wavemind

分类: 数据与AI | 上传者: lilyzhng | 下载: 0 | 版本: v1.0（最新）
将思维产物（对话记录、头脑风暴笔记）转变为美丽的视觉思维演化图。捕捉、可视化并列出您的思维过程。
更新日志: Source: GitHub https://github.com/lilyzhng/WaveMind
目录结构

当前层级: tree/main/
📁 data/
- 📁 artifacts/
  - 📄 glm-benchmark.md 16.9 KB
- 📁 visuals/
  - 📄 glm-benchmark.html 36.7 KB
- 📄 .gitignore 189 B
📁 examples/
- 📄 glm-benchmark-visual.png 465.2 KB
- 📄 teaser.png 90.7 KB
- 📄 wavemind-design-evolution.png 297.1 KB
📁 lib/
- 📄 capture.sh 2.1 KB
- 📄 store.sh 2.2 KB
- 📄 visualize.sh 1.6 KB
📄 CLAUDE.md 2.5 KB
📄 LICENSE 1.0 KB
📄 README.md 3.1 KB
📄 SKILL.md 7.9 KB
SKILL.md

---
name: wavemind
description: Turn thinking artifacts (conversation transcripts, brainstorm notes) into beautiful visual thought evolution maps. Capture, visualize, and list your thinking process.
argument-hint: capture [filepath] | visualize <id> | list
allowed-tools: Read, Write, Edit, Glob, Grep, Bash
---

# WaveMind: Thought Capture + Visualization

Transform thinking artifacts into beautiful visual maps of how your ideas evolved.

## Commands

### `/wavemind capture [filepath]`
Capture a thinking artifact. Two modes:

**Mode 1: Import existing file** (`/wavemind capture <filepath>`)
1. Read the file at `<filepath>`
2. Analyze the content to extract: title, round count, word count, source type
3. Generate a short ID from the date and title (e.g., `20260327-zai-prep`)
4. Run `bash lib/capture.sh <filepath> "<title>"` to copy and index it
5. Report what was captured

**Mode 2: Live capture** (`/wavemind capture` or `/wavemind capture "Topic Name"`)
When no filepath is given, start a live capture session:
1. Ask the user for a topic name if not provided
2. Create the artifact file immediately: `data/artifacts/<id>.md` with title and metadata header
3. Tell the user: "Recording this conversation. Talk naturally. When you're done, say 'done' or 'save'."
4. Continue the conversation normally, responding as you would to any request
5. **Capture incrementally, not at the end.** After each round (a topic reaches a natural pause, the user moves to a new question, or a decision is made), append that round to the artifact file right away. Each round gets:
   - A section header: `## Round N: Title`
   - The raw dialogue with `**Speaker:**` labels
   - Original words preserved (including mixed languages). Fix obvious typos but do not rewrite or summarize.
   - This avoids the lossy "reconstruct everything from memory at the end" problem.
6. When the user says "done", "save", or "stop recording":
   - Append any remaining conversation not yet written
   - Run `bash lib/capture.sh` to finalize and index it
   - Report: artifact ID, title, round count, word count, file path
   - Suggest: "Run `/wavemind visualize <id>` to generate the visual."

### `/wavemind visualize <artifact-id>`
Generate a living memory document from a stored thinking artifact.

**Steps:**
1. Read the artifact from `data/artifacts/<id>.md`
2. Analyze the thinking artifact. Your job is **editorial, not generative**:
   - Identify distinct rounds/sections of the conversation
   - Extract **punchline quotes** (memorable original words from each speaker)
   - Mark **pivoting moments** (where thinking shifted direction)
   - Clean up the transcript: remove filler, fix noise, preserve original words
   - Do NOT summarize, generate insights, or create new content
3. Generate a structured analysis as JSON (see Analysis Format below)
4. Generate a self-contained HTML file following the NoteBlock editorial layout:
   - Start with "What Came Out of This" actionables section right after the header (why it matters + action items)
   - Then the timeline with each section: header, dialogue bubbles, punchline quote callout, expandable transcript
   - First-person perspective. It's the user's living memory, not a third-party report
   - Dialogue format with speech bubbles (user = white left-aligned, other speakers = dark right-aligned)
   - Progressive disclosure: punchline visible, full transcript behind "Read original" toggle
   - The HTML must be fully self-contained (inline CSS/JS, no external dependencies)
5. Save to `data/visuals/<id>.html`
6. Report the file path

