Meeting Summary Generator

Extract action items, decisions, and key points from meeting transcripts. Automatically routes items to tasks.md or GitHub Issues.

Usage

/summarize_meeting [transcript_file]
/summarize_meeting .research/meetings/transcripts/2024-12-02-lab-meeting.md

When to Use

• After running /transcribe
• On any meeting transcript or notes
• To process handwritten meeting notes (type them first)

Prerequisites

• Transcript exists in .research/meetings/transcripts/ folder
• Transcript is in markdown format

Execution Steps

1. Load Transcript

Read the meeting transcript and project context:

• .research/meetings/transcripts/[filename].md - The transcript
• .research/project_telos.md - Project aims (for context)
• tasks.md - Current tasks (avoid duplicates)

2. Extract Key Information

Analyze transcript for:

• Decisions made
• Action items (who, what, when)
• Questions raised
• Key insights or ideas
• Follow-up needed

3. Generate Meeting Summary

Append summary to transcript or create separate file:

---

# Meeting Summary

## Key Decisions
<!-- Decisions that were made during the meeting -->
1. [Decision 1] - [Brief context]
2. [Decision 2] - [Brief context]

## Action Items

### Tasks (< 2 hours, single implementation)
<!-- These will be added to tasks.md -->

| Item | Owner | Due | Priority |
|------|-------|-----|----------|
| [Task description] | [Name/@you] | [Date/ASAP] | [High/Med/Low] |
| [Task description] | [Name/@you] | [Date] | [Priority] |

### Issues (> 2 hours, needs tracking)
<!-- These will become GitHub Issues -->

1. **[Issue title]**
   - Description: [What needs to be done]
   - Why: [Why this is needed]
   - Complexity: [Estimate]
   - Labels: [Suggested labels]

2. **[Issue title]**
   - Description: [Details]
   - Why: [Rationale]

## Key Insights
<!-- Important points or ideas worth remembering -->
- [Insight 1]
- [Insight 2]

## Open Questions
<!-- Questions that weren't resolved -->
- [ ] [Question 1] - Needs: [Who/what to resolve]
- [ ] [Question 2] - Needs: [Who/what to resolve]

## Follow-up Needed
<!-- Things to discuss or check on later -->
- [Follow-up item]

## Next Meeting
<!-- If discussed -->
- **Date**: [If scheduled]
- **Agenda items**: [If mentioned]

---

*Summary generated: [Timestamp]*

4. Task vs Issue Classification

Apply this heuristic:

When uncertain, ask:

I found this action item: "[Item description]"

This could be:
A) A quick task (< 2 hours, add to tasks.md)
B) A larger issue (needs GitHub Issue for tracking)

Which fits better? (Or provide more context)

5. Update tasks.md

Add new tasks with meeting reference:

## From Meeting: 2024-12-02-lab-meeting

- [ ] [Task 1] (Due: [date])
- [ ] [Task 2] (Due: [date])
- [ ] [Task 3]

6. Create GitHub Issues

For items classified as issues, offer to create:

I identified 2 items that should be GitHub Issues:

1. "Compare SVM vs Random Forest for classification"
   - Would require testing both approaches
   - Results should be documented for paper
   
2. "Implement alternative normalization method"
   - Needs research into options
   - May change downstream pipeline

Create these as GitHub Issues? (Y/n)

If yes, create issues with:

• Clear title
• Description from meeting context
• Labels (if determinable)
• Reference to meeting transcript

7. Post-Summary Actions

Meeting summarized!

Summary added to: .research/meetings/transcripts/2024-12-02-lab-meeting.md
Tasks added: 3 new items in tasks.md
Issues to create: 2 (awaiting confirmation)

Next steps:
A) Create the GitHub Issues
B) Review and prioritize new tasks
C) Update project_telos.md with decisions made
D) Continue with other work

What would you like to do?

Example Output

# Meeting Summary

## Key Decisions
1. Use random forest as primary classifier (SVM as comparison)
2. Deadline for analysis: end of month
3. Weekly check-ins moving to Tuesdays

## Action Items

### Tasks
| Item | Owner | Due | Priority |
|------|-------|-----|----------|
| Fix axis labels on Figure 2 | @you | Dec 4 | Low |
| Send PI the draft methods section | @you | Dec 3 | High |
| Update README with new instructions | @you | Dec 5 | Med |

### Issues
1. **Compare SVM vs Random Forest performance**
   - Description: Run both classifiers with same CV scheme, compare metrics
   - Why: Reviewer may ask about method choice
   - Complexity: ~4-6 hours
   - Labels: analysis, methodology

## Key Insights
- PI suggested looking at recent paper by Smith et al. on normalization
- Feature importance might be more interesting than just accuracy

## Open Questions
- [ ] Which normalization method should we use? - Needs: literature review
- [ ] Include supplementary figures in main text? - Needs: check journal guidelines

Related Skills

• transcribe - Generate transcript from audio
• weekly-review - See tasks in context of weekly work
• plan-week - Incorporate new tasks into weekly plan
• next - Get next suggestion

Notes

• Review summaries for accuracy - AI may misinterpret discussion
• Action items should have clear owners
• Link issues back to meeting for context
• Consider who said what for attribution