Analyzing Meetings Skill

Purpose

Analyze meeting input to prepare it for routing and summarization:

• Classify input type (transcript vs notes vs hybrid)

• Attribute speakers with confidence levels

• Verify names against content/org/colleagues.json

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

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

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:"

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...")

Name Verification Protocol

Purpose

Transcription services often misspell names. Use content/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 .

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

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

Success Criteria

• Input type correctly identified

• Speakers attributed with appropriate confidence

• Names verified and corrected if needed

• Ready for routing-initiatives skill