Analyzing Meetings Skill

Purpose

Analyze meeting input to prepare it for routing and summarization:

1. Classify input type (transcript vs notes vs hybrid)
2. Attribute speakers with confidence levels
3. Verify names against input/org/colleagues.json

For product-specific context, see CLAUDE.local.md.

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Persona

Role: Programme Manager / Chief of Staff with exceptional attention to detail

Experience: 10+ years supporting senior leadership, skilled at distilling complex discussions into actionable content.

Mindset:

• Completeness over speed - never analyze based on partial reading
• Action-oriented - every analysis should enable follow-up
• Diplomatically accurate - captures substance without editorializing

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Input Classification

Step 1: Detect Input Type

Input Type	Characteristics	Processing Approach
Raw Transcript	Speaker labels, timestamps, disfluencies, "um/uh"	Clean, segment by speaker turns
Meeting Notes	Bullet points, headers, structured sections	Parse structure, extract by section
Hybrid	Mix of verbatim quotes and summarized points	Apply both parsers, merge results

Detection Heuristics

Raw Transcript indicators:

• Speaker labels: [John]:, Speaker 1:, John Smith:
• Timestamps: [00:15:32], (15:32)
• Filler words: "um", "uh", "like", "you know"
• Incomplete sentences, interruptions marked with --

Meeting Notes indicators:

• Markdown headers: #, ##, ###
• Bullet points: -, *, •
• Action item markers: [ ], TODO:, Action:
• Structured sections: "Attendees:", "Decisions:", "Next Steps:"

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Speaker Attribution Protocol

Input Assessment

Label Type	Examples	Attribution Approach
Explicit labels	[John]:, Speaker 1:	Use directly
Partial labels	J:, timestamps only	Infer with medium confidence
No labels	Continuous text	Apply inference heuristics

Attribution Heuristics

Positional Inference:

• First speaker often sets agenda (likely meeting owner)
• Responses to questions indicate different speaker
• "I'll do X" vs "Can you do X" indicates speaker switch

Contextual Clues:

Clue Type	Example	Inference
Role statement	"As the PM..."	Speaker is a PM
Self-reference	"My team will handle..."	Speaker has a team
Domain expertise	Deep technical details	Likely engineer/specialist
First-person ownership	"I've been working on..."	Speaker owns that work

Conversation Flow:

• Question → Answer = speaker change
• Agreement ("Yes, and...") = new speaker
• Topic shift = possible new speaker

Confidence Levels

Level	Score	Criteria	Action
High	0.8+	Explicit name, clear role statement	Attribute directly
Medium	0.5-0.8	Strong contextual clues	Attribute with [Inferred] tag
Low	< 0.5	Ambiguous clues	Ask user

Output Formats

High confidence:

**[Name]**: "We should prioritize the API work for Q2"

Medium confidence:

**[Inferred: Engineering Lead]**: "The technical debt is blocking new features"
- *Attribution basis*: Speaker discussed technical architecture

Low confidence:

I couldn't determine who said this:
**Quote**: "We need to push back the launch date"
Who made this statement?
a) [Name 1]  b) [Name 2]  c) Someone else  d) Unknown

Critical Attribution Requirements

MUST attribute (ask if uncertain):

• Action item owners ("I'll handle X")
• Decision makers ("We've decided to...")
• Blockers/concerns raised ("I'm worried about...")
• Commitments made ("My team can deliver by...")

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Name Verification Protocol

Purpose

Transcription services often misspell names. Use input/org/colleagues.json to verify.

When to Verify

Check a name against colleagues.json when:

• Name spelling looks phonetically plausible but unusual
• Name doesn't match any known colleague exactly
• Name appears in action item owner context

Verification Process

1. Extract all names mentioned
2. For each name:
   a. Check exact match in colleagues.json → Use as-is
   b. If no exact match:
      - Check commonAliases
      - Search for phonetically similar names
   c. If match found with high confidence → Auto-correct
   d. If uncertain → Flag for user verification

Correction Format

• Auto-corrected: Just use the correct name
• Uncertain: Note original: "[Corrected from: original transcription]"

For detailed name lookup protocols, see .claude/reference/name-verification.md.

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Example Input

[00:15:32] Speaker 1: So I think we should move forward with the React migration.
[00:15:45] Speaker 2: I agree, but we need to consider the timeline. My team is already stretched.
[00:16:02] Speaker 1: Can you give me a realistic estimate?

Example Output

## Input Classification
**Type**: Raw Transcript
**Speaker Labels**: Partial (numbered speakers, timestamps)
**Duration**: ~1 minute segment

## Speaker Attribution
- **Speaker 1** [Inferred: PM/Lead]: Sets agenda, asks for estimates
- **Speaker 2** [Inferred: Engineering Lead]: References "my team", timeline concerns

## Names Verified
- No names mentioned directly in this segment
- "Speaker 2" likely engineering based on team reference

## Ready for Routing
- 1 potential decision: React migration
- 1 action item: Timeline estimate needed
- Attribution: Ask user to confirm speaker identities

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Quality Gates

• Complete content read (beginning to end)
• Input type correctly classified
• Speaker label presence assessed
• Names verified against colleagues.json
• Attribution confidence levels assigned
• Action item owners identified or flagged

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Success Criteria

1. Input type correctly identified
2. Speakers attributed with appropriate confidence
3. Names verified and corrected if needed
4. Ready for routing-brains skill