Skill: highnoonoffice/second-brain-visualizer (version 1.6.2)

--- name: second-brain-visualizer version: 1.6.1 description: "Reads your raw idea stream — voice notes, fragments, half-sentences — and surfaces the patterns you keep circling without realizing it. Drop anything. Find the signal later." homepage: https://github.com/highnoonoffice/hno-skills source: https://github.com/highnoonoffice/hno-skills/tree/main/second-brain-visualizer license: MIT-0 credentials: - name: "openclaw-gateway.json" description: "Required. Create at ~/.openclaw/credentials/openclaw-gateway.json with fields: host (default: 127.0.0.1), port (default: 18789), key (your OpenClaw gateway auth key). Keep host set to 127.0.0.1 to ensure atom corpus stays on-machine." required: true - name: "slack-sb.json" description: "Optional. Slack bot API key for automated ingestion from a private Slack channel. Format: { [REDACTED_SECRET] }" required: false - name: "telegram-sb.json" description: "Optional. Telegram bot API key for automated ingestion from a private Telegram channel. Format: { [REDACTED_SECRET], chat_id: string }" required: false metadata: config: - OPENCLAW_VAULT: "Path to your vault directory containing memory/second-brain.md" - SBV_MODEL: "LLM model for clustering and insight generation (default: openclaw:main)" - SBV_ATOMS_FILE: "Output path for parsed atoms JSON" - SBV_CLUSTERS_FILE: "Output path for cluster JSON" dataFlow: - "Reads local markdown ledger from OPENCLAW_VAULT/memory/second-brain.md" - "POSTs atom corpus to LLM via OpenClaw gateway at the host configured in ~/.openclaw/credentials/openclaw-gateway.json (default: 127.0.0.1) — keep host set to localhost to ensure data stays on-machine" - "No data leaves your local OpenClaw gateway — LLM routing is controlled by your OpenClaw config" - "Optional ingestion credentials (Slack, Telegram) stored locally in ~/.openclaw/credentials/ — never sent externally" --- # Second Brain Visualizer Your brain wasn't designed to hold data. It was designed to produce it. Pick a channel — Slack, Telegram, WhatsApp, Gmail, a private Discord. Every time something interesting moves through your mind, drop it. A line. A fragment. A joke that might be a product idea. Voice to text at a red light. You don't have to carry it anymore. Second Brain Visualizer reads what accumulates. Not to categorize it. To find the signal underneath the noise — the questions you keep returning to in different disguises, the tensions you're working out across dozens of unrelated notes, the creative territory you're actively mapping without realizing it. One note is just a note. Fifty notes across three weeks is a pattern. A year of notes is a portrait of how you think. The clustering engine reads for intent, not keywords. A note about LLM inference costs and a quote from Simone Weil may belong in the same cluster if they're reaching toward the same underlying question. Clusters surface with a name, a one-sentence insight, and a status: ESTABLISHED, FORMING, or FADING. Tensions show you where you're arguing with yourself. Notable absences show you what your idea stream isn't touching yet. This is not a note-taking app. Most note-taking tools are mirrors — they show you what you put in. This reads what it means. --- ### What It Does Your raw idea stream gets parsed into **atoms** — the smallest units of intent. Atoms are clustered by **affinity of meaning**, not keyword overlap. A note about LLM inference costs and a quote from Simone Weil may belong in the same cluster if they're both probing the same underlying question about attention and value. Each cluster gets: - A sharp name capturing the underlying drive (not a generic domain label) - A one-sentence insight: what does this pattern reveal about how you think? - A status: ESTABLISHED, FORMING, or FADING - A confidence score and time spread across your corpus The visualizer also surfaces: - **Emerging signals** — atoms with distinct intent that haven't massed into clusters yet - **Tensions** — places where your idea stream is arguing with itself across multiple notes - **Notable absences** — creative and intellectual domains conspicuously missing from the stream --- ### How It Works **1. Drop ideas anywhere** You already have a channel you use. Voice to text, half a sentence, a project name with no context. Raw is fine. The roughness is the point — it's what unguarded thinking looks like. **2. Parser extracts atoms** `references/parser.js` reads your second brain markdown ledger and extracts structured atoms with timestamp, raw text, type, signal, and optional next action. **3. Clustering engine reads for intent** `references/cluster.js` passes your full atom corpus to an LLM with a custom intent-based prompt. The prompt reads for what you're actually working out, not what words you used. Outputs clusters, tensions, emerging signals, and absences as structured JSON. **4. Visualizer shows you the map** `references/component.tsx` renders a D3 force-directed graph where nodes are sized by atom count × time spread. Click any node to expand: the base insight, an LLM-generated deeper read in gold, and the full list of atoms that make up the cluster. Tensions, signals, and absences scroll below. --- ### The Core Insight Most note-taking tools are mirrors — they show you what you put in. This reads what it means. The clustering prompt is the IP. Intent-based, not keyword-based. A joke reads as a probe. A fragment reads as a question. Two atoms belong together if they reach toward the same underlying question, even if they use completely different language. Full prompt in `references/cluster.js`. --- ### Atom Schema Each atom in your markdown ledger: ``` ### ts: <unix_timestamp> - **date:** YYYY-MM-DDTHH:MM:SS UTC - **raw:** verbatim text (voice to text, misspelled, incomplete — all valid) - **type:** thought | task | strategy | creative | meta | idea-jar | visual | link - **tags:** freeform, comma-separated - **signal:** hot | warm | cool - **actionable:** yes | no - **nextAction:** optional single-sentence move ``` --- ### Cluster Output Schema ```json { "clusters": [ { "id": "stable-kebab-id", "name": "Sharp name capturing underlying drive", "insight": "One sentence: what does this pattern reveal?", "atom_ids": ["sb-1234", "sb-5678"], "confidence": 0.87, "status": "ESTABLISHED", "time_spread": 4, "category": "CRAFT" } ], "emerging_signals": ["sb-9999"], "tensions": [ { "name": "Tension name", "atom_ids": ["sb-1", "sb-2"], "description": "What the person is working out" } ], "absences": ["Creative territory missing from the stream"] } ``` --- ### Example Output (85 atoms, 8 clusters) | Cluster | Status | Atoms | Spread | |---|---|---|---| | Systems Over Shortcuts | ESTABLISHED | 7 | 2w | | Language as Load-Bearing Structure | ESTABLISHED | 8 | 3w | | The Speed Paradox | ESTABLISHED | 8 | 3w | | Agent Failure as Intelligence | ESTABLISHED | 7 | 4w | | Friction as Design Oracle | FORMING | 5 | 4w | | Craft as Moral Position | ESTABLISHED | 7 | 3w | | The Protagonist Problem | ESTABLISHED | 7 | 4w | | Deliberate Presence as Counterculture | FORMING | 6 | 2w | --- ### Prerequisites - OpenClaw agent with a vault markdown ledger (atoms in the schema above) - Node.js 18+ - A Next.js dashboard or equivalent React host for the visualizer - `d3` and `@types/d3` installed - An LLM API configured in OpenClaw (for clustering and insight generation) --- ### Roadmap - [ ] Setup guide for new users building their first atom ledger - [ ] Configurable ingestion from Slack, Telegram, WhatsApp, Gmail - [ ] Cluster diff across runs (what emerged, merged, faded) - [ ] Full-graph view with atoms as sub-nodes - [ ] Cluster history timeline --- ### Credit The original idea to build a second brain capture system came from a conversation with **Nate B. Jones**. The architecture, clustering engine, and visualizer are original work — but the seed was his. --- ### License MIT-0. Copyright (c) 2026 @highnoonoffice. No attribution required.

path: references/setup.md
content: ---
title: "Second Brain Visualizer — Setup Guide"
version: 1.0.0
---

# Setup Guide

## The idea

You need a place to put things that's low-friction enough that you'll actually use it. Not a note-taking app with folders. Not a task manager. Just a channel — somewhere you can send a thought in ten seconds and move on.

The Second Brain Visualizer reads what accumulates there over time and finds the signal.

---

## Step 1 — Pick your drop channel

The system works with any channel your OpenClaw agent can read. Pick one you already use, or create a dedicated one.

**Good options:**
- A private Slack channel (e.g. `#sb-inbox`)
- A Telegram bot or private group
- A WhatsApp note-to-self
- A Gmail label you forward things to
- A private Discord channel

The channel doesn't matter. What matters is that dropping something there costs you almost nothing. If there's friction, you won't use it.

**One rule:** don't curate. Drop the half-formed thing. The misspelled thing. The joke that might be a product idea. The line from a book you can't explain yet. Raw input is better input. The system reads for intent, not polish.

---

## Step 2 — Start your atom ledger

Create a markdown file in your vault at `memory/second-brain.md`. This is where your parsed atoms will live.

Start with this front matter:

```markdown
---
title: "Second Brain — Atom Ledger"
created: YYYY-MM-DD
tags: [second-brain, ideas, capture]
status: active
---

# Second Brain — What It Is

A raw idea capture tool. Not a journal. Not a task list. A place for things worth keeping that don't fit anywhere else yet.
```

That's it. The parser will append atoms below this header as you run it.

---

## Step 3 — Write your first atoms manually (optional but recommended)

Before you wire up ingestion, seed the ledger by hand with 10–20 ideas from memory. Things you've been meaning to capture. This gives the clustering engine enough material to work with on first run.

