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session-analysis

outfitter-dev/session-analysis

Data & Analytics

About

SKILL.md

session-analysis

outfitter-dev/session-analysis

Data & Analytics

About

This skill should be used when analyzing conversation patterns, identifying frustration or success signals, or when "analyze conversation", "what went wrong", or "patterns" are mentioned.

SKILL.md

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", stage 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

Load the maintain-tasks skill for stage tracking. Stages advance only, never regress.

Stage	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"

Task 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

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
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
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
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 stage 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 stage transitions
Extract low-confidence signals without marking them
Claim patterns from single occurrences
Regress stages
Deliver without marking final stage complete
Over-interpret neutral language

signal-patterns.md — extended taxonomy, edge cases
extraction-techniques.md — regex, heuristics
sample-analysis.md — complete walkthrough

About

SKILL.md

About

This skill should be used when analyzing conversation patterns, identifying frustration or success signals, or when "analyze conversation", "what went wrong", or "patterns" are mentioned.

SKILL.md

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", stage 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

Load the maintain-tasks skill for stage tracking. Stages advance only, never regress.

Stage	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"

Task 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

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
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
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
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 stage 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 stage transitions
Extract low-confidence signals without marking them
Claim patterns from single occurrences
Regress stages
Deliver without marking final stage complete
Over-interpret neutral language

signal-patterns.md — extended taxonomy, edge cases
extraction-techniques.md — regex, heuristics
sample-analysis.md — complete walkthrough