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Agent Evaluation Methods

Build tests, rubrics, and quality gates to measure whether an AI agent works well.

Source Download skill
Repository
muratcankoylan/Agent-Skills-for-Context-Engineering
Stars
18,282
Licence
MIT
Files
3 files

What it does

Once installed, the agent approaches testing an AI agent differently: it designs checks that focus on whether