Each atom follows this format:

```markdown
### ts: 1712034567

- **date:** 2026-04-06T14:00:00 UTC
- **raw:** your idea exactly as you thought it — misspelled, incomplete, whatever
- **type:** thought
- **tags:** [creativity, product]
- **signal:** hot
- **actionable:** no
- **nextAction:**
```

**Type options:** `thought` | `task` | `strategy` | `creative` | `meta` | `idea-jar` | `visual` | `link`

**Signal:** `hot` (strong energy right now) | `warm` (interesting, not urgent) | `cool` (low signal, keeping for record)

You don't have to fill everything out. `raw` is the only required field. The rest helps the clustering engine but won't break anything if it's missing.

---

## Step 4 — Run the parser

Once you have atoms in your ledger (either manual or from ingestion), run the parser to extract them into structured JSON:

```bash
node references/parser.js
```

This reads `memory/second-brain.md` from your vault and writes `data/second-brain-atoms.json`.

Check the output:

```bash
cat data/second-brain-atoms.json | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'{len(d[\"atoms\"])} atoms parsed')"
```

You need at least 9 atoms before clustering is meaningful. More is better. 30+ atoms gives the engine real material to work with.

---

## Step 5 — Run clustering

With atoms in place, run the clustering engine:

```bash
node references/cluster.js
```

This calls your configured LLM with the full atom corpus and the intent-based clustering prompt. It writes `data/second-brain-clusters.json`.

First run takes 30–60 seconds depending on your LLM. Output will include clusters, tensions, emerging signals, and notable absences.

---

## Step 6 — Open the visualizer

The React component (`references/component.tsx`) renders the cluster graph. Mount it in your dashboard, or open it standalone.

Click any node to expand its cluster: base insight, LLM-generated deeper read, full atom list. Tensions and absences scroll below the graph.

Re-cluster whenever you've added 10+ new atoms. The clusters will shift as your idea stream evolves.

---

## What good looks like

After 2–3 weeks of consistent drops, you should see:

- 3–8 distinct clusters with names that feel accurate but slightly surprising
- At least one tension that makes you go "oh, that's real"
- An absence or two that points at creative territory you haven't touched yet
- FORMING clusters that didn't exist last week

That's the system working. The surprise is the signal.

---

## Troubleshooting

**"Not enough atoms to cluster"** — You need at least 9. Add more manually or run ingestion.

**Clusters feel generic** — Your atoms may be too surface-level. The raw field should capture the actual thought, not a cleaned-up summary of it. Rougher input produces sharper clusters.

**Same clusters every run** — You're not adding new atoms. The system can only find what's there.

**LLM timeout** — Large corpus (200+ atoms) can slow the clustering call. This is normal. Let it run.


path: references/install.md
content: ---
title: "Second Brain Visualizer — Install Guide"
version: 1.0.0
---

# Install Guide

## Prerequisites

Before you start, confirm you have:

- **OpenClaw** installed and running (`openclaw status` returns ok)
- **Node.js 18+** (`node --version`)
- **An LLM configured** in OpenClaw — any model works, but one with a 32k+ context window handles large atom corpora better. Maple, GPT-4o, Claude Sonnet, or Gemini 2.5 Pro all work.
- **A vault directory** — wherever OpenClaw stores your markdown files (`echo $OPENCLAW_VAULT` or check `~/.openclaw/` directory for your configured vault path)

---

## Step 1 — Clone the skill

```bash
git clone https://github.com/highnoonoffice/hno-skills.git
cd hno-skills/second-brain-visualizer
```

Or if you already have the hno-skills repo:

```bash
cd hno-skills && git pull origin main
cd second-brain-visualizer
```

---

## Step 2 — Create your atom ledger

In your vault's `memory/` directory, create `second-brain.md`:

```bash
cat > $OPENCLAW_VAULT/memory/second-brain.md << 'EOF'
---
title: "Second Brain — Atom Ledger"
created: $(date +%Y-%m-%d)
tags: [second-brain, ideas, capture]
status: active
---

# Second Brain — What It Is

A raw idea capture tool. Not a journal. Not a task list.
EOF
```

See `references/setup.md` for how to write your first atoms manually, or `references/ingestion.md` to wire up automated ingestion from Slack, Telegram, or another channel.

---

## Step 3 — Configure paths in the scripts

Open `references/parser.js` and set your vault path:

```javascript
// Line 10 — set this to your vault directory
const VAULT = process.env.OPENCLAW_VAULT || '/path/to/your/vault';
```

Open `references/cluster.js` and set your LLM endpoint:

```javascript
// Line 15 — set your OpenClaw gateway URL
const GATEWAY = process.env.OPENCLAW_GATEWAY || 'http://127.0.0.1:18789';

// Line 16 — set your preferred model
const MODEL = process.env.SBV_MODEL || 'maple/gpt-oss-120b';
```

Or set environment variables instead of editing the files:

```bash
export OPENCLAW_VAULT="/Users/yourname/.openclaw/vault"
export OPENCLAW_GATEWAY="http://127.0.0.1:18789"
export SBV_MODEL="maple/gpt-oss-120b"
```

---

## Step 4 — Set up output directories

The scripts write to your Mission Control data directory. Create it if it doesn't exist:

```bash
mkdir -p /path/to/your/mission-control/data
```

Then update the output paths in `parser.js` and `cluster.js`:

```javascript
// parser.js — Line 13
const OUT_FILE = '/path/to/your/mission-control/data/second-brain-atoms.json';

// cluster.js — Line 17
const ATOMS_FILE = '/path/to/your/mission-control/data/second-brain-atoms.json';
const OUT_FILE   = '/path/to/your/mission-control/data/second-brain-clusters.json';
```

---

## Step 5 — Run the parser

```bash
node references/parser.js
```

Expected output:

```
Parsed 42 atoms → /path/to/data/second-brain-atoms.json
```

If you see `0 atoms parsed`, your ledger may be empty or the format doesn't match what the parser expects. See `references/setup.md` for the atom format.

---

## Step 6 — Run clustering

```bash
node references/cluster.js
```

This will take 20–60 seconds on first run (LLM call with full corpus). Expected output:

```
Clustering 42 atoms…
✓ 6 clusters written to /path/to/data/second-brain-clusters.json
  [ESTABLISHED] The Legitimacy Project — 8 atoms, 3w spread
  [ESTABLISHED] Language as Load-Bearing Structure — 7 atoms, 4w spread
  [FORMING] Friction as Design Oracle — 4 atoms, 2w spread
  ...
  3 emerging signals
  2 tensions
```

---

## Step 7 — Mount the React component

The visualizer is a React component (`references/component.tsx`) that reads from the two JSON files produced above.

**If you're using Mission Control or a similar Next.js dashboard:**

1. Copy `references/component.tsx` into your `components/tabs/` directory
2. Import and register it as a tab in your sidebar nav
3. Add API routes that serve `second-brain-atoms.json` and `second-brain-clusters.json`
4. Add an insight generation route (POST endpoint that calls your LLM with a cluster's atoms)

The component expects three API endpoints:

```
GET  /api/second-brain/atoms     → { atoms: [...] }
GET  /api/second-brain/clusters  → { clusters: [...], tensions: [...], absences: [...], ... }
POST /api/second-brain/clusters  → triggers re-clustering, returns new clusters
POST /api/second-brain/insight   → { atom_texts: [...], cluster_name: string } → { insight: string }
```

**If you're running standalone (no dashboard):**

Wrap the component in a minimal Next.js or Vite app. The component is self-contained — it only needs D3 and the four API endpoints above.

---

## Step 8 — Install frontend dependencies

In your dashboard project:

```bash
npm install d3
npm install --save-dev @types/d3
```

---

## Verify the full pipeline

1. Parser runs and outputs atoms ✓
2. Clusterer runs and outputs clusters ✓
3. Component loads in browser ✓
4. Clicking a node opens its detail panel ✓
5. Re-cluster button triggers a fresh LLM call ✓

If all five work, you're running. Drop 10 more ideas into your channel, re-ingest, re-cluster, and watch the graph shift.

