conversation-analysis

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Analyze conversation history to identify patterns, signals, and behaviors. Use when analyzing conversations, finding patterns in chat, identifying what went well/wrong, scanning for frustration, success, workflow transitions, or user preferences. Triggers on analyze, pattern(s), signal(s), conversation analysis, or `--analyze-conversation`.

⭐4stars🔀0forks📁View on GitHub🕐Updated Dec 31, 2025
[Voice of Customer Insights]

When & Why to Use This Skill

This Claude skill provides a sophisticated framework for analyzing conversation histories to detect behavioral patterns, sentiment signals, and workflow transitions. It transforms raw chat logs into structured insights by identifying success markers, user frustration, and explicit preferences, enabling data-driven improvements to agent performance and user experience through automated signal extraction and pattern recognition.

Use Cases

  • User Friction Analysis: Automatically scan interaction logs to pinpoint specific triggers for user frustration, such as repetitive requests or frequent corrections.
  • Preference & Requirement Extraction: Identify and document explicit user constraints and preferences (e.g., 'Always use X', 'Never do Y') mentioned throughout a conversation history.
  • Success Rate Auditing: Evaluate the effectiveness of AI interactions by tracking 'Success' signals like explicit praise, adoption of suggestions, and seamless workflow continuations.
  • Workflow Optimization: Analyze recurring tool-chain sequences and transition patterns to identify bottlenecks or opportunities for process automation.
  • Behavioral Trend Tracking: Monitor how user requirements and satisfaction levels evolve over the course of a long-term project or multi-session interaction.
nameconversation-analysis
version2.0.0
descriptionThis skill should be used when analyzing conversation patterns, identifying frustration or success signals, or when "analyze conversation", "what went wrong", or "patterns" are mentioned.

Conversation Analysis

Signal extraction → pattern detection → behavioral insights.

  • User requests conversation analysis
  • Identifying frustration, success, or workflow patterns
  • Extracting user preferences and requirements
  • Understanding task evolution and iterations

NOT for: real-time monitoring, content generation, single message analysis
Type Subtype Indicators
Success Explicit Praise "Perfect!", "Exactly what I needed", exclamation marks
Success Continuation "Now do the same for...", building on prior work
Success Adoption User implements suggestion without modification
Success Acceptance "Looks good", "Ship it", "Merge this"
Frustration Correction "No, I meant...", "That's wrong", "Do X instead"
Frustration Reversion User undoes agent changes, "Go back"
Frustration Repetition Same request 2+ times, escalating specificity
Frustration Explicit "This isn't working", "Why did you...", accusatory tone
Workflow Sequence "First...", "Then...", "Finally...", numbered lists
Workflow Transition "Now that X is done, let's Y", phase changes
Workflow Tool Chain Recurring tool usage patterns (Read → Edit → Bash)
Workflow Context Switch Abrupt topic changes, no transition language
Request Prohibition "Don't use X", "Never do Y", "Avoid Z"
Request Requirement "Always check...", "Make sure to...", "You must..."
Request Preference "I prefer...", "It's better to...", comparative language
Request Conditional "If X then Y", "When A, do B", situational rules

Confidence levels:

  • High (0.8–1.0): Explicit keywords match taxonomy, no ambiguity, strong context
  • Medium (0.5–0.79): Implicit signal, partial context, minor ambiguity
  • Low (0.2–0.49): Ambiguous language, weak context, borderline classification

Track with TodoWrite. Phases advance only, never regress.

Phase Trigger activeForm
Parse Input Session start "Parsing input"
Extract Signals Scope validated "Extracting signals"
Detect Patterns Signals extracted "Detecting patterns"
Synthesize Report Patterns detected "Synthesizing report"

TodoWrite format:

- Parse Input { scope description }
- Extract Signals { from N messages }
- Detect Patterns { category focus }
- Synthesize Report { output format }

Edge cases:

  • Small scope (<5 messages): Skip Extract Signals, jump to Synthesize
  • Re-analysis: Resume at Detect Patterns
  • Narrow focus (single signal type): Skip Detect Patterns

Workflow:

  • Start: Create Parse Input in_progress
  • Transition: Mark current completed, add next in_progress
  • After delivery: Mark Synthesize Report completed

  1. Define Scope

    • Message range (all, recent N, date range)
    • Actors (user only, agent only, both)
    • Exclusions (system messages, tool outputs, code blocks)
    • Mark Parse Input completed, create Extract Signals in_progress

  2. Extract Signals

    • Scan messages for signal keywords
    • Match against taxonomy
    • Assign confidence (high/medium/low)
    • Record: type, subtype, message_id, timestamp, quote, context
    • Mark Extract Signals completed, create Detect Patterns in_progress

  3. Detect Patterns

    • Group signals by type/subtype
    • Find clusters (3+ related signals)
    • Identify evolution (signal changes over time)
    • Track repetition (recurring themes)
    • Spot correlations (tool chains, workflows)
    • Mark Detect Patterns completed, create Synthesize Report in_progress

  4. Output

    • Generate JSON with signals, patterns, summary
    • Include confidence, recommendations, action items
    • Append △ Caveats if gaps exist
    • Mark Synthesize Report completed

Behavioral patterns from signal clusters:

Pattern Detection Confidence
Repetition Same signal 3+ times Strong: 5+ signals
Evolution Signal type changes over time Moderate: 3-4 signals
Preferences Consistent request signals Strong: across sessions
Tool Chains Recurring tool sequences (5+ times) High: frequent use
Problem Areas Clustered frustration signals Strong: 3+ in same topic

Temporal patterns:

  • Escalation: Increasing frustration/stronger requirements
  • De-escalation: Frustration → success transition
  • Cyclical: Same issue recurs across sessions

JSON structure:

{
  "analysis": {
    "scope": {
      "message_count": N,
      "date_range": "YYYY-MM-DD to YYYY-MM-DD",
      "actors": ["user", "agent"]
    },
    "signals": [
      {
        "type": "success|frustration|workflow|request",
        "subtype": "specific_subtype",
        "message_id": "msg_123",
        "timestamp": "ISO8601",
        "quote": "exact text",
        "confidence": "high|medium|low",
        "context": "brief explanation"
      }
    ],
    "patterns": [
      {
        "pattern_type": "repetition|evolution|preference|tool_chain",
        "category": "success|frustration|workflow|request",
        "description": "pattern summary",
        "occurrences": N,
        "confidence": "strong|moderate|weak",
        "first_seen": "ISO8601",
        "last_seen": "ISO8601",
        "recommendation": "actionable next step"
      }
    ],
    "summary": {
      "total_signals": N,
      "by_type": { "success": N, "frustration": N, ... },
      "key_insights": ["insight 1", "insight 2"],
      "action_items": ["item 1", "item 2"]
    }
  }
}

ALWAYS:

  • Create Parse Input at session start
  • Update todos at phase transitions
  • Include confidence levels for all signals
  • Support patterns with 2+ signals minimum
  • Mark Synthesize Report completed after delivery
  • Apply recency weighting (recent overrides old)

NEVER:

  • Skip phase transitions
  • Extract low-confidence signals without marking them
  • Claim patterns from single occurrences
  • Regress phases
  • Deliver without marking final phase complete
  • Over-interpret neutral language