✈️ Meeting Autopilot

Turn meeting transcripts into structured operational outputs — NOT just summaries.

Activation

This skill activates when the user mentions:

• "meeting transcript", "meeting notes", "meeting autopilot"
• "action items from meeting", "meeting follow-up"
• "process this transcript", "analyze this meeting"
• "extract decisions from meeting", "meeting email draft"
• Uploading or pasting a VTT, SRT, or text transcript

Permissions

permissions:
  exec: true          # Run extraction scripts
  read: true          # Read transcript files
  write: true         # Save history and reports
  network: true       # LLM API calls (Anthropic or OpenAI)

Requirements

• bash, jq, python3, curl (typically pre-installed)
• ANTHROPIC_API_KEY or OPENAI_API_KEY environment variable

Agent Workflow

Step 1: Get the Transcript

Ask the user for their meeting transcript. Accept any of:

• A file path to a VTT, SRT, or TXT file
• Pasted text directly in the conversation
• A file upload

Important: This skill does NOT do audio transcription. If the user has an audio/video file, suggest they use:

• Zoom/Google Meet/Teams built-in transcription
• Otter.ai or Fireflies.ai for recording + transcription
• whisper.cpp for local transcription

Step 2: Get Optional Context

Ask for (but don't require):

• Meeting title — helps with email subject lines and report headers
• If not provided, the skill derives it from the filename or uses "Meeting [date]"

Step 3: Run the Autopilot

Save the transcript to a temporary file if pasted, then run:

bash "$SKILL_DIR/scripts/meeting-autopilot.sh" <transcript_file> --title "Meeting Title"

Or from stdin:

echo "$TRANSCRIPT" | bash "$SKILL_DIR/scripts/meeting-autopilot.sh" - --title "Meeting Title"

The script handles all three passes automatically:

• Parse — normalize the transcript format
• Extract — pull out decisions, action items, questions via LLM
• Generate — create email drafts, ticket drafts, beautiful report

Step 4: Present the Report

The script outputs a complete Markdown report to stdout. Present it directly — the formatting is designed to look great in Slack, email, or any Markdown renderer.

The report includes:

• 📊 Overview table (counts by category)
• ✅ Decisions with rationale
• 📋 Action items table (owner, deadline, status)
• ❓ Open questions
• 🅿️ Parking lot items
• 📧 Follow-up email draft(s) — ready to send
• 🎫 Ticket/issue drafts — ready to file

Discord v2 Delivery Mode (OpenClaw v2026.2.14+)

When the conversation is happening in a Discord channel:

• Send a compact first summary (decision count, action-item count, top owners), then ask if the user wants full report sections.
• Keep the first response under ~1200 characters and avoid long tables in the first message.
• If Discord components are available, include quick actions:
• Show Action Items
• Show Follow-Up Email Draft
• Show Ticket Drafts
• If components are not available, provide the same follow-ups as a numbered list.
• Prefer short follow-up chunks (<=15 lines per message) for long reports.

Step 5: Offer Next Steps

After presenting the report, offer:

• "Want me to refine any of the email drafts?"
• "Should I adjust any action item assignments?"
• "Want to save this report to a file?"
• "I can also process another meeting — transcripts from different meetings build up a tracking history."

Error Handling

Notes for the Agent

• The report is the star. Present it in full. Don't summarize the summary.
• Follow-up emails are the WOW moment. Highlight them — they're ready to copy and send.
• Be proactive: After the report, suggest specific improvements based on what was found.
• Cross-meeting tracking: Items are automatically saved to ~/.meeting-autopilot/history/. Mention this — it's a preview of the v1.1 feature that tracks commitments across meetings.
• If the transcript has no speaker labels, mention that adding "Speaker: text" format improves attribution accuracy.

References

• scripts/meeting-autopilot.sh — Main orchestrator (the only entry point you need)
• scripts/parse-transcript.sh — Transcript parser (VTT/SRT/TXT → JSONL)
• scripts/extract-items.sh — LLM extraction + classification
• scripts/generate-outputs.sh — Operational output generation + report formatting