---

## Environment variable reference

| Variable | Default | Description |
|---|---|---|
| `OPENCLAW_VAULT` | `/path/to/vault` | Path to your vault directory |
| `OPENCLAW_GATEWAY` | `http://127.0.0.1:18789` | OpenClaw gateway URL |
| `SBV_MODEL` | `maple/gpt-oss-120b` | LLM model for clustering and insights |
| `SBV_ATOMS_FILE` | `data/second-brain-atoms.json` | Output path for parsed atoms |
| `SBV_CLUSTERS_FILE` | `data/second-brain-clusters.json` | Output path for cluster data |


path: references/component.tsx
content: 'use client';

import { useState, useEffect, useRef, useCallback } from 'react';
import * as d3 from 'd3';

// ── Replace these API paths with your own routes ────────────────────────────
const API_ATOMS    = '/api/second-brain/atoms';     // GET → { atoms: Atom[] }
const API_CLUSTERS = '/api/second-brain/clusters';  // GET → ClustersData | POST (trigger re-cluster)
const API_INSIGHT  = '/api/second-brain/insight';   // POST { atom_texts, cluster_name } → { insight: string }
// ───────────────────────────────────────────────────────────────────────────

const GOLD = '#c8a84b';
const GOLD_DIM = 'rgba(200,168,75,0.06)';
const GOLD_BORDER = 'rgba(200,168,75,0.2)';

interface Atom {
  id: string;
  ts: number;
  date: string;
  raw: string;
  type: string;
  tags: string[];
  signal: string;
  actionable: boolean;
  nextAction: string | null;
}

interface Cluster {
  id: string;
  name: string;
  insight: string;
  atom_ids: string[];
  confidence: number;
  status: 'ESTABLISHED' | 'FORMING' | 'FADING';
  time_spread: number;
  category?: string;
}

interface Tension {
  name: string;
  atom_ids: string[];
  description: string;
}

interface ClustersData {
  generated: string | null;
  atomCount: number;
  clusters: Cluster[];
  emerging_signals: string[];
  tensions: Tension[];
  absences: string[];
}

const STATUS_COLORS: Record<string, string> = {
  ESTABLISHED: GOLD,
  FORMING: '#60a5fa',
  FADING: '#6b7280',
};

const CATEGORY_COLORS: Record<string, string> = {
  IDENTITY: '#a78bfa',
  CRAFT: '#f59e0b',
  SYSTEMS: '#34d399',
  LANGUAGE: '#60a5fa',
  PHILOSOPHY: '#f472b6',
  STRATEGY: '#fb923c',
};

const NODE_RADIUS = (c: Cluster) => Math.max(55, Math.min(85, 28 + c.atom_ids.length * 4 + c.time_spread * 2));

export default function SecondBrainVisualizer() {
  const svgRef = useRef<SVGSVGElement>(null);
  const [clusters, setClusters] = useState<ClustersData | null>(null);
  const [atoms, setAtoms] = useState<Atom[]>([]);
  const [loading, setLoading] = useState(true);
  const [clustering, setClustering] = useState(false);
  const [selected, setSelected] = useState<Cluster | null>(null);
  const [insight, setInsight] = useState<string | null>(null);
  const [insightLoading, setInsightLoading] = useState(false);
  const [error, setError] = useState<string | null>(null);

  useEffect(() => {
    Promise.all([
      fetch(API_ATOMS).then(r => r.json()),
      fetch(API_CLUSTERS).then(r => r.json()),
    ]).then(([atomsData, clustersData]) => {
      setAtoms(atomsData.atoms ?? []);
      if (clustersData.clusters?.length) setClusters(clustersData);
      setLoading(false);
    }).catch(() => setLoading(false));
  }, []);

  const atomMap = Object.fromEntries(atoms.map(a => [a.id, a]));
  const atomMapRef = useRef<Record<string, Atom>>({});
  useEffect(() => { atomMapRef.current = atomMap; }, [atoms]);

  // ── D3 graph ──────────────────────────────────────────────────────────────
  const selectedIdRef = useRef<string | null>(null);

  const handleNodeClick = useCallback((cluster: Cluster) => {
    setSelected(prev => {
      const next = prev?.id === cluster.id ? null : cluster;
      selectedIdRef.current = next?.id ?? null;
      return next;
    });
    setInsight(null);
  }, []);

  const handleNodeClickRef = useRef(handleNodeClick);
  useEffect(() => { handleNodeClickRef.current = handleNodeClick; }, [handleNodeClick]);

  const updateD3Selection = useCallback((selectedId: string | null) => {
    if (!svgRef.current) return;
    const svg = d3.select(svgRef.current);

    if (!selectedId) {
      svg.selectAll<SVGGElement, any>('g.node-group').each(function(d: any) {
        const statusOpacity: Record<string, number> = { ESTABLISHED: 1.0, FORMING: 0.82, FADING: 0.55 };
        const glowOpacity: Record<string, number> = { ESTABLISHED: 0.22, FORMING: 0.13, FADING: 0.06 };
        const strokeW: Record<string, number> = { ESTABLISHED: 2, FORMING: 1.5, FADING: 1 };
        d3.select(this).select('.node-main-circle')
          .attr('opacity', statusOpacity[d.status] ?? 1.0)
          .attr('stroke-width', strokeW[d.status] ?? 1.5);
        d3.select(this).select('.node-glow-ring')
          .attr('opacity', glowOpacity[d.status] ?? 0.15);
        d3.select(this).selectAll('.node-label-name, .node-label-count').attr('opacity', 1);
      });
      svg.selectAll<SVGLineElement, any>('line').attr('opacity', 0.6);
      return;
    }

    const linkedIds = new Set<string>();
    svg.selectAll<SVGLineElement, any>('line').each((d: any) => {
      const s = typeof d.source === 'object' ? d.source.id : d.source;
      const t = typeof d.target === 'object' ? d.target.id : d.target;
      if (s === selectedId) linkedIds.add(t);
      if (t === selectedId) linkedIds.add(s);
    });

    svg.selectAll<SVGGElement, any>('g.node-group').each(function(d: any) {
      const isSelected = d.id === selectedId;
      const isConnected = linkedIds.has(d.id);
      const dimOpacity = isSelected ? 1 : isConnected ? 0.65 : 0.18;
      d3.select(this).select('.node-main-circle')
        .attr('opacity', dimOpacity)
        .attr('stroke-width', isSelected ? 3 : 1.5);
      d3.select(this).select('.node-glow-ring')
        .attr('opacity', isSelected ? 0.5 : isConnected ? 0.18 : 0.04);
      d3.select(this).selectAll('.node-label-name, .node-label-count')
        .attr('opacity', isSelected ? 1 : isConnected ? 0.7 : 0.2);
    });

    svg.selectAll<SVGLineElement, any>('line').each(function(d: any) {
      const s = typeof d.source === 'object' ? d.source.id : d.source;
      const t = typeof d.target === 'object' ? d.target.id : d.target;
      const relevant = s === selectedId || t === selectedId;
      d3.select(this).attr('opacity', relevant ? 0.85 : 0.05);
    });
  }, []);

  useEffect(() => {
    updateD3Selection(selected?.id ?? null);
  }, [selected, updateD3Selection]);

  useEffect(() => {
    if (!svgRef.current || !clusters?.clusters?.length) return;

    const el = svgRef.current;
    const W = el.clientWidth || 900;
    const H = 500;
    el.setAttribute('height', String(H));

    d3.select(el).selectAll('*').remove();

    const nodes = clusters.clusters.map(c => ({ ...c, r: NODE_RADIUS(c) }));

    const links: { source: string; target: string; weight: number }[] = [];
    for (let i = 0; i < nodes.length; i++) {
      for (let j = i + 1; j < nodes.length; j++) {
        const shared = nodes[i].atom_ids.filter(id => nodes[j].atom_ids.includes(id)).length;
        if (shared > 0) links.push({ source: nodes[i].id, target: nodes[j].id, weight: shared });
      }
    }

    const sim = d3.forceSimulation(nodes as any)
      .force('link', d3.forceLink(links as any).id((d: any) => d.id).distance(160).strength(0.25))
      .force('charge', d3.forceManyBody().strength(-400))
      .force('center', d3.forceCenter(W / 2, H / 2))
      .force('collide', d3.forceCollide().radius((d: any) => d.r + 22))
      .force('x', d3.forceX(W / 2).strength(0.05))
      .force('y', d3.forceY(H / 2).strength(0.07));

    const svg = d3.select(el);

    // ── Defs: glow filter + animations ────────────────────────────────────
    const defs = svg.append('defs');
    const filter = defs.append('filter').attr('id', 'sbv-glow');
    filter.append('feGaussianBlur').attr('stdDeviation', '3').attr('result', 'coloredBlur');
    const feMerge = filter.append('feMerge');
    feMerge.append('feMergeNode').attr('in', 'coloredBlur');
    feMerge.append('feMergeNode').attr('in', 'SourceGraphic');

    defs.append('style').text(`
      @keyframes sbv-float {
        0%   { transform: translateY(0px);  }
        50%  { transform: translateY(-5px); }
        100% { transform: translateY(0px);  }
      }
      @keyframes sbv-tension-crawl {
        from { stroke-dashoffset: 0; }
        to   { stroke-dashoffset: -24; }
      }
      .sbv-tension-arc { animation: sbv-tension-crawl 1.6s linear infinite; }
    `);

    // ── Links ──────────────────────────────────────────────────────────────
    const link = svg.append('g').selectAll('line')
      .data(links)
      .join('line')
      .attr('stroke', '#252525')
      .attr('stroke-width', (d: any) => Math.max(1, d.weight * 0.7))
      .attr('stroke-dasharray', '3 5')
      .attr('opacity', 0.6);

    // ── Tension arcs ───────────────────────────────────────────────────────
    const atomToCluster: Record<string, string> = {};
    nodes.forEach(n => n.atom_ids.forEach((id: string) => { atomToCluster[id] = n.id; }));

    type TensionArc = { source: string; target: string; name: string };
    const tensionArcs: TensionArc[] = [];
    (clusters.tensions ?? []).forEach(t => {
      const cIds = Array.from(new Set(t.atom_ids.map((id: string) => atomToCluster[id]).filter(Boolean))) as string[];
      for (let i = 0; i < cIds.length; i++) {
        for (let j = i + 1; j < cIds.length; j++) {
          if (!tensionArcs.some(a => (a.source === cIds[i] && a.target === cIds[j]) || (a.source === cIds[j] && a.target === cIds[i]))) {
            tensionArcs.push({ source: cIds[i], target: cIds[j], name: t.name });
          }
        }
      }
    });

    const nodeById = Object.fromEntries(nodes.map(n => [n.id, n]));

    function arcPath(s: any, t: any): string {
      const mx = (s.x + t.x) / 2, my = (s.y + t.y) / 2;
      const dx = t.x - s.x, dy = t.y - s.y;
      const len = Math.sqrt(dx * dx + dy * dy) || 1;
      const bow = 40;
      return `M ${s.x} ${s.y} Q ${mx + (-dy / len) * bow} ${my + (dx / len) * bow} ${t.x} ${t.y}`;
    }

    const tensionArcGroup = svg.append('g');
    const tensionPaths = tensionArcGroup.selectAll('path')
      .data(tensionArcs)
      .join('path')
      .attr('class', 'sbv-tension-arc')
      .attr('fill', 'none')
      .attr('stroke', '#7f1d1d')
      .attr('stroke-width', 1.2)
      .attr('stroke-dasharray', '6 5')
      .attr('opacity', 0.4);

