Structured Minutes

Structured Minutes — Raw Notes to Structured Minutes SOP

Transforms raw meeting materials (transcripts, notes, chat logs) into professional, structured meeting minutes with automatic extraction of topics, decisions, and action items.

Quick Start

1. User provides raw meeting material (transcript / notes / pasted text)
2. Agent processes step by step following the SOP below
3. Outputs structured minutes; optionally runs scripts/validate_minutes.py to verify completeness
4. After user confirmation, exports as a Markdown file

SOP Workflow

Phase 1: Input Collection & Preprocessing

Goal: Identify the type of source material and fill in any missing metadata.

Steps:

1. Identify the material type and confirm with the user:

   • Speech-to-text transcript (ASR transcript)
   • Handwritten notes
   • IM chat log (Slack / Teams / Discord / etc.)
   • Mixed materials

2. Extract or ask for metadata (all fields are required):

   Field	Description	Example
   Meeting Title	Topic of the meeting	Q2 Product Review
   Date	YYYY-MM-DD format	2026-04-14
   Time	HH:MM-HH:MM	14:00-15:30
   Location / Format	Physical room or online tool	Zoom Meeting
   Facilitator	Meeting organizer	Alice Chen
   Recorder	Person writing the minutes	AI-assisted
   Attendees	List of all participants	Alice, Bob, Carol

3. If any of the above fields are missing from the material, proactively ask the user to fill them in. Do not guess attendees or dates.

Phase 2: Topic Identification & Segmentation

Goal: Split the continuous meeting content into distinct agenda topics.

Method:

1. Read through the entire text and identify topic transition points. Common signals:

   • Explicit topic introductions ("Next topic", "Moving on to", "Regarding XX")
   • Speaker change + subject change
   • Timestamp jumps (if available)

2. Number and name each topic using this format:

   Topic 1: [Concise title, ≤ 10 words]
   Topic 2: [Concise title, ≤ 10 words]
   ...
   

3. Special handling rules:

   • If a topic was interrupted and revisited later, merge into a single topic
   • Brief small talk or off-topic chat should not become a standalone topic — ignore or group under "Other"
   • Even if the entire meeting covers only one subject, explicitly label it as "Topic 1"

4. Present the topic list to the user for confirmation before proceeding.

Phase 3: Deep Extraction per Topic

Goal: Extract structured information for each topic.

For each topic, extract using the following template:

### Topic N: [Title]

**Background**: (1-2 sentences — why this was discussed)

**Discussion Highlights**:
- [Point 1]: [Key opinion / data / proposal] (Speaker: XX)
- [Point 2]: [Key opinion / data / proposal] (Speaker: XX)
- ...

**Disagreements**: (if any)
- [Issue]: Side A argues… / Side B argues…

**Decisions**:
- ✅ [Clear decision, stated as a declarative sentence]
- ✅ [List each decision separately if there are multiple]

**Action Items**:
| # | Task Description | Owner | Deadline | Priority |
|---|------------------|-------|----------|----------|
| 1 | [Specific, actionable task] | [Name] | YYYY-MM-DD | High/Med/Low |

Extraction rules:

• Discussion Highlights: Retain key information; remove repetitive or overly colloquial content. Each point ≤ 50 words.
• Decisions: Must be an agreed-upon outcome, not "to be continued." If no clear decision was reached, note "Pending: needs [condition] before revisiting."
• Action item criteria (all of the following must be met):
  • Has a clear "what to do" (verb + object)
  • Has a clear or inferable owner
  • Is a specific, executable task — not a directional statement
• Deadline handling:
  • Explicitly mentioned in the transcript → use directly
  • Vague expressions like "next week" / "end of month" → convert to a specific date and mark (estimated)
  • Not mentioned at all → mark "TBD" and flag it for the user in notes
• Priority assessment:
  • High: Blocks other work / has a clearly urgent deadline / was emphasized repeatedly
  • Medium: Has a deadline but not urgent / routine follow-up
  • Low: Nice-to-have / exploratory task

Phase 4: Cross-Topic Global Extraction

Goal: Extract information that spans across topics.

