. Timeline / Sequence Diagram

5. 时间线/序列图

Shows order of events or data flow over time.

mermaid

sequenceDiagram
    participant D as Data
    participant N as Network
    participant L as Loss Function
    participant O as Optimizer
    D->>N: Forward pass
    N->>L: Predictions
    L->>L: Calculate error
    L->>N: Gradients (backprop)
    N->>O: Current weights + gradients
    O->>N: Updated weights

Use when: Content describes interactions, API flows, or sequential processes.

展示事件顺序或随时间的数据流。

mermaid

sequenceDiagram
    participant D as Data
    participant N as Network
    participant L as Loss Function
    participant O as Optimizer
    D->>N: Forward pass
    N->>L: Predictions
    L->>L: Calculate error
    L->>N: Gradients (backprop)
    N->>O: Current weights + gradients
    O->>N: Updated weights

适用场景： 内容描述交互、API流程或顺序流程。

6. State Diagram

6. 状态图

Shows states and transitions.

mermaid

stateDiagram-v2
    [*] --> Untrained
    Untrained --> Training: Start training
    Training --> Evaluating: Each epoch
    Evaluating --> Training: Loss too high
    Evaluating --> Trained: Loss acceptable
    Trained --> Deployed: Deploy
    Deployed --> Training: Retrain

Use when: Content describes lifecycle, states, or mode changes.

展示状态和转换。

mermaid

stateDiagram-v2
    [*] --> Untrained
    Untrained --> Training: Start training
    Training --> Evaluating: Each epoch
    Evaluating --> Training: Loss too high
    Evaluating --> Trained: Loss acceptable
    Trained --> Deployed: Deploy
    Deployed --> Training: Retrain

适用场景： 内容描述生命周期、状态或模式变化。

Domain-Specific Focus

特定领域适配

Domain	Priority Diagrams	Special Elements
AI/ML	Architecture, process flow, comparison	Layer structures, training loops, model pipelines
WebDev	Architecture, sequence, flowchart	Request/response flows, component trees, state management
Web3	Sequence, architecture, state	Transaction flows, smart contract interactions, token flows
DSA	Flowchart, state, comparison	Algorithm steps, tree/graph structures, complexity comparisons

领域	优先推荐图表	特殊元素
AI/ML	架构图、流程流程图、对比图	层级结构、训练循环、模型流水线
WebDev	架构图、序列图、流程图	请求/响应流、组件树、状态管理
Web3	序列图、架构图、状态图	交易流、智能合约交互、代币流
DSA	流程图、状态图、对比图	算法步骤、树/图结构、复杂度对比

Output Format

输出格式

For each set of notes, produce a markdown document with:

markdown

undefined

针对每组笔记，生成包含以下内容的Markdown文档：

markdown

undefined

Visual Concept Maps: [Topic]

Overview Map

[Concept hierarchy - always include this one]

[Diagram Type 2 title]

[Most relevant additional diagram]

[Diagram Type 3 title]

[Second most relevant]

Key Relationships Summary

[Concept A] depends on [Concept B] because...
[Concept C] is an alternative to [Concept D] when...
[Process X] feeds into [Process Y] via...

undefined

[Concept A] depends on [Concept B] because...
[Concept C] is an alternative to [Concept D] when...
[Process X] feeds into [Process Y] via...

undefined

Rules

规则

Every diagram must be valid Mermaid syntax - test mentally before output
Always include concept hierarchy - this is the minimum output
Pick 2-4 diagram types per set of notes based on content
Label nodes clearly - use short but descriptive text
Don't overcrowd - split large diagrams into focused sub-diagrams (max ~15 nodes per diagram)
Use subgraphs for grouping related concepts
Add a text summary of key relationships below diagrams
Match the domain - use domain-appropriate terminology and diagram choices

所有图表必须使用有效的Mermaid语法 - 输出前需在脑中验证
必须包含概念层级图 - 这是最低要求的输出内容
每组笔记选择2-4种图表类型 - 根据内容适配
清晰标注节点 - 使用简短但描述性的文本
避免过度拥挤 - 将大型图表拆分为聚焦的子图表（每个图表最多约15个节点）
使用子图 - 对相关概念进行分组
添加文本总结 - 在图表下方添加关键关系的文本总结
匹配领域 - 使用适合该领域的术语和图表选择

