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session-analysis

Extract behavioral signals and patterns from conversation transcripts.

6|1|Updated Jan 22, 2026
Search Tags:#conversation analysis#pattern detection#sentiment analysis#user behavior#signal extraction#feedback analysis
One-click install
npx skills add https://github.com/outfitter-dev/outfitter --skill session-analysis-outfitter-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: session-analysis
Source: https://github.com/outfitter-dev/outfitter/tree/main/plugins/fieldguides/skills/session-analysis
Command: npx skills add https://github.com/outfitter-dev/outfitter --skill session-analysis-outfitter-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill analyzes conversation transcripts to identify patterns, user sentiment, and key behavioral signals, providing actionable insights into user interactions.

Core Features & Use Cases

  • Signal Extraction: Identifies specific indicators of success, frustration, workflow, and requests within messages.
  • Pattern Detection: Groups signals to uncover recurring themes like repetition, evolution of user needs, or common tool chains.
  • Use Case: A product manager can use this Skill to analyze user feedback from support chats to understand common points of frustration or identify features users are successfully adopting.

Quick Start

Analyze the conversation history from the last 7 days to identify any frustration signals.

Frequently Asked Questions about session-analysis

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

FAQPage Schema
How do I analyze chat transcripts to detect user frustration signals?▼

Conversation analysis detects user frustration signals by scanning chat transcripts for specific behavioral indicators, grouping them into recurring patterns, and synthesizing the findings into actionable product insights.

What is the best way to extract user behavior patterns from support conversations?▼

Extracting user behavior patterns from support conversations involves identifying recurring workflow signals, requests, and sentiment shifts, then applying temporal analysis to map the evolution of user needs over time.

Can I use conversation analysis to identify which features users are successfully adopting?▼

Conversation analysis identifies successful feature adoption by isolating success signals within user messages, detecting common tool chains, and grouping these interactions to uncover workflows that users effectively leverage.

How does temporal analysis improve sentiment analysis on conversation transcripts?▼

Temporal analysis enhances sentiment analysis by tracking the evolution of user frustration and success signals across the timeline of a conversation, providing a nuanced understanding of shifting conversational dynamics.

Does conversation pattern detection provide confidence scoring for behavioral insights?▼

Conversation pattern detection provides confidence scoring to validate extracted behavioral insights, ensuring that synthesized recommendations regarding user and agent interactions are statistically reliable.

What are the limitations of analyzing user feedback without defining a detailed scope?▼

Analyzing user feedback without a detailed scope limits the ability to accurately extract targeted signals, detect meaningful behavioral patterns, and generate specific actionable recommendations from conversational dynamics.

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