conversation-analysis

name	conversation-analysis
description	Analyze AI chat conversations to extract patterns, identify repeated instructions, and categorize potential artifacts. Use when processing past conversations for self-improvement or when user provides conversation content for analysis.

Conversation Analysis Skill

Analyze AI chat conversations to extract patterns, identify repeated instructions, and categorize potential artifacts for self-improvement.

When to Use

• Use this skill when analyzing past AI chat conversations
• Use when user provides conversation content for analysis
• Use when identifying patterns for artifact creation
• Use when extracting reusable knowledge from conversations

Instructions

Analysis Process

1. Read the conversation: Understand the full context of the conversation
2. Identify patterns: Look for recurring instructions, corrections, or workflows
3. Categorize findings: Group patterns by type (rule, skill, command, subagent)
4. Extract key information: Identify specific patterns and their contexts
5. Suggest artifacts: Recommend artifacts to create based on patterns

Pattern Identification

Look for:

• Repeated instructions: Instructions that appear multiple times
• Correction patterns: When user corrects AI behavior
• Workflow patterns: Sequences of actions that recur
• Context patterns: Contexts where certain patterns apply

Categorization

For each pattern identified:

• Determine artifact type: Rule, Skill, Command, Subagent, or Cursor hooks (when behavior should run in the agent loop)
• Assess frequency: How often the pattern appears
• Evaluate significance: Is it worth creating an artifact (or hook config)?
• Suggest organization: Where should the artifact be placed? For hooks: .cursor/hooks.json and .cursor/hooks/ scripts (project) or user config

Consider Cursor hooks when the pattern involves:

• Gating or allowing specific agent actions (e.g. blocking shell commands, file reads, or MCP calls)
• Running something automatically after every file edit (e.g. format, lint)
• Auditing or logging agent behavior (shell, MCP, tool use)
• Injecting context or env at session start
• Different policy for Tab vs Agent (e.g. redact secrets for Tab only)

When hooks might apply, note the relevant hook event(s) (e.g. beforeShellExecution, afterFileEdit) and suggest command-based vs prompt-based. See Cursor Hooks for when to use hooks and which events exist.

Output Format

For each pattern, provide:

• Pattern description: What the pattern is
• Frequency: How often it appears
• Artifact type: Recommended type (rule/skill/command/subagent) or hooks (with suggested hook event(s))
• Suggested name: Following naming conventions (for hooks: script name or hook event name)
• Suggested location: Category and path (for hooks: .cursor/hooks.json and e.g. .cursor/hooks/script.sh)
• Content outline: Key points to include (for hooks: which event, command vs prompt, matcher if applicable)

Examples

Example 1: Repeated Instruction Pattern

Conversation excerpt: User repeatedly asks to "always use TypeScript strict mode"

Analysis:

• Pattern: TypeScript strict mode preference
• Frequency: Appears 5+ times
• Artifact type: Rule
• Suggested name: typescript-strict-mode
• Suggested location: cursor/rules/organization/typescript-strict-mode.mdc
• Content: Rule enforcing TypeScript strict mode usage

Example 2: Workflow Pattern

Conversation excerpt: User repeatedly follows same steps for code review

Analysis:

• Pattern: Code review workflow
• Frequency: Appears 3+ times
• Artifact type: Command
• Suggested name: code-review-checklist
• Suggested location: cursor/commands/meta/code-review-checklist.md
• Content: Step-by-step code review checklist

Best Practices

• Focus on high-frequency patterns
• Validate pattern significance
• Consider context and conditions
• Document pattern rationale
• Suggest appropriate artifact type

Related Artifacts

• Pattern Extraction Skill
• Artifact Creation Skill
• Process Chat Command
• Conversation Analyzer Subagent
• Cursor Hooks – When to recommend hooks when processing transcripts
• AI Agent Patterns