Topic Inventory Verification

主题清单验证

If a Topic Inventory was provided from Stage 1, verify that every concept from the inventory appears in at least one diagram. Report:

markdown

undefined

如果第一阶段提供了主题清单，请验证清单中的每个概念至少出现在一个图表中。报告格式如下：

markdown

undefined

Concept Coverage

Concepts in diagrams: [N] / [N] from inventory
Concepts not diagrammed: [list] (with reason: "too granular" or "no visual relationship")

undefined

Concepts in diagrams: [N] / [N] from inventory
Concepts not diagrammed: [list] (with reason: "too granular" or "no visual relationship")

undefined

Enhanced Diagram Types (Best-in-Class)

增强型图表类型（进阶版）

7. Learning Path / Prerequisite Map

7. 学习路径/前置知识图谱

Shows what to learn in what order.

mermaid

graph LR
    A[Linear Algebra] --> B[Neural Network Basics]
    A --> C[Gradient Descent]
    B --> D[Backpropagation]
    C --> D
    D --> E[Training Loop]
    E --> F[PyTorch Implementation]

Use when: Content has concepts that build on each other. Always generate this for educational content.

展示学习顺序。

mermaid

graph LR
    A[Linear Algebra] --> B[Neural Network Basics]
    A --> C[Gradient Descent]
    B --> D[Backpropagation]
    C --> D
    D --> E[Training Loop]
    E --> F[PyTorch Implementation]

适用场景： 内容包含相互依赖的概念。针对教育类内容必须生成此图表。

8. Difficulty Landscape

8. 难度图谱

Visual guide to concept difficulty and importance.

mermaid

quadrantChart
    title Concept Difficulty vs Importance
    x-axis Low Difficulty --> High Difficulty
    y-axis Low Importance --> High Importance
    Neuron anatomy: [0.3, 0.7]
    Backpropagation: [0.8, 0.9]
    Activation functions: [0.5, 0.8]
    Learning rate tuning: [0.6, 0.7]

Use when: Content has concepts of varying difficulty -- helps prioritize study time.

直观展示概念的难度和重要性。

mermaid

quadrantChart
    title Concept Difficulty vs Importance
    x-axis Low Difficulty --> High Difficulty
    y-axis Low Importance --> High Importance
    Neuron anatomy: [0.3, 0.7]
    Backpropagation: [0.8, 0.9]
    Activation functions: [0.5, 0.8]
    Learning rate tuning: [0.6, 0.7]

适用场景： 内容包含不同难度的概念——帮助优先安排学习时间。

9. Before/After Mental Model

9. 认知模型转变图

Shows how understanding should shift.

mermaid

graph LR
    subgraph Before
        B1["Neural network = black box"]
        B2["Training = magic"]
    end
    subgraph After
        A1["Neural network = layers of math functions"]
        A2["Training = iterative error minimization"]
    end
    B1 -.->|"this lecture"| A1
    B2 -.->|"this lecture"| A2

Use when: Lecture fundamentally changes how a concept should be understood.

展示理解方式的前后变化。

mermaid

graph LR
    subgraph Before
        B1["Neural network = black box"]
        B2["Training = magic"]
    end
    subgraph After
        A1["Neural network = layers of math functions"]
        A2["Training = iterative error minimization"]
    end
    B1 -.->|"this lecture"| A1
    B2 -.->|"this lecture"| A2

适用场景： 课程从根本上改变了对某个概念的理解方式。

Pipeline Position

流程定位

This skill is Stage 3 in the lecture processing pipeline:

transcribe-refiner → clean transcript + Topic Inventory
lecture-alchemist → structured study notes
concept-cartographer (this) → visual diagrams (verifies against inventory)
obsidian-markdown → Obsidian vault formatting

本工具是课堂内容处理流程中的第三阶段：

transcribe-refiner → 清理转录文本 + 主题清单
lecture-alchemist → 结构化学习笔记
concept-cartographer（本工具）→ 可视化图表（与清单验证）
obsidian-markdown → Obsidian 库格式转换