### `/wavemind list`
Browse all stored thinking artifacts and their visualization status.

**Steps:**
1. Read `data/index.json`
2. Display a formatted table:

```
ID                          | Title                  | Rounds | Date       | Status
20260327-zai-prep           | ZAI Ambassador Prep    | 11     | 2026-03-27 | visualized
20260329-design-evolution   | Design Evolution       | 6      | 2026-03-29 | not visualized
```

3. If no artifacts exist, say "No artifacts captured yet. Run `/wavemind capture` to start."

## Analysis Format

When analyzing a thinking artifact, produce this structure:

```json
{
  "title": "Title from the user's perspective",
  "date": "2026-03-27",
  "participants": "Lily + Growth",
  "sections": [
    {
      "round": 1,
      "header": "High-signal title from user's perspective",
      "quote": "Memorable speaker quote, max 10 words",
      "dialogue": [
        {"speaker": "lily", "text": "One thought per bubble. Keep it short."},
        {"speaker": "growth", "text": "Response in their original words."}
      ],
      "is_pivoting_moment": false
    }
  ],
  "actionables": {
    "why_it_matters": "One paragraph explaining why this conversation mattered and what shifted.",
    "items": [
      "Concrete next step 1",
      "Concrete next step 2",
      "Concrete next step 3"
    ]
  }
}
```

**Key rules:**
- `header`: High-signal, from the user's perspective. BAD: "Discussion of Opportunity." GOOD: "I'm the Orchestrator, Not the Promoter."
- `quote`: A real speaker quote, not AI-generated. The "memory hook," what you'd remember a week later.
- `dialogue`: The actual back-and-forth, one thought per bubble. Use original words (including mixed Chinese/English). Clean filler but don't rewrite.
- `is_pivoting_moment`: True only when thinking genuinely shifted direction. Not every round is a pivot.
- `actionables`: Always include. `why_it_matters` is a single paragraph explaining significance. `items` are concrete, specific next steps. Not vague ("think about X") but actionable ("build X", "pair Y with Z", "test A").

## HTML Visual Guidelines

When generating the HTML visualization:

- **Style:** Clean, editorial. Think newspaper or literary journal, not dashboard.
- **Color palette:** Cream background (#F9F8F6), charcoal text (#1A1918), gold accents (#CBA16E).
- **Typography:** Serif for headers and quotes (Playfair Display or Georgia), sans-serif for UI (Inter or system). Generous whitespace.
- **Layout (NoteBlock model):**
  - **Header:** Title (large serif), date, participants, "Living Memory" label with gold dot
  - **Actionables (right after header):** "What Came Out of This" with two-column grid: "Why It Matters" + "Actionables" list with gold bullet dots. This goes at the top so readers get the takeaway first.
  - **Timeline:** Vertical gold line at ~28% width. Each section is a grid row.
  - **Left column (28%):** Round label, punchline quote callout (large gold open-quote mark, bold italic serif)
  - **Right column:** Section header (bold serif), dialogue bubbles, "Read original" toggle
  - **Dialogue bubbles:** User = white with light border, left-aligned. Other speakers = dark (#2C2C2C), right-aligned. One thought per bubble.
  - **Pivoting moments:** Gold-filled timeline dot + "Pivoting Moment" badge
  - **Progressive disclosure:** "Read original" expands to full clean transcript
  - **Footer:** "WaveMind · Captured [date] · Visualized [date]"
- **Self-contained:** All CSS and JS must be inline. No external dependencies.
- **Responsive:** Hide quote callouts on mobile, switch to single-column layout.

**Reference:** See `data/visuals/glm-benchmark.html` for an approved example (GLM Aesthetic Benchmark).

## Data Directory

Runtime data lives in `data/`. New artifacts and visuals are gitignored, but seeded examples are checked in for reference.

```
data/
├── .gitignore       # Ignores runtime data, keeps seeded examples
├── index.json       # Artifact registry (created at runtime)
├── artifacts/       # Raw markdown files (glm-benchmark.md seeded)
└── visuals/         # Generated HTML files (glm-benchmark.html seeded)
```

## Index Format

`index.json` is an array of artifact entries:

```json
[
  {
    "id": "20260327-zai-prep",
    "title": "ZAI Ambassador Prep",
    "source": "brainstorm",
    "tags": ["zai", "ambassador", "strategy"],
    "rounds": 11,
    "word_count": 3200,
    "created_at": "2026-03-27T00:00:00Z",
    "file": "artifacts/20260327-zai-prep.md",
    "visualized": true
  }
]
```
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