convo-analysis

name	convo-analysis
argument-hint	focus-area
description	Analyze conversation flows for behavioral patterns. Use this skill when debugging AI compliance issues, reviewing human request clarity, or identifying root causes of human-AI miscommunication. Produces sanitized reports safe to share.
disable-model-invocation	true

Conversation Analysis

Analyze human-AI conversation flows to identify behavioral patterns, compliance gaps, and improvement opportunities.

Role

You are a conversation analyst. Your job is to EXTRACT and ANALYZE the conversation flow - not to judge, implement, or fix anything.

Context Assessment

Before starting, analyze conversation history:

• Empty conversation? → Report "Nothing to analyze"
• Start analysis from first message to this command trigger

Core Principles

1. Balanced analysis - Evaluate BOTH human and AI behavior equally; neither party is presumed at fault
2. Chronological preservation - Show conversation as it happened, turn by turn
3. Behavioral focus - What happened, not blame assignment
4. Contribution assessment - Quantify each party's contribution to any miscommunication
5. Aggressive sanitization - Replace all specifics with placeholders
6. Rule mapping - Check against orchestrator directives, _apply-all rules, command workflows, Holy Trinity

Analysis Process

1. Extract

Gather all messages from session start to this command:

• User messages (requests, clarifications, approvals)
• AI responses (reasoning, actions taken)
• Tools used
• Skills activated
• Agents spawned
• Commands invoked

2. Analyze

1. Classify user messages - Identify type: direct-request, meta-request, mixed-content, clarification, feedback
2. Scope determination - Is this analyzing THIS session or a REFERENCED session?
3. Behavior mapping - Check both parties against expected patterns
4. Contribution scoring - Assign percentages to understand root cause
5. Improvement targeting - Identify specific fixes for user, AI, and system

3. Output

• Write report to docs/session-reports/{YYYYMMDDHHMMSS}-<short-title>.md
• Display brief summary to user

Sanitization Rules

Replace with placeholders:

• File paths → [FILE_1], [FILE_2]
• Feature names → [FEATURE_A], [FEATURE_B]
• API endpoints → [ENDPOINT_X]
• Variable/function names → [CODE_REF]
• Business terms → [DOMAIN_TERM]
• Code blocks → [CODE_BLOCK]

Keep as-is:

• Tool names (Read, Grep, Task, etc.)
• Skill names (/cook, etc.)
• Agent names (the-mechanic, etc.)
• Generic actions (search, read, write, edit)

Analysis Checklist

See references/rules-checklist.md

Output Format

See templates/report-template.md

Guardrails

Holy Trinity:

• YAGNI: Only analyze - don't suggest fixes inline
• KISS: Simple extraction, delegate complexity to skill
• DRY: Reuse existing references and templates

Communication:

• Report what happened, not who's "wrong"
• Neutral behavioral observations
• No blame assignment

Constraints:

• NO code snippets in output
• NO business logic exposure
• NO file paths or domain-specific terms
• Report must be shareable without editing
• Aggressive sanitization: replace specifics with placeholders

Common Pitfalls

Pitfall	How to Avoid
Focusing only on AI rule violations	Always analyze user message clarity first
Analyzing quoted/pasted content as primary subject	Identify meta-requests and scope correctly
Assigning 100% blame to one party	Use contribution percentages based on evidence
Missing buried requests in mixed content	Parse each message for multiple intents
Skipping rule loading	MUST read all rules BEFORE analysis - see rules-checklist.md
Success bias (completed = good)	Check HOW it completed, not just that it completed
Surface-level analysis	Check principles (delegation, YAGNI), not just workflow steps

Focus Area (Optional)

$ARGUMENTS

If provided, focus analysis on specific aspect (e.g., "rule compliance", "request clarity", "workflow gates").