samarv/analyzing-meetings

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