sampling-bluesky-zeitgeist

# Sampling Bluesky Zeitgeist

**⚠️ DEPRECATED: This skill has been consolidated into the `browsing-bluesky` skill.**

Use `browsing-bluesky` for firehose sampling via the `sample_firehose()` function.

---

## Legacy Documentation

Capture and analyze multiple windows of Bluesky firehose data, identify content clusters, and present results showing both individual sample windows and aggregate trends.

## Workflow

### 1. Setup

Install dependencies:

```bash
cd /home/claude && npm install ws https-proxy-agent 2>/dev/null
```

### 2. Run Sample Windows

Execute 3 consecutive 10-second samples:

```bash
node /mnt/skills/user/sampling-bluesky-zeitgeist/scripts/zeitgeist-sample.js --duration 10 > /home/claude/sample1.json 2>/dev/null
node /mnt/skills/user/sampling-bluesky-zeitgeist/scripts/zeitgeist-sample.js --duration 10 > /home/claude/sample2.json 2>/dev/null
node /mnt/skills/user/sampling-bluesky-zeitgeist/scripts/zeitgeist-sample.js --duration 10 > /home/claude/sample3.json 2>/dev/null
```

### 3. Analyze & Build DATA Object

Parse each JSON file and aggregate:
- Sum totalPosts, calculate avgPostsPerSecond
- Merge topWords, topPhrases, entities across windows
- Calculate per-minute rates: `(count / totalDurationSec) * 60`
- Detect trends by comparing window counts

### 4. Identify Content Clusters

Group related terms into thematic clusters. For each cluster:
- **name**: Descriptive label
- **emoji**: Visual identifier
- **terms**: Keywords that belong to this cluster
- **searchQuery**: OR-joined terms for Bsky search (e.g., `"trump OR republican OR congress"`)
- **totalMentions**: Sum across all windows
- **mentionsPerMin**: `(totalMentions / totalDurationSec) * 60`
- **trend**: "up" if last window > first by 30%, "down" if <30%, else "stable"
- **samples**: 2-3 example posts matching cluster

### 5. Create Artifact via Template

Copy template and inject DATA:

```bash
cp /mnt/skills/user/sampling-bluesky-zeitgeist/assets/zeitgeist-template.html /mnt/user-data/outputs/zeitgeist.html
```

Then use str_replace to inject the DATA object:

```
old_str: const DATA = {"aggregate":{"totalPosts":0,"totalDurationSec":30,"avgPostsPerSecond":0,"timestamp":""},"windows":[],"clusters":[],"entities":[],"phrases":[],"languages":{}};
new_str: const DATA = {YOUR_ACTUAL_DATA_OBJECT};
```

See `references/artifact-template.md` for the complete DATA schema.

## Output Format

Present the artifact link, then provide a brief prose summary:
- What's dominating the conversation (top 1-2 clusters)
- Any notable velocity spikes
- Interesting patterns (e.g., "Japanese-language posts spiking around [topic]")

Keep summary to 2-3 sentences. The artifact is the main deliverable.

## Refresh Workflow

When user asks to refresh/update/sample again:

1. Run new sample windows (same 3x10s pattern)
2. Update the DATA object with new results
3. Use str_replace to swap the DATA line in the existing artifact
4. Report what changed: "Trump mentions up 20% from last sample, M23 discussion fading"

For comparison, track previous aggregate totals and show delta:
```
Previous: trump 50/min → Current: trump 62/min (+24%)
```

## Topic Monitoring Workflow

When user specifies a topic to monitor (e.g., "track the Lakers game", "what's happening with the drone sightings"):

### Option A: Filtered Sampling
Run samples with the --filter flag to capture only matching posts:

```bash
node zeitgeist-sample.js --duration 15 --filter "lakers" > /home/claude/topic1.json 2>/dev/null
```

This gives deeper analysis of a specific topic but misses broader context.

### Option B: General + Topic Velocity (Recommended)
Run general samples, then:
1. Calculate topic-specific velocity from the results
2. Add a dedicated "Monitored Topic" cluster at the top
3. Include sample posts matching the topic

In the DATA object, add a `monitoredTopic` field:
```json
{
  "monitoredTopic": {
    "query": "lakers",
    "totalMentions": 45,
    "mentionsPerMin": 90.0,
    "trend": "up",
    "windowCounts": [12, 15, 18],
    "samples": ["Lakers up by 10 in the 4th!", "LeBron with another triple double"]
  }
}
```

### Responding to Topic Requests

**At start of conversation:**
- "What's happening with the drone sightings?" → Run general sample, highlight drone-related content as monitored topic
- "Track mentions of Claude AI" → Same approach, focus on that term

**Mid-conversation:**
- "Can you focus on the M23 conflict?" → Re-run samples, add M23 as monitored topic
- "What about Japanese content specifically?" → Filter for lang:ja or highlight existing Japanese cluster

**Follow-up pattern:**
User: "refresh but focus on the game"
→ Run new samples, keep monitored topic, update all data

## Error Handling

If WebSocket connection fails:
- Check that `*.bsky.network` is in allowed domains
- Retry once with shorter duration (5s)
- If still failing, report the error and suggest user check network settings

If sample returns zero posts:
- Likely network/proxy issue
- Report and do not create artifact

## Customization Options

User may request:
- **Longer samples**: Increase duration per window or add more windows
- **Topic focus**: After initial sample, run filtered samples for specific clusters
- **Comparison**: Run samples at different times and compare