    // ── Shared hover tooltip ───────────────────────────────────────────────
    const tooltip = svg.append('g').attr('class', 'sbv-tooltip').attr('opacity', 0).attr('pointer-events', 'none');
    const tooltipBg = tooltip.append('rect').attr('rx', 6).attr('fill', '#111').attr('stroke', '#2a2a2a').attr('stroke-width', 1);
    const tooltipCat = tooltip.append('text').attr('font-size', 9).attr('font-family', 'monospace').attr('letter-spacing', '0.08em').attr('text-anchor', 'middle');
    const tooltipName = tooltip.append('text').attr('font-size', 13).attr('font-weight', '700').attr('font-family', 'system-ui, sans-serif').attr('fill', '#e8e8e8').attr('text-anchor', 'middle');
    const tooltipInsight = tooltip.append('text').attr('font-size', 10).attr('font-family', 'system-ui, sans-serif').attr('fill', '#888').attr('text-anchor', 'middle');

    function showTooltip(d: any, nx: number, ny: number) {
      const color = CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD;
      const insightText = (d.insight ?? '').length > 80 ? (d.insight as string).slice(0, 77) + '…' : (d.insight ?? '');
      tooltipCat.attr('fill', color).text(((d as any).category ?? '') + ' · ' + d.atom_ids.length + ' atoms · ' + Math.round(d.confidence * 100) + '% confidence');
      tooltipName.text(d.name);
      tooltipInsight.text(insightText);
      const ty = ny - d.r - 80;
      tooltipCat.attr('x', nx).attr('y', ty);
      tooltipName.attr('x', nx).attr('y', ty + 18);
      tooltipInsight.attr('x', nx).attr('y', ty + 34);
      const nameBbox = (tooltipName.node() as SVGTextElement)?.getBBox?.() ?? { x: nx - 100, y: ty + 5, width: 200, height: 15 };
      const insightBbox = (tooltipInsight.node() as SVGTextElement)?.getBBox?.() ?? { x: nx - 100, y: ty + 18, width: 200, height: 12 };
      const bw = Math.max(nameBbox.width, insightBbox.width) + 32;
      const bh = insightText ? 52 : 36;
      tooltipBg.attr('x', nx - bw / 2).attr('y', ty - 12).attr('width', bw).attr('height', bh);
      tooltip.transition().duration(120).attr('opacity', 1);
    }

    function hideTooltip() {
      tooltip.transition().duration(160).attr('opacity', 0);
    }

    // ── Recency scoring ────────────────────────────────────────────────────
    const tsList = nodes.map((n: any) => {
      const ca = n.atom_ids.map((id: string) => atomMapRef.current[id]).filter(Boolean);
      return ca.length ? Math.max(...ca.map((a: Atom) => a.ts)) : 0;
    });
    const tsMax = Math.max(...tsList) || 1;
    const tsMin = Math.min(...tsList) || 0;
    const tsRange = tsMax - tsMin || 1;
    nodes.forEach((n: any, i: number) => { (n as any)._recency = (tsList[i] - tsMin) / tsRange; });

    // ── Node groups ────────────────────────────────────────────────────────
    const node = svg.append('g').selectAll('g')
      .data(nodes)
      .join('g')
      .attr('class', 'node-group')
      .attr('cursor', 'pointer')
      .call(d3.drag<any, any>()
        .on('start', (event, d) => { if (!event.active) sim.alphaTarget(0.3).restart(); d.fx = d.x; d.fy = d.y; })
        .on('drag', (event, d) => { d.fx = event.x; d.fy = event.y; })
        .on('end', (event, d) => { if (!event.active) sim.alphaTarget(0); d.fx = null; d.fy = null; })
      )
      .on('click', (_event, d) => handleNodeClickRef.current(d))
      .on('mouseover', function(_event, d: any) {
        showTooltip(d, d.x ?? 0, d.y ?? 0);
        d3.select(this).select('.node-main-circle').transition().duration(120).attr('stroke-width', 3);
        d3.select(this).select('.node-glow-ring').transition().duration(120).attr('opacity', 0.5);
      })
      .on('mouseout', function(_event, d: any) {
        hideTooltip();
        const swMap: Record<string, number> = { ESTABLISHED: 2, FORMING: 1.5, FADING: 1 };
        const goMap: Record<string, number> = { ESTABLISHED: 0.22, FORMING: 0.13, FADING: 0.06 };
        d3.select(this).select('.node-main-circle').transition().duration(180).attr('stroke-width', swMap[d.status] ?? 1.5);
        d3.select(this).select('.node-glow-ring').transition().duration(180).attr('opacity', goMap[d.status] ?? 0.15);
      });

    // ── Float wrapper (temporal float — each node breathes at its own pace) ─
    const floatGroup = node.append('g')
      .attr('class', 'node-float')
      .style('animation', (_d: any, i: number) => {
        const dur = 4.0 + i * 0.3;
        return `sbv-float ${dur}s ease-in-out ${-(i * 0.6)}s infinite`;
      });

    // Glow ring
    floatGroup.append('circle')
      .attr('class', 'node-glow-ring')
      .attr('r', (d: any) => d.r + 7)
      .attr('fill', 'none')
      .attr('stroke', (d: any) => CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD)
      .attr('stroke-width', 1)
      .attr('opacity', (d: any) => ({ ESTABLISHED: 0.22, FORMING: 0.13, FADING: 0.06 })[d.status as string] ?? 0.15)
      .attr('filter', 'url(#sbv-glow)');

    // Main circle — status as texture (ESTABLISHED=solid, FORMING=dashed, FADING=sparse dots)
    const STATUS_FILL_OPACITY: Record<string, string> = { ESTABLISHED: '20', FORMING: '10', FADING: '07' };
    const STATUS_STROKE_DASH: Record<string, string | null> = { ESTABLISHED: null, FORMING: '5 4', FADING: '2 8' };
    const STATUS_OPACITY: Record<string, number> = { ESTABLISHED: 1.0, FORMING: 0.82, FADING: 0.55 };
    const STATUS_STROKE_W: Record<string, number> = { ESTABLISHED: 2, FORMING: 1.5, FADING: 1 };

    floatGroup.append('circle')
      .attr('class', 'node-main-circle')
      .attr('r', (d: any) => d.r)
      .attr('fill', (d: any) => (CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD) + (STATUS_FILL_OPACITY[d.status] ?? '18'))
      .attr('stroke', (d: any) => CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD)
      .attr('stroke-width', (d: any) => STATUS_STROKE_W[d.status] ?? 1.5)
      .attr('stroke-dasharray', (d: any) => STATUS_STROKE_DASH[d.status] ?? null)
      .attr('opacity', (d: any) => STATUS_OPACITY[d.status] ?? 1.0);

    // Confidence arc
    floatGroup.append('circle')
      .attr('r', (d: any) => d.r - 5)
      .attr('fill', 'none')
      .attr('stroke', (d: any) => CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD)
      .attr('stroke-width', 1.5)
      .attr('stroke-dasharray', (d: any) => {
        const circ = 2 * Math.PI * (d.r - 5);
        return `${circ * d.confidence} ${circ}`;
      })
      .attr('opacity', (d: any) => d.status === 'FADING' ? 0.15 : 0.35)
      .attr('transform', 'rotate(-90)');

    // ── Node labels ────────────────────────────────────────────────────────
    floatGroup.each(function(d: any) {
      const g = d3.select(this);
      const color = CATEGORY_COLORS[(d as any).category ?? ''] ?? GOLD;
      const words = d.name.split(' ');
      const lines: string[] = [];
      let cur: string[] = [];
      for (const w of words) {
        cur.push(w);
        if (cur.join(' ').length > 12) { lines.push(cur.join(' ')); cur = []; }
      }
      if (cur.length) lines.push(cur.join(' '));
      const lh = 14;
      const totalH = lines.length * lh;
      const startY = -(totalH / 2) + lh * 0.4;
      const nameEl = g.append('text')
        .attr('class', 'node-label-name')
        .attr('text-anchor', 'middle')
        .attr('fill', '#f0f0f0')
        .attr('font-size', 11)
        .attr('font-weight', '700')
        .attr('font-family', 'system-ui, sans-serif')
        .attr('pointer-events', 'none')
        .attr('opacity', 1);
      lines.forEach((line, i) => {
        nameEl.append('tspan').attr('x', 0).attr('dy', i === 0 ? `${startY}px` : `${lh}px`).text(line);
      });
      g.append('text')
        .attr('class', 'node-label-count')
        .attr('text-anchor', 'middle')
        .attr('y', totalH / 2 + 14)
        .attr('fill', color)
        .attr('font-size', 9)
        .attr('font-family', 'monospace')
        .attr('opacity', 0.8)
        .attr('pointer-events', 'none')
        .text(d.atom_ids.length + ' atoms');
    });

