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analyzing-mlflow-session

Reconstruct multi-turn chat sessions from MLflow traces and diagnose turn-level issues.

Updated Sep 9, 2017
Search Tags:#trace#assessment#analyze#mlflow#chat-session#root-span
One-click install
npx skills add https://github.com/mirakui/dotfiles --skill analyzing-mlflow-session-mirakui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analyzing-mlflow-session
Source: https://github.com/mirakui/dotfiles/tree/main/claude/skills/analyze-mlflow-chat-session
Command: npx skills add https://github.com/mirakui/dotfiles --skill analyzing-mlflow-session-mirakui

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, jq, and includes scripts (resource) components.

What problem does it solve?

Analyzes MLflow traces to diagnose and understand multi-turn chat sessions, helping identify where conversations go wrong and reveal patterns across turns.

Core Features & Use Cases

  • Reconstruct sessions by identifying the root span, discovering inputs and outputs, and aggregating per-turn data for review.
  • Analyze session-level and per-turn assessments to surface quality issues, bottlenecks, and misalignments across the conversation.
  • Provide safe, guided analysis practices that minimize unnecessary full-trace fetches while preserving context for debugging.

Quick Start

Run the scripts/discover_schema.sh with your Experiment ID and Session ID to print the root-span inputs and outputs.

Frequently Asked Questions about analyzing-mlflow-session

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

FAQPage Schema
How do I analyze MLflow traces to debug multi-turn chat sessions?▼

MLflow traces are analyzed by locating the first trace of a session, extracting root-span inputs and outputs, and aggregating turns chronologically to diagnose conversation failures. This approach avoids fetching full traces for every turn to ensure scalability.

How do I extract inputs and outputs from MLflow trace metadata without fetching full traces?▼

You extract inputs, outputs, and assessments directly from trace metadata by locating the root span and handling wrapper spans. This avoids fetching full traces for every turn, enabling scalable session analysis without overwhelming overhead.

Do I need jq to analyze MLflow chat session traces?▼

You need both mlflow and jq installed to run the discovery scripts. The scripts use jq to parse trace metadata and print the root-span inputs and outputs when provided with an Experiment ID and Session ID.

Why does my multi-turn chat session analysis fail to surface turn-level issues?▼

Turn-level issues are surfaced by aggregating per-turn data chronologically and analyzing session-level assessments. If analysis fails, ensure you are correctly identifying the root span and extracting metadata rather than relying on incomplete trace fragments.

What is the best way to diagnose where a conversation goes wrong in MLflow?▼

The best way to diagnose conversation failures is to reconstruct the session by aggregating per-turn inputs and outputs from the root span, then analyzing session-wide assessments to identify quality issues, bottlenecks, and misalignments across turns.

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