error-analysis

Name: error-analysis
Availability: InStock
Author: adamatdevops

Analyze LLM pipeline traces to prioritize failure modes by root cause.

1|Updated Jan 7, 2026

Search Tags:#debugging #llm evaluation #instrumentation #root-cause analysis #trace analysis #failure categorization #quality prioritization

Ready to run

npx skills add https://github.com/adamatdevops/forge-works --skill error-analysis-adamatdevops

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: error-analysis
Source: https://github.com/adamatdevops/forge-works/tree/main/.skills/hamelsmu/error-analysis
Command: npx skills add https://github.com/adamatdevops/forge-works --skill error-analysis-adamatdevops

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Error-analysis helps you transform messy LLM pipeline trace failures into an actionable, prioritized catalog of what breaks and why, so you can fix the highest-impact issues faster.

Core Features & Use Cases

Trace-driven failure discovery: Review representative LLM pipeline traces and determine pass/fail based on the actual first failure point.
Category building grounded in reality: Cluster observed failure notes into 5–10 actionable categories with clear one-sentence definitions.
Quantified prioritization: Label every trace against the categories and compute failure rates to decide what to fix first and when to add evaluators.

Quick Start

Ask the AI to help you review 30–50 representative LLM pipeline traces, then iteratively produce 5–10 failure categories and a failure-rate ranking to guide your next fixes.

Frequently Asked Questions about error-analysis

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

FAQPage Schema

How do I pinpoint why my LLM pipeline traces are failing?▼

To pinpoint why LLM pipeline traces fail, review 30–50 representative traces to identify the first failure point, then cluster failure notes into 5–10 categories and compute failure rates for prioritized debugging.

What is root-cause analysis for LLM evaluation and debugging?▼

Root-cause analysis for LLM debugging is the process of annotating pass/fail outcomes from pipeline traces, defining actionable failure categories, and labeling traces to compute failure rates that guide prompt or evaluator fixes.

How do I categorize and prioritize failures in AI systems?▼

Categorize and prioritize AI failures by collecting representative traces, clustering observed failure notes into 5–10 categories with clear definitions, and computing failure rates to decide what to fix first.

How many LLM pipeline traces do I need for effective trace analysis?▼

For effective trace analysis, reviewing 30–50 representative LLM pipeline traces is recommended to iteratively produce 5–10 failure categories and a failure-rate ranking that guides your next fixes.

When should I fix my prompt versus adding evaluator instrumentation?▼

Decide between prompt fixes and evaluator instrumentation by labeling traces against defined failure categories and computing failure rates; high-impact categories dictate whether to adjust prompts or add evaluators.

Can I use trace analysis on synthetic LLM pipeline data?▼

Yes, trace analysis applies to evaluation and debugging workflows using real or synthetic traces across input and feature dimensions to categorize failures and compute prioritized failure rates.