Help the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.
Quickly install error-analysis AI skill to your development environment via command line
Source: hamelsmu/evals-skills.
Guide the user through reading LLM pipeline traces and building a catalog of how the system fails.
Capture the full trace: input, all intermediate LLM calls, tool uses, retrieved documents, reasoning steps, and final output.
Target: 100 traces. This is roughly where new traces stop revealing new kinds of failures. The number depends on system complexity.
Help the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents. Source: hamelsmu/evals-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/hamelsmu/evals-skills --skill error-analysisHelp the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents. Source: hamelsmu/evals-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/hamelsmu/evals-skills --skill error-analysis Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/hamelsmu/evals-skills