Can I use this for real-time monitoring?

No, this skill is specifically designed for post-hoc analysis of conversation history to identify trends and patterns rather than live monitoring.

What format is the final analysis provided in?

The skill produces a structured JSON report that includes scope metrics, a list of categorized signals, detected behavioral patterns, and a summary of key insights.

What signals does the Conversation Analysis skill detect?

It identifies four primary signal types: Success (praise, adoption), Frustration (corrections, repetition), Workflow (sequences, transitions), and Requests (preferences, requirements).

How does the skill determine the accuracy of its findings?

Every signal and pattern is assigned a confidence level (High, Medium, or Low) based on keyword expliciteness and contextual clarity within the chat history.

Conversation Analysis

Name: Conversation Analysis
Author: outfitter-dev

byoutfitter-dev

分析与监控

Analyzes conversation history to extract behavioral signals, identify recurring patterns, and synthesize actionable insights.

关于

The Conversation Analysis skill transforms chat histories into structured behavioral intelligence by scanning for specific signals such as user frustration, success markers, and workflow transitions. It uses a sophisticated signal taxonomy to categorize interactions and assign confidence levels, allowing Claude to detect patterns like tool-chain usage, preference evolution, and recurring bottlenecks. This skill is essential for developers and teams looking to audit AI interactions, refine requirements, and optimize automated workflows based on empirical conversational data.

主要功能

Pattern detection to identify recurring behaviors and session-over-session trends
Phase-based state management to track analysis progress systematically
Signal taxonomy mapping for success, frustration, and workflow indicators
Structured JSON reporting with confidence scores and actionable recommendations
0 GitHub stars
Temporal analysis to detect sentiment escalation or workflow de-escalation

使用场景

Identifying friction points in developer workflows by analyzing tool usage patterns
Auditing agent performance to identify where complex technical tasks succeed or fail
Extracting implicit user preferences and requirements from long-form chat histories

关于

主要功能

Pattern detection to identify recurring behaviors and session-over-session trends
Phase-based state management to track analysis progress systematically
Signal taxonomy mapping for success, frustration, and workflow indicators
Structured JSON reporting with confidence scores and actionable recommendations
0 GitHub stars
Temporal analysis to detect sentiment escalation or workflow de-escalation

使用场景

Identifying friction points in developer workflows by analyzing tool usage patterns
Auditing agent performance to identify where complex technical tasks succeed or fail
Extracting implicit user preferences and requirements from long-form chat histories