1. Open Issues (items with no conclusion that need further discussion):

   ## Open Issues
   | # | Description | Related Topic | Next Steps |
   |---|-------------|---------------|------------|
   | 1 | [Issue] | Topic N | [Discuss next meeting / Waiting on XX for more info] |
   

2. Risk Alerts (potential risks identified during the summarization process):

   ## ⚠️ Risk Alerts
   - [Risk 1]: [Description] (Source: Topic N)
   - [Risk 2]: [Description]
   

   Common risk signals: deadline conflicts, insufficient resources, unclear dependencies, action items with no owner.

3. Key Metrics (specific numbers mentioned during the meeting):

   ## Key Metrics
   - [Metric name]: [Value] (Source: Topic N)
   

Phase 5: Assembly & Output

Goal: Assemble all extracted results into complete minutes.

Output template:

# Meeting Minutes: [Meeting Title]

| Field | Details |
|-------|---------|
| Date | YYYY-MM-DD |
| Time | HH:MM - HH:MM |
| Location | [Location / online tool] |
| Facilitator | [Name] |
| Recorder | [Name] |
| Attendees | [List of names] |

---

## Topic Overview

| Topic | Decision Status | Action Items |
|-------|----------------|--------------|
| Topic 1: [Title] | ✅ Decided / ⏳ Pending | N |
| Topic 2: [Title] | ✅ Decided / ⏳ Pending | N |

---

## Detailed Record

### Topic 1: [Title]
(Full content extracted in Phase 3)

### Topic 2: [Title]
(Full content extracted in Phase 3)

---

## Action Items Summary

| # | Task Description | Owner | Deadline | Priority | Source Topic |
|---|------------------|-------|----------|----------|--------------|
| 1 | [Task] | [Name] | YYYY-MM-DD | High/Med/Low | Topic N |
| ... | | | | | |

## Open Issues
(Phase 4 content)

## ⚠️ Risk Alerts
(Phase 4 content — omit this section if none)

## Key Metrics
(Phase 4 content — omit this section if none)

Phase 6: Quality Check

Goal: Ensure the minutes are complete, accurate, and actionable.

Automated checklist (check each item and report):

• All metadata fields are filled (no "unknown" or blank values)
• Every topic has a decision (even if marked "Pending")
• Every action item has an owner (no "TBD" owners)
• Every action item has a deadline (may be "TBD" but must be noted)
• Action items summary count = sum of per-topic action items
• No names appear that were not mentioned in the source material (guard against hallucination)
• Date format is consistently YYYY-MM-DD
• Data cited in the minutes matches the source

Validation script: After completing the minutes, you can run scripts/validate_minutes.py to perform structural validation on the output Markdown file.

python3 scripts/validate_minutes.py <minutes_file.md>

The script checks:

• Whether required sections are present
• Whether the action item table format is complete
• Whether deadline formats are valid
• Whether owner fields are empty
• Whether topic overview and detailed record counts match

Phase 7: Delivery & Follow-up

1. Present the minutes to the user for review, focusing on:

   • "Are the action items accurate? Anything missing?"
   • "Do the decisions reflect what was actually discussed?"
   • "Is there anything that needs to be added or changed?"

2. Revise based on user feedback until the user is satisfied.

3. Export options:

   • Save as a Markdown file
   • If the user needs another format (Google Docs, Word, etc.), suggest using the corresponding skill for conversion

Configuration

This skill requires no external parameters. The following are optional customizations:

Common Scenarios

Scenario 1: Transcript Cleanup

User pastes a transcript exported from Otter.ai, Fireflies, or a similar tool. Agent follows the SOP to produce structured minutes.

Scenario 2: Chat Log Organization

User pastes a Slack thread or Teams chat. Agent identifies topics and extracts action items.

Scenario 3: Handwritten Notes

User pastes bullet points jotted down during the meeting. Agent adds structure and confirms any gaps.

Scenario 4: Cross-Timezone Multilingual Meeting

User provides a transcript in any language. Agent processes it with the same workflow and outputs minutes in the user's preferred language.