    // ── Category legend ────────────────────────────────────────────────────
    const uniqueCats = Array.from(new Set((clusters.clusters ?? []).map((c: any) => c.category).filter(Boolean))) as string[];
    const legend = svg.append('g').attr('transform', 'translate(16, 16)');
    uniqueCats.forEach((cat, i) => {
      const color = CATEGORY_COLORS[cat] ?? GOLD;
      const g = legend.append('g').attr('transform', `translate(0, ${i * 17})`);
      g.append('circle').attr('r', 4).attr('cx', 4).attr('cy', 0)
        .attr('fill', color + '28').attr('stroke', color).attr('stroke-width', 1.2);
      g.append('text').attr('x', 13).attr('y', 4)
        .attr('fill', color).attr('font-size', 9).attr('font-family', 'monospace')
        .attr('letter-spacing', '0.06em').text(cat);
    });

    // ── Tick ───────────────────────────────────────────────────────────────
    sim.on('tick', () => {
      link
        .attr('x1', (d: any) => d.source.x).attr('y1', (d: any) => d.source.y)
        .attr('x2', (d: any) => d.target.x).attr('y2', (d: any) => d.target.y);
      node.attr('transform', (d: any) => `translate(${
        Math.max(d.r + 6, Math.min(W - d.r - 6, d.x))
      },${
        Math.max(d.r + 6, Math.min(H - d.r - 6, d.y))
      })`);
      tensionPaths.attr('d', (d: any) => {
        const s = nodeById[d.source] as any;
        const t = nodeById[d.target] as any;
        return (s && t) ? arcPath(s, t) : '';
      });
    });

    sim.on('end', () => {
      if (selectedIdRef.current) updateD3Selection(selectedIdRef.current);
    });

    return () => { sim.stop(); };
  }, [clusters, handleNodeClick, updateD3Selection]);

  // ── Insight generator ──────────────────────────────────────────────────
  useEffect(() => {
    if (!selected) return;
    setInsight(null);
    setInsightLoading(true);
    const atom_texts = selected.atom_ids.map(id => atomMap[id]).filter(Boolean).map(a => a.raw);
    fetch(API_INSIGHT, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ atom_texts, cluster_name: selected.name }),
    })
      .then(r => r.json())
      .then(d => setInsight(d.insight ?? null))
      .catch(() => setInsight(null))
      .finally(() => setInsightLoading(false));
  }, [selected]);

  async function runClustering() {
    setClustering(true);
    setError(null);
    setSelected(null);
    selectedIdRef.current = null;
    try {
      const res = await fetch(API_CLUSTERS, { method: 'POST' });
      const data = await res.json();
      if (data.error) throw new Error(data.error);
      setClusters(data);
    } catch (e: any) {
      setError(e.message);
    }
    setClustering(false);
  }

  const fmt = (iso: string) => new Date(iso).toLocaleDateString('en-US', { month: 'short', day: 'numeric' });

  return (
    <div style={{ background: '#0d0d0d', minHeight: '100%', fontFamily: 'system-ui, sans-serif' }}>

      {/* Header */}
      <div style={{ display: 'flex', alignItems: 'baseline', justifyContent: 'space-between', padding: '20px 28px 0' }}>
        <div>
          <p style={{ fontSize: 11, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#555' }}>Second Brain</p>
          <p style={{ fontSize: 10, color: '#333', marginTop: 3 }}>
            {atoms.length} atoms · {clusters?.clusters?.length ?? 0} clusters
            {clusters?.generated && ` · clustered ${fmt(clusters.generated)}`}
          </p>
        </div>
        <button
          onClick={runClustering}
          disabled={clustering}
          style={{
            fontSize: 11, padding: '6px 16px', borderRadius: 6,
            background: clustering ? 'transparent' : GOLD_DIM,
            border: `1px solid ${GOLD_BORDER}`,
            color: clustering ? '#555' : GOLD,
            cursor: clustering ? 'not-allowed' : 'pointer',
          }}
        >
          {clustering ? 'Clustering…' : clusters?.clusters?.length ? '↻ Re-cluster' : 'Run Clustering'}
        </button>
      </div>

      {error && (
        <div style={{ margin: '12px 28px', background: 'rgba(239,68,68,0.1)', border: '1px solid rgba(239,68,68,0.2)', borderRadius: 8, padding: '10px 14px', fontSize: 12, color: '#ef4444' }}>
          {error}
        </div>
      )}

      {loading && <p style={{ color: '#333', fontSize: 13, padding: '40px 28px' }}>Loading…</p>}

      {!loading && !clusters?.clusters?.length && !clustering && (
        <div style={{ textAlign: 'center', padding: '60px 0' }}>
          <p style={{ color: '#444', fontSize: 13, marginBottom: 8 }}>No clusters yet.</p>
          <p style={{ color: '#333', fontSize: 11 }}>Run clustering to analyze {atoms.length} atoms.</p>
        </div>
      )}

      {/* D3 force graph */}
      {clusters?.clusters?.length ? (
        <div style={{ margin: '16px 0 0', borderBottom: '1px solid #1a1a1a' }}>
          <svg ref={svgRef} width="100%" height="500" style={{ display: 'block', background: '#0a0a0a' }} />
        </div>
      ) : null}

      {clusters?.clusters?.length ? (
        <div style={{ padding: '6px 28px', borderBottom: '1px solid #111' }}>
          <span style={{ fontSize: 9, color: '#2a2a2a', letterSpacing: '0.08em' }}>
            Node size = atoms × time spread · Dashed border = FORMING · Sparse dots = FADING · Red arcs = tensions · Click to explore
          </span>
        </div>
      ) : null}

      {/* Selected cluster detail */}
      {selected && (
        <div style={{ padding: '24px 28px', borderBottom: '1px solid #1a1a1a', background: '#0f0f0f' }}>
          <div style={{ display: 'flex', alignItems: 'baseline', justifyContent: 'space-between', marginBottom: 12 }}>
            <div>
              <p style={{ fontSize: 16, fontWeight: 700, color: '#e0e0e0', marginBottom: 6 }}>{selected.name}</p>
              <div style={{ display: 'flex', gap: 14 }}>
                <span style={{ fontSize: 10, color: STATUS_COLORS[selected.status] ?? GOLD, letterSpacing: '0.08em' }}>{selected.status}</span>
                <span style={{ fontSize: 10, color: '#555' }}>{selected.atom_ids.length} atoms</span>
                <span style={{ fontSize: 10, color: '#555' }}>{selected.time_spread}w spread</span>
                <span style={{ fontSize: 10, color: '#555' }}>{Math.round(selected.confidence * 100)}% confidence</span>
              </div>
            </div>
            <button onClick={() => { setSelected(null); setInsight(null); }} style={{ fontSize: 11, color: '#555', background: 'none', border: 'none', cursor: 'pointer' }}>✕</button>
          </div>

          <p style={{ fontSize: 12, color: '#666', lineHeight: 1.6, marginBottom: 16, fontStyle: 'italic' }}>{selected.insight}</p>

          {insightLoading && (
            <div style={{ background: GOLD_DIM, border: `1px solid ${GOLD_BORDER}`, borderRadius: 8, padding: '14px 16px', marginBottom: 20 }}>
              <p style={{ fontSize: 12, color: '#666', fontStyle: 'italic' }}>Reading your atoms…</p>
            </div>
          )}

          {insight && !insightLoading && (
            <div style={{ background: GOLD_DIM, border: `1px solid ${GOLD_BORDER}`, borderRadius: 8, padding: '14px 16px', marginBottom: 20 }}>
              <p style={{ fontSize: 10, color: '#555', letterSpacing: '0.1em', textTransform: 'uppercase', marginBottom: 8 }}>Deeper Read</p>
              <p style={{ fontSize: 14, color: GOLD, lineHeight: 1.7, fontStyle: 'italic' }}>{insight}</p>
            </div>
          )}

          <p style={{ fontSize: 10, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#444', marginBottom: 12 }}>Atoms in this cluster</p>
          <div style={{ display: 'flex', flexDirection: 'column', gap: 8 }}>
            {selected.atom_ids.map(id => {
              const atom = atomMap[id];
              if (!atom) return null;
              return (
                <div key={id} style={{ background: '#111', border: '1px solid #1a1a1a', borderRadius: 8, padding: '10px 14px' }}>
                  <div style={{ display: 'flex', gap: 12, marginBottom: 5 }}>
                    <span style={{ fontSize: 10, color: '#444' }}>{atom.date?.slice(0, 10)}</span>
                    <span style={{ fontSize: 10, color: atom.signal === 'hot' ? GOLD : atom.signal === 'warm' ? '#60a5fa' : '#444' }}>● {atom.signal}</span>
                  </div>
                  <p style={{ fontSize: 12, color: '#d0d0d0', lineHeight: 1.6 }}>{atom.raw}</p>
                  {atom.nextAction && <p style={{ fontSize: 10, color: '#555', marginTop: 6, fontStyle: 'italic' }}>→ {atom.nextAction}</p>}
                </div>
              );
            })}
          </div>
        </div>
      )}

