Meeting Synthesis

Meeting Synthesis

Purpose

Extract actionable product signals from meeting transcripts:

• Feature requests and capability asks
• Pain points and friction areas
• Customer quotes and verbatim feedback
• Evidence for epic creation and prioritization
• Pattern detection across multiple customers

When to Use This Skill

Activate automatically when:

• product-planning workflow gathers signals for epics
• cs-prep workflow compiles customer context for QBRs
• strategy-session workflow needs customer insights
• User explicitly requests meeting synthesis
• Any workflow requires customer evidence extraction

Signal Types

1. Feature Requests ("Asks")

Explicit requests for new capabilities or enhancements.

Indicators:

• "We need..."
• "Can you build..."
• "It would be great if..."
• "Our team wants..."

Extract:

• What capability is requested?
• Why is it needed? (business impact)
• Who requested it? (customer, role)
• When? (meeting date)

2. Pain Points ("Problems")

Friction, blockers, or inefficiencies users experience.

Indicators:

• "It's frustrating that..."
• "We struggle with..."
• "The current process is..."
• "Our team spends too much time..."

Extract:

• What is the pain point?
• How does it impact workflow/​outcomes?
• Frequency/​severity?
• Who experiences it?

3. Onboarding Friction

Specific difficulties during setup or first value realization.

Indicators:

• "Setup was confusing..."
• "Took us X weeks to..."
• "Couldn't figure out how to..."
• "Documentation unclear on..."

Extract:

• What step caused friction?
• How long did it take?
• Was it eventually resolved?
• Suggestions for improvement?

4. Integration Gaps

Missing connectors or data sync capabilities.

Indicators:

• "We use [Tool X] but can't connect..."
• "Data sync with [Platform Y]..."
• "Export to [System Z]..."

Extract:

• What integration is needed?
• What data needs to flow?
• Current workaround (if any)?
• Business impact of gap?

5. Performance Needs

Speed, scale, or reliability requirements.

Indicators:

• "Too slow when..."
• "Times out on..."
• "Can't handle X records..."
• "Need real-time..."

Extract:

• What operation is slow?
• Current performance vs. needed?
• Scale requirements?
• Impact on usage?

Synthesis Process

1. Determine Time Window

Inputs:

• days: Explicit lookback window (e.g., 7, 14, 30)
• last_run: Timestamp from state file (e.g., datasets/​product/​.meetings-to-backlog-state.json)
• Default: 3 days if no inputs provided

Calculate cutoff date:

If days provided:
  cutoff_date = current_date - days
Else if last_run exists:
  cutoff_date = last_run timestamp
Else:
  cutoff_date = current_date - 3 days

2. Collect Meeting Files

Scan paths:

datasets/​meetings/​Customers/​**/​YYYY/​*.md  (where date >= cutoff_date)
datasets/​meetings/​Internal/​Product/​YYYY/​*.md  (where date >= cutoff_date)

Use Glob to find files:

Glob pattern: datasets/​meetings/​Customers/​**/​2025/​*.md
Glob pattern: datasets/​meetings/​Internal/​Product/​2025/​*.md

Filter by date:

• Extract date from filename (YYYY-MM-DD prefix)
• Compare to cutoff_date
• Keep only files >= cutoff_date

3. Extract Signals from Each Meeting

For each meeting file:

A. Parse frontmatter:

date: "YYYY-MM-DD"
type: "sales|product|customersuccess|..."
customer: "Company Name"
participants: ["Person A", "Person B"]
tags: ["2025Q3", "keyword"]

B. Read sections:

• ## ⬇️ AI Summary → High-level signals
• ## ⬇️ Action Items → Commitments and follow-ups
• ## ⬇️ Full Transcript → Detailed context and quotes

C. Identify signal candidates: Use pattern matching:

Feature requests: grep -i "we need\|can you\|would be great\|want to" transcript
Pain points: grep -i "frustrat\|struggle\|difficult\|problem\|issue" transcript
Onboarding: grep -i "setup\|onboard\|getting started\|first" transcript
Integrations: grep -i "integrat\|connect\|sync\|export" transcript
Performance: grep -i "slow\|timeout\|performance\|scale" transcript

D. Extract verbatim quotes:

• Capture 1-3 sentence snippets around signal indicators
• Preserve exact wording for evidence
• Include speaker attribution if available

E. Record metadata: For each signal:

{
  "signal_text": "Verbatim quote or paraphrase",
  "signal_type": "ask|problem|friction|integration|performance",
  "customer": "Company Name",
  "date": "YYYY-MM-DD",
  "meeting_type": "sales|product|customersuccess",
  "source_file": "/​path/​to/​meeting.md",
  "section": "AI Summary|Action Items|Full Transcript"
}

4. Cluster Signals

Semantic clustering:

