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conversation-analysis

Analyze AI chat conversations to identify recurring patterns and recommend artifacts.

8|Updated Feb 4, 2026
Search Tags:#self-improvement#metadata#conversations#analyze#cursor#pattern-detection#artifact-generation
One-click install
npx skills add https://github.com/hutchic/.cursor --skill conversation-analysis-hutchic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: conversation-analysis
Source: https://github.com/hutchic/.cursor/tree/main/.cursor/skills/meta/conversation-analysis
Command: npx skills add https://github.com/hutchic/.cursor --skill conversation-analysis-hutchic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing AI chat conversations to extract patterns, identify recurring instructions, and surface artifacts for self-improvement and artifact creation.

Core Features & Use Cases

  • Identify and categorize recurring patterns across conversations (rules, skills, commands, subagents).
  • Provide structured insights to guide artifact generation and cross-reference updates.
  • Support self-improvement by turning conversation learnings into practical Cursor artifacts (rules, skills, commands, subagents, or hooks).

Quick Start

Analyze a sample conversation transcript and return patterns and recommended artifact ideas.

Frequently Asked Questions about conversation-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify recurring patterns in AI chat conversations?▼

Conversation pattern detection works by scanning chat transcripts to extract recurring instructions and workflows, categorizing findings by artifact type such as rules or subagents, and generating structured summaries to guide future refinements.

What is the best way to extract actionable artifacts from chat history?▼

To generate artifacts from chat history, analyze transcripts to identify recurring instructions and workflows, categorize findings by artifact type such as rules, commands, or subagents, and output structured artifact recommendations for maintenance.

How do I turn conversation learnings into Cursor rules and skills?▼

You can turn conversation learnings into Cursor artifacts by analyzing chat transcripts to detect recurring patterns, categorizing the extracted findings into rules, skills, commands, subagents, or hooks, and generating structured artifact recommendations.

Can I analyze multiple AI chats to generate cross-reference maintenance insights?▼

Yes, you can analyze multiple AI chats to generate cross-reference maintenance insights by applying pattern detection across conversations, categorizing recurring workflows by artifact type, and surfacing actionable insights to guide future refinements.

Does conversation analysis support categorizing findings by specific artifact types?▼

Yes, conversation analysis supports categorizing findings by specific artifact types including rules, skills, commands, subagents, and hooks, providing structured insights to guide artifact generation and cross-reference updates.

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