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:

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

2. Run Sample Windows

Execute 3 consecutive 10-second samples:

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:

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:

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:

{
  "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

Source: oaustegard/claude-skills — distributed by TomeVault.