agents

Compare two skill outputs blindly and grade expectation evidence.

1|Updated May 17, 2026
One-click install
npx skills add https://github.com/mykcs/myk-skills --skill agents-mykcs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agents
Source: https://github.com/mykcs/myk-skills/tree/main/agents
Command: npx skills add https://github.com/mykcs/myk-skills --skill agents-mykcs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you evaluate competing skill outputs objectively by providing purpose-built agents that analyze results after comparisons, compare outputs without bias, and grade executions against expectations.

Core Features & Use Cases

  • Analyzer (Post-hoc): Explains why a winner beat a loser by unblinding comparison inputs, inspecting the relevant skills, and reading execution transcripts to produce actionable improvement guidance.
  • Grader: Grades expectations as PASS/FAIL using evidence from transcripts and output files, including checking for superficial or unverifiable claims.
  • Comparator (Blind): Compares two outputs without knowing which skill produced them by generating a rubric, scoring structure and content, and selecting a winner (or tie) based on rubric outcomes.

Quick Start

Use the comparator agent to judge which of two outputs is better for a specific eval task, then run the analyzer to generate improvement suggestions for the losing skill.

Frequently Asked Questions about agents

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

FAQPage Schema
How do I blindly compare two AI skill outputs to pick the best one?▼

Blind comparison evaluates two outputs without knowing their source by generating a scoring rubric, grading structure and content, and selecting a winner based on the rubric outcomes.

What is rubric scoring for post-hoc analysis of skill execution transcripts?▼

Rubric scoring for post-hoc analysis explains why a winner beat a loser by unblinding comparison inputs, inspecting skills, and reading execution transcripts to produce actionable improvement guidance.

How do I grade expectation evidence as PASS or FAIL using output files and transcripts?▼

Grading expectation evidence as PASS or FAIL involves checking execution transcripts and output files for cited evidence, including detecting superficial or unverifiable claims.

Can I output benchmarking evaluation results as JSON for downstream consumption?▼

Benchmarking evaluation results require producing JSON outputs for downstream consumption, including winner selection logic and evidence-cited PASS or FAIL grading with optional expectation checks.

What is the best way to benchmark multi-run skill outputs without bias?▼

Blind benchmarking of multi-run skill outputs prevents bias by generating a rubric, scoring both outputs anonymously, and selecting a winner or declaring a tie based strictly on rubric outcomes.

Are execution transcripts and output files required for evidence-based grading?▼

Execution transcripts and output files are required for evidence-based grading, as the grader checks these sources to verify claims and cite evidence for PASS or FAIL expectation outcomes.