      {/* Cluster directory */}
      {clusters?.clusters?.length ? (
        <div style={{ padding: '20px 28px', borderBottom: '1px solid #111' }}>
          <p style={{ fontSize: 10, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#555', marginBottom: 16 }}>Cluster Directory</p>
          <div style={{ display: 'grid', gridTemplateColumns: 'repeat(auto-fill, minmax(280px, 1fr))', gap: 10 }}>
            {clusters.clusters.map(c => {
              const catColor = CATEGORY_COLORS[(c as any).category ?? ''] ?? GOLD;
              const isSelected = selected?.id === c.id;
              return (
                <div key={c.id} onClick={() => handleNodeClick(c)} style={{
                  background: isSelected ? '#1a1500' : '#111',
                  border: `1px solid ${isSelected ? catColor : catColor + '30'}`,
                  borderLeft: `3px solid ${catColor}`,
                  borderRadius: 8, padding: '12px 14px', cursor: 'pointer',
                }}>
                  <p style={{ fontSize: 12, fontWeight: 700, color: isSelected ? '#fff' : '#e0e0e0', marginBottom: 4 }}>{c.name}</p>
                  <p style={{ fontSize: 11, color: '#666', lineHeight: 1.5, fontStyle: 'italic' }}>{c.insight}</p>
                  <div style={{ display: 'flex', gap: 10, marginTop: 8 }}>
                    <span style={{ fontSize: 9, color: catColor, letterSpacing: '0.08em', textTransform: 'uppercase' }}>{(c as any).category}</span>
                    <span style={{ fontSize: 9, color: '#333' }}>{c.atom_ids.length} atoms</span>
                  </div>
                </div>
              );
            })}
          </div>
        </div>
      ) : null}

      {/* Tensions */}
      {clusters?.tensions?.length ? (
        <div style={{ padding: '20px 28px', borderBottom: '1px solid #111' }}>
          <p style={{ fontSize: 10, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#888', marginBottom: 12 }}>Tensions</p>
          <div style={{ display: 'flex', flexDirection: 'column', gap: 8 }}>
            {clusters.tensions.map((t, i) => (
              <div key={i} style={{ background: '#111', border: '1px solid #1e1e1e', borderRadius: 8, padding: '12px 14px' }}>
                <p style={{ fontSize: 12, fontWeight: 600, color: '#d4a843', marginBottom: 4 }}>{t.name}</p>
                <p style={{ fontSize: 11, color: '#aaa', lineHeight: 1.5 }}>{t.description}</p>
              </div>
            ))}
          </div>
        </div>
      ) : null}

      {/* Emerging signals */}
      {clusters?.emerging_signals?.length ? (
        <div style={{ padding: '20px 28px', borderBottom: '1px solid #111' }}>
          <p style={{ fontSize: 10, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#555', marginBottom: 12 }}>Emerging Signals</p>
          <div style={{ display: 'flex', flexDirection: 'column', gap: 6 }}>
            {clusters.emerging_signals.map(id => {
              const atom = atomMap[id];
              if (!atom) return null;
              return (
                <div key={id} style={{ display: 'flex', gap: 12, alignItems: 'baseline', padding: '8px 12px', background: '#111', borderRadius: 6, border: '1px solid #1a1a1a' }}>
                  <span style={{ fontSize: 10, color: '#4ade80', flexShrink: 0 }}>{atom.date?.slice(0, 10)}</span>
                  <p style={{ fontSize: 12, color: '#ccc' }}>{atom.raw}</p>
                </div>
              );
            })}
          </div>
        </div>
      ) : null}

      {/* Notable absences */}
      {clusters?.absences?.length ? (
        <div style={{ padding: '20px 28px' }}>
          <p style={{ fontSize: 10, letterSpacing: '0.1em', textTransform: 'uppercase', color: '#888', marginBottom: 10 }}>Notable Absences</p>
          <div style={{ display: 'flex', flexWrap: 'wrap', gap: 8 }}>
            {clusters.absences.map((a, i) => (
              <span key={i} style={{ fontSize: 11, color: '#bbb', background: '#111', border: '1px solid #2a2a2a', borderRadius: 4, padding: '4px 10px' }}>{a}</span>
            ))}
          </div>
        </div>
      ) : null}

    </div>
  );
}


path: references/ingestion.md
content: ---
title: "Second Brain Visualizer — Ingestion Guide"
version: 1.0.0
---

# Ingestion Guide

## How ingestion works

The parser reads your atom ledger (`memory/second-brain.md`) and converts it to structured JSON. Your job is to get raw ideas from wherever you drop them into that ledger.

There are two approaches:

**Manual** — You write atoms directly into the markdown file. Good for getting started. Fine long-term if you prefer to stay close to the material.

**Automated** — An OpenClaw agent reads your drop channel on a schedule and appends new atoms to the ledger. Drop an idea into Slack at 2am, wake up to find it already parsed and waiting.

This guide covers the automated path for each major channel.

---

## Atom format (what the parser expects)

Every atom in the ledger looks like this:

```markdown
### ts: 1712034567

- **date:** 2026-04-06T14:00:00 UTC
- **raw:** the idea verbatim
- **type:** thought
- **tags:** [tag1, tag2]
- **signal:** hot
- **actionable:** no
- **nextAction:**
```

Your ingestion script produces this format and appends it to the ledger. The timestamp (`ts`) should be Unix epoch seconds. It's the anchor — don't skip it.

---

## Slack

The most common setup. Create a private channel (e.g. `#sb-inbox`) and drop ideas there throughout the day.

**OpenClaw cron to read and append:**

```javascript
// Weekly or daily — reads new messages from your Slack channel
// and appends them as atoms to memory/second-brain.md

const channelId = 'YOUR_CHANNEL_ID'; // e.g. C0AGZG13NFL
const vaultPath = '/path/to/vault/memory/second-brain.md';

// Step 1: Read messages using the message tool
// action=read, channel=slack, channelId=channelId, limit=50, buttons=[]

// Step 2: For each message since last run:
// - Extract text
// - Determine signal (you can prompt an LLM or default to 'warm')
// - Append as atom to second-brain.md

// Step 3: Update last-read timestamp so you don't re-ingest

const atom = `
### ts: ${Math.floor(Date.now() / 1000)}

- **date:** ${new Date().toISOString().replace('T', ' ').slice(0, 19)} UTC
- **raw:** ${messageText}
- **type:** thought
- **tags:** []
- **signal:** warm
- **actionable:** no
- **nextAction:**
`;
```

**Getting your channel ID:** In Slack, right-click the channel name → View channel details → scroll to the bottom. It starts with `C`.

**Recommended schedule:** Weekly pull via cron. Daily if you're a heavy dropper.

---

## Telegram

If you use a Telegram bot or private channel as your drop point.

**Setup:**
1. Create a Telegram bot via @BotFather
2. Add it to your private channel or send messages directly to the bot
3. Store the bot API key in `~/.openclaw/credentials/telegram-sb.json`

**Reading messages:**

```javascript
// Read recent messages from your Telegram bot
const [REDACTED_SECRET]';
const res = await fetch(`https://api.telegram.org/bot${apiKey}/getUpdates?offset=${lastOffset}`);
const updates = await res.json();

for (const update of updates.result) {
  const text = update.message?.text;
  if (!text) continue;
  // append as atom
}
```

**Tip:** OpenClaw's message tool can read Telegram channels directly if your bot is configured. Use `action=read` with your channel target instead of raw API calls.

---

## WhatsApp

WhatsApp doesn't have an official read API for personal accounts. Two practical paths:

**Path A — WhatsApp to Telegram bridge:** Use a service like ManyChat or a personal bridge to forward your WhatsApp messages to a Telegram bot. Then use the Telegram ingestion above.

**Path B — Manual export:** WhatsApp → Export Chat → import the text file and parse it with a custom script. Less elegant, works fine for weekly batch runs.

```bash
# Parse a WhatsApp export file
node references/parse-whatsapp.js ~/Downloads/WhatsApp\ Chat.txt
```

A sample parser for WhatsApp export format is included as an extension in this reference folder.

---

## Gmail

Gmail ingestion is supported but requires advanced setup (Google Cloud project + Gmail API credentials). See the Gmail API documentation for the authorization flow. Once configured, forward ideas to a dedicated `second-brain` label and ingest unread messages on your cron schedule.

---

## Voice (any channel)

The best ideas often arrive when you can't type. Voice to text works with any channel above — iOS and Android both offer voice keyboard input. The transcription won't be clean. That's fine. The parser handles it.

**One tip:** start voice notes with a signal word so you can filter later:

- "Hot idea:" → signal: hot
- "Note to self:" → signal: warm
- "Weird thought:" → signal: cool

You can write a simple pre-processing step that detects these prefixes and sets the signal field automatically.

---

## Scheduling ingestion

Once your ingestion script is working, wire it as an OpenClaw cron:

```bash
openclaw cron add \
  --name "second-brain-ingest" \
  --cron "0 8 * * *" \
  --message "Run second brain ingestion from [channel]. Read new messages since last run, append as atoms to memory/second-brain.md, update last-read timestamp." \
  --model "maple/gpt-oss-120b"
```

Daily at 8am is a good default. Weekly works if you're a lighter dropper.

---

## After ingestion

Once new atoms are in the ledger, run:

```bash
node references/parser.js   # extract atoms to JSON
node references/cluster.js  # re-cluster with new material
```

Or trigger this from Mission Control's Re-cluster button if you're running the full visualizer.


path: references/cluster.js
content: #!/usr/bin/env node
/**
 * cluster-second-brain.js
 * Reads second-brain-atoms.json, calls your local OpenClaw gateway LLM,
 * writes second-brain-clusters.json
 *
 * Usage: node scripts/cluster-second-brain.js
 */

const fs = require('fs');
const path = require('path');

const ATOMS_FILE = process.env.SBV_ATOMS_FILE || path.join(__dirname, '../data/second-brain-atoms.json');
const OUT_FILE   = process.env.SBV_CLUSTERS_FILE || path.join(__dirname, '../data/second-brain-clusters.json');
const MODEL = process.env.SBV_MODEL || 'openclaw:main';

// Reads gateway config from credentials file (mirrors Ghost Pro credential pattern)
const CREDS_FILE = path.join(
  process.env.HOME || process.env.USERPROFILE || '~',
  '.openclaw', 'credentials', 'openclaw-gateway.json'
);

function loadGatewayCreds() {
  if (!fs.existsSync(CREDS_FILE)) {
    console.error(
      `[SBV] Missing credentials file: ${CREDS_FILE}\n` +
      'Create it with: { "host": "127.0.0.1", "port": 18789, "key": "<your-gateway-key>" }'
    );
    process.exit(1);
  }
  const creds = JSON.parse(fs.readFileSync(CREDS_FILE, 'utf8'));
  if (!creds.key) {
    console.error('[SBV] openclaw-gateway.json must include a "key" field.');
    process.exit(1);
  }
  const host = creds.host || '127.0.0.1';
  if (host !== '127.0.0.1' && host !== 'localhost') {
    console.warn(
      `[SBV] Warning: gateway host is "${host}". ` +
      'This will send your atom corpus to a remote host. ' +
      'Set host to 127.0.0.1 in openclaw-gateway.json to keep data local.'
    );
  }
  return { host, port: creds.port || 18789, key: creds.key };
}

// All LLM calls go to the configured local gateway — no external API calls
function callLLM(gatewayKey, gatewayHost, gatewayPort, prompt) {
  return new Promise((resolve, reject) => {
    const body = JSON.stringify({
      model: MODEL,
      max_tokens: 4096,
      temperature: 0.3,
      messages: [{ role: 'user', content: prompt }],
    });

    const req = require('http').request({
      hostname: gatewayHost,
      port: gatewayPort,
      path: '/v1/chat/completions',
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${gatewayKey}`,
        'Content-Length': Buffer.byteLength(body),
      },
    }, (res) => {
      let data = '';
      res.on('data', chunk => data += chunk);
      res.on('end', () => {
        try {
          const parsed = JSON.parse(data);
          if (parsed.error) return reject(new Error(JSON.stringify(parsed.error)));
          const text = parsed.choices?.[0]?.message?.content ?? '';
          resolve(text);
        } catch (e) {
          reject(new Error('Failed to parse response: ' + data.slice(0, 200)));
        }
      });
    });

    req.on('error', reject);
    req.write(body);
    req.end();
  });
}

async function main() {
  if (!fs.existsSync(ATOMS_FILE)) {
    console.error('Run build-second-brain.js first');
    process.exit(1);
  }

  const { host: GATEWAY_HOST, port: GATEWAY_PORT, key: gatewayKey } = loadGatewayCreds();

  const { atoms, atomCount } = JSON.parse(fs.readFileSync(ATOMS_FILE, 'utf8'));
  // log(`Clustering ${atomCount} atoms…`);

  const corpus = atoms.map(a =>
    `[${a.id}] (${a.date?.slice(0, 10) ?? '?'}) ${a.raw}`
  ).join('\n');

  const prompt = `You are a cognitive analyst reading the unfiltered thought stream of a single person over an extended period of time. Your input is a corpus of raw personal notes — voice to text, incomplete, misspelled, joke-form, fragment-form, action-item-form. Treat low fidelity as intentional. The roughness is not a bug. It is what unguarded thinking looks like.

Your job is not to categorize what was said. It is to identify what the person is becoming.

WHAT YOU ARE LOOKING FOR

Beneath every note is a layer of intent. A joke about AI models and computational overhead is not about overhead — it is a probe into the transactional nature of machine intelligence. Read the joke. Then ask: what is the person actually working out?

Look for:
- Strategic direction: what is this person building toward, even when they don't say it directly?
- Recurring preoccupations: what questions keep returning in different forms, different domains, different registers?
- Underlying tensions: where is the person arguing with themselves across multiple notes?
- Aesthetic commitments: what do the design observations, humor choices, and word selections reveal?

HOW TO CLUSTER

Group atoms by affinity of intent — not surface similarity of words. Two atoms belong in the same cluster if they are reaching toward the same underlying question, even if one is a joke about LLMs and the other is a note about Simone Weil.

Do not impose categories onto the data. Let the categories emerge from the mass and direction of the atoms.

CLUSTER REQUIREMENTS
- Minimum 3 atoms to form a cluster
- Minimum 3, maximum 10 clusters total — determined by the data
- A cluster that was strong months ago and gone quiet is FADING, not absent

FOR EACH CLUSTER use exactly these fields:
- id: stable kebab-case string
- name: sharp specific name capturing underlying drive, NOT generic domain labels
- insight: one sentence — what does this pattern reveal about how this person thinks?
- atom_ids: array of atom id strings
- confidence: number 0.0 to 1.0
- status: string, one of: ESTABLISHED, FORMING, FADING
- time_spread: integer, number of distinct calendar weeks this theme spans

TOP LEVEL OUTPUT FIELDS (all required):
- clusters: array of cluster objects
- emerging_signals: array of atom id strings (atoms with distinct intent but not enough mass for a cluster)
- tensions: array of objects with fields: name (string), atom_ids (array), description (string)
- absences: array of strings naming themes notably absent given the overall profile

RULES:
- Do NOT name a cluster after a domain: not Technology, Business, Humor, Writing
- Do NOT group by keyword overlap — intent makes a cluster
- Do NOT invent themes the data doesn't support
- Output ONLY valid JSON, no prose, no markdown fences

CORPUS:
${corpus}`;

  try {
    const response = await callLLM(gatewayKey, GATEWAY_HOST, GATEWAY_PORT, prompt);
    
    // Extract JSON
    const jsonMatch = response.match(/\{[\s\S]*\}/);
    if (!jsonMatch) throw new Error('No JSON found in response');
    
    const clusters = JSON.parse(jsonMatch[0]);
    
    const output = {
      generated: new Date().toISOString(),
      atomCount,
      ...clusters,
    };

    fs.writeFileSync(OUT_FILE, JSON.stringify(output, null, 2));
    // log(`✓ ${clusters.clusters?.length ?? 0} clusters written to ${OUT_FILE}`);
    clusters.clusters?.forEach(c => {
      // log(`  [${c.status}] ${c.name} — ${c.atom_ids?.length} atoms, ${c.time_spread}w spread`);
    });
    // log(`  ${clusters.emerging_signals?.length ?? 0} emerging signals`);
    // log(`  ${clusters.tensions?.length ?? 0} tensions`);
  } catch (e) {
    console.error('Clustering failed:', e.message);
    process.exit(1);
  }
}

main();


path: references/parser.js
content: #!/usr/bin/env node
/**
 * build-second-brain.js
 * Parses second-brain.md into atoms JSON for the SBV clustering pipeline.
 * Output: data/second-brain-atoms.json
 */

const fs = require('fs');
const path = require('path');

// Configure via environment variables — see references/install.md
const VAULT   = process.env.OPENCLAW_VAULT || process.env.VAULT_DIR || path.join(process.env.HOME, 'vault');
const SB_FILE = process.env.SBV_LEDGER_FILE || path.join(VAULT, 'memory/second-brain.md');
const OUT_FILE = process.env.SBV_ATOMS_FILE || path.join(__dirname, '../data/second-brain-atoms.json');

function parseAtoms(md) {
  const atoms = [];
  // Split on atom headers — ### ts: <timestamp>
  const sections = md.split(/(?=### ts: \d+)/);

  for (const section of sections) {
    const tsMatch = section.match(/### ts: (\d+)/);
    if (!tsMatch) continue;

    const ts = tsMatch[1];
    const dateMatch = section.match(/\*\*date:\*\*\s*(.+)/);
    const rawMatch = section.match(/\*\*raw:\*\*\s*"?([^"\n]+)"?/);
    const typeMatch = section.match(/\*\*type:\*\*\s*(.+)/);
    const tagsMatch = section.match(/\*\*tags:\*\*\s*(.+)/);
    const signalMatch = section.match(/\*\*signal:\*\*\s*(.+)/);
    const actionableMatch = section.match(/\*\*actionable:\*\*\s*(.+)/);
    const nextActionMatch = section.match(/\*\*nextAction:\*\*\s*(.+)/);
    const sourceTypeMatch = section.match(/\*\*source_type:\*\*\s*(.+)/);

    // Skip image-only atoms with no raw text
    const raw = rawMatch ? rawMatch[1].trim() : null;
    if (!raw || raw === '*(image only)*') continue;

    atoms.push({
      id: `sb-${ts}`,
      ts: parseInt(ts),
      date: dateMatch ? dateMatch[1].trim() : null,
      raw,
      type: typeMatch ? typeMatch[1].trim() : 'thought',
      source_type: sourceTypeMatch ? sourceTypeMatch[1].trim() : 'text',
      tags: tagsMatch ? tagsMatch[1].split(',').map(t => t.trim()) : [],
      signal: signalMatch ? signalMatch[1].trim() : 'cool',
      actionable: actionableMatch ? actionableMatch[1].trim() === 'yes' : false,
      nextAction: nextActionMatch ? nextActionMatch[1].trim() : null,
    });
  }

  return atoms;
}

function main() {
  if (!fs.existsSync(SB_FILE)) {
    console.error(`second-brain.md not found at ${SB_FILE}`);
    process.exit(1);
  }

  const md = fs.readFileSync(SB_FILE, 'utf8');
  const atoms = parseAtoms(md);

  const output = {
    generated: new Date().toISOString(),
    atomCount: atoms.length,
    dateRange: atoms.length ? {
      earliest: new Date(atoms[atoms.length - 1].ts * 1000).toISOString().slice(0, 10),
      latest: new Date(atoms[0].ts * 1000).toISOString().slice(0, 10),
    } : null,
    atoms,
  };

  fs.mkdirSync(path.dirname(OUT_FILE), { recursive: true });
  fs.writeFileSync(OUT_FILE, JSON.stringify(output, null, 2));
  // log(`Parsed ${atoms.length} atoms → ${OUT_FILE}`);
}

main();


path: references/sample-ledger.md
content: ---
title: "Second Brain — Sample Atom Ledger"
created: 2026-01-01
tags: [second-brain, sample, example]
status: example
---

# Second Brain — Sample Atom Ledger

This is a sample ledger showing what 30 atoms look like across different idea types, signals, and timeframes. Use it to test the full pipeline before you have your own data.

Run it through the parser to generate `second-brain-atoms.json`, then cluster to see what emerges.

---

### ts: 1735689600

- **date:** 2026-01-01T08:00:00 UTC
- **raw:** every tool I use should feel like it was made for me specifically, not a product designed for the largest possible audience
- **type:** thought
- **tags:** [tools, design, agency]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1735776000

- **date:** 2026-01-02T08:00:00 UTC
- **raw:** the gap between a good idea and a shipped product is almost entirely a project management problem not a creativity problem
- **type:** strategy
- **tags:** [competition, execution, HNO]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1735862400

- **date:** 2026-01-03T08:00:00 UTC
- **raw:** idea jar: bluetooth speaker that only plays music you've heard before — familiar comfort mode
- **type:** idea-jar
- **tags:** [product, consumer, comfort]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1736035200

- **date:** 2026-01-05T08:00:00 UTC
- **raw:** the difference between a writer and someone who writes is that the writer notices the thing before they sit down
- **type:** thought
- **tags:** [craft, observation, writing]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1736121600

- **date:** 2026-01-06T08:00:00 UTC
- **raw:** build a tool that turns your Slack dm history with yourself into a timeline of what you were worried about
- **type:** creative
- **tags:** [product, data, anxiety-mapping]
- **signal:** warm
- **actionable:** yes
- **nextAction:** sketch the data model

---

### ts: 1736294400

- **date:** 2026-01-08T08:00:00 UTC
- **raw:** language that's too clean is lying. the roughness is the honesty
- **type:** thought
- **tags:** [language, authenticity, writing]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1736380800

- **date:** 2026-01-09T08:00:00 UTC
- **raw:** most apps solve the wrong problem with perfect execution. figure out the right problem first
- **type:** strategy
- **tags:** [product, problem-finding, execution]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1736467200

- **date:** 2026-01-10T08:00:00 UTC
- **raw:** the roman concrete survived 2000 years because they used volcanic ash. what's the volcanic ash equivalent in software
- **type:** thought
- **tags:** [durability, software, materials]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1736553600

- **date:** 2026-01-11T08:00:00 UTC
- **raw:** comedy bit: AI customer service that keeps apologizing for things that weren't mistakes
- **type:** creative
- **tags:** [humor, AI, service]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1736726400

- **date:** 2026-01-13T08:00:00 UTC
- **raw:** the best interfaces don't ask you to learn anything new they just make the right thing obvious
- **type:** thought
- **tags:** [attention, economics, scarcity]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1736812800

- **date:** 2026-01-14T08:00:00 UTC
- **raw:** what if the onboarding for a new tool started by showing you what other people have built with it instead of explaining how it works
- **type:** strategy
- **tags:** [onboarding, UX, product]
- **signal:** hot
- **actionable:** yes
- **nextAction:** apply to next product landing page

---

### ts: 1736899200

- **date:** 2026-01-15T08:00:00 UTC
- **raw:** the aesthetic of a tool signals who it's for. ugly tools attract people who don't care about aesthetics. that filters your customer base in ways you didn't intend
- **type:** thought
- **tags:** [design, product, signaling]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1737072000

- **date:** 2026-01-17T08:00:00 UTC
- **raw:** idea: a writing prompt app that generates prompts based on what you've already written, not generic ones
- **type:** idea-jar
- **tags:** [writing, product, personalization]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1737158400

- **date:** 2026-01-18T08:00:00 UTC
- **raw:** a deadline is not a creative constraint it's a forcing function and those are different things with different results
- **type:** thought
- **tags:** [systems, design, friction]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1737244800

- **date:** 2026-01-19T08:00:00 UTC
- **raw:** most business plans are written for investors not customers. the document is the wrong format for the actual problem
- **type:** strategy
- **tags:** [business, writing, format]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1737417600

- **date:** 2026-01-21T08:00:00 UTC
- **raw:** the first sentence of anything is a contract with the reader about what kind of experience they're about to have
- **type:** thought
- **tags:** [writing, craft, contract]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1737504000

- **date:** 2026-01-22T08:00:00 UTC
- **raw:** idea jar: a physical notebook where every page is numbered and you can search by writing the page number into an app
- **type:** idea-jar
- **tags:** [analog, digital, search, product]
- **signal:** cool
- **actionable:** no
- **nextAction:**

---

### ts: 1737590400

- **date:** 2026-01-23T08:00:00 UTC
- **raw:** AI that helps you figure out what question to ask, not just answer the one you already have
- **type:** creative
- **tags:** [AI, questioning, product]
- **signal:** hot
- **actionable:** yes
- **nextAction:** think about how to prototype this

---

### ts: 1737763200

- **date:** 2026-01-25T08:00:00 UTC
- **raw:** the people who write about writing are usually not the best writers. the best writers are too busy writing
- **type:** thought
- **tags:** [writing, meta, observation]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1737849600

- **date:** 2026-01-26T08:00:00 UTC
- **raw:** market timing is mostly luck but preparation isn't. the people who catch waves are the people who were already in the water
- **type:** strategy
- **tags:** [timing, preparation, market]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1737936000

- **date:** 2026-01-27T08:00:00 UTC
- **raw:** the best tools disappear. you stop thinking about the tool and start thinking about the work
- **type:** thought
- **tags:** [design, tools, invisibility]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1738108800

- **date:** 2026-01-29T08:00:00 UTC
- **raw:** comedy bit: a consultant who charges more for saying less
- **type:** creative
- **tags:** [humor, consulting, brevity]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1738195200

- **date:** 2026-01-30T08:00:00 UTC
- **raw:** most meeting summaries are longer than the meeting should have been
- **type:** thought
- **tags:** [creativity, endurance, belief]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1738281600

- **date:** 2026-01-31T08:00:00 UTC
- **raw:** every city has a smell. every era does too. what does right now smell like
- **type:** creative
- **tags:** [observation, sensory, zeitgeist]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1738368000

- **date:** 2026-02-01T08:00:00 UTC
- **raw:** the question isn't whether AI will replace jobs. it's which jobs are actually just pattern matching that humans were doing expensively
- **type:** strategy
- **tags:** [AI, work, economics, automation]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1738540800

- **date:** 2026-02-03T08:00:00 UTC
- **raw:** what if pricing was a design decision, not a financial one
- **type:** thought
- **tags:** [pricing, design, product]
- **signal:** hot
- **actionable:** yes
- **nextAction:** apply to next product decision

---

### ts: 1738627200

- **date:** 2026-02-04T08:00:00 UTC
- **raw:** the most underrated skill in any creative field is knowing when something is done
- **type:** thought
- **tags:** [craft, completion, judgment]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1738713600

- **date:** 2026-02-05T08:00:00 UTC
- **raw:** idea: a browser extension that shows you how many times you've visited a page this month — just the number, nothing else
- **type:** idea-jar
- **tags:** [browser, attention, data, product]
- **signal:** warm
- **actionable:** no
- **nextAction:**

---

### ts: 1738886400

- **date:** 2026-02-07T08:00:00 UTC
- **raw:** simplicity is the byproduct of clarity about what you're trying to do. if the thing is complex you haven't figured out what it is yet
- **type:** thought
- **tags:** [simplicity, clarity, design]
- **signal:** hot
- **actionable:** no
- **nextAction:**

---

### ts: 1738972800

- **date:** 2026-02-08T08:00:00 UTC
- **raw:** the people who move the fastest aren't the ones with the best plan. they're the ones who are most comfortable being wrong
- **type:** strategy
- **tags:** [speed, iteration, risk-tolerance]
- **signal:** hot
- **actionable:** no
- **nextAction:**


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