• Group similar signals across customers
• Identify common themes
• Label clusters with action-style names (Verb + Object + Outcome)

Examples:

• "Revamp onboarding to reduce TTFV" (multiple onboarding friction signals)
• "Add Google Sheets export capability" (multiple export/​integration requests)
• "Improve dashboard load performance" (multiple performance complaints)

For each cluster:

{
  "cluster_label": "Action-style theme name",
  "mention_count": N,  # Total signal occurrences
  "unique_accounts": N,  # Distinct customers
  "unique_functions": N,  # Customer vs. internal sources
  "signals": [array of signal objects],
  "recentness_weight": calculated_score  # Newer = higher
}

5. Compute Signal Metrics

For each cluster:

A. Mention count: Total occurrences across all meetings.

B. Unique accounts: Distinct customer names in signal sources.

C. Unique functions: Count of different source types:

• External customers
• Internal product team
• Partner/​agency feedback

D. Recentness weight:

For each signal in cluster:
  days_ago = current_date - signal.date
  recentness_score = max(0, 30 - days_ago) / 30  # Linear decay over 30 days
Average recentness across all signals in cluster

E. Source diversity: Mix of meeting types:

• Sales calls
• Product feedback sessions
• Customer success check-ins
• Onboarding calls

Higher diversity = stronger signal.

6. Output Synthesized Data

Return structured object:

{
  "time_window": {
    "start_date": "YYYY-MM-DD",
    "end_date": "YYYY-MM-DD",
    "days_scanned": N,
    "meetings_processed": N
  },
  "clusters": [
    {
      "label": "Cluster name",
      "mention_count": N,
      "unique_accounts": N,
      "unique_functions": N,
      "recentness_weight": 0.0-1.0,
      "source_diversity": 0.0-1.0,
      "signals": [
        {
          "text": "Quote or paraphrase",
          "customer": "Company",
          "date": "YYYY-MM-DD",
          "meeting_type": "...",
          "source_file": "..."
        }
      ]
    }
  ],
  "raw_signals": [all unclustered signals for reference]
}

Filtering and Scoping

Customer Filter

Input: include_customers (comma-separated list)

Behavior:

• If provided: Only process meetings where customer field matches list
• If not provided: Process all customers

Example:

include_customers: "PrettyBoy,CompoundStudio,Joy Organics"
→ Only synthesize signals from these three customers

Function Filter

Input: include_internal_functions (comma-separated list)

Behavior:

• If provided: Only process internal meetings from specified functions
• Functions: Product, CS, Sales, Marketing, Ops

Example:

include_internal_functions: "Product,CS"
→ Process internal Product and CS meetings, skip Sales/​Marketing/​Ops

Signal Thresholds

Input filters:

• min_mentions: Minimum total mentions to qualify (default: 1)
• min_sources: Minimum distinct sources (customers/​functions) to qualify (default: 1)

Application: After clustering, filter out clusters that don't meet thresholds:

Keep cluster if:
  cluster.mention_count >= min_mentions AND
  cluster.unique_accounts + cluster.unique_functions >= min_sources

Type Exclusions

Input: exclude_types (comma-separated list)

Behavior: Ignore signals matching certain categories.

Default exclusions:

exclude_types: "bugs,housekeeping"

Examples:

• "bugs": Bug reports (should go to issue tracker, not epics)
• "housekeeping": Internal cleanup tasks
• "research": Exploratory ideas (not ready for roadmap)

Integration with Workflows

Product Planning Integration

Invoked by:

• product-planning workflow

Inputs:

• Time window (days or last_run)
• Customer filter (optional)
• Function filter (optional)
• Thresholds (min_mentions, min_sources)

Outputs:

• Clustered signals ready for epic creation
• Evidence for epic "Why now" sections
• Customer quotes for epic proposals

CS Prep Integration

Invoked by:

• cs-prep workflow

Inputs:

• Specific customer name
• Time window (typically 30-90 days for QBR prep)

Outputs:

• Customer-specific signal history
• Pain points and friction areas
• Feature requests and timeline
• Quotes for QBR discussion

Strategy Session Integration

Invoked by:

• strategy-session workflow

Inputs:

• Time window (often broader, e.g., 90 days)
• Optional topic filter

Outputs:

• Cross-customer patterns
• Market trends from customer feedback
• Evidence for strategic decisions

Success Criteria

Meeting synthesis complete when:

• All meetings in time window processed
• Signals extracted and categorized
• Clustering applied (semantic grouping)
• Metrics computed (mentions, accounts, recency)
• Output structured for consuming workflows
• Verbatim quotes preserved for evidence

Common Mistakes

Related Skills

• meeting-schema-validation: Validates meetings before synthesis
• priority-scoring: Uses synthesis output for epic prioritization
• product-planning: Primary consumer of synthesis output

Anti-Rationalization Blocks

Common excuses that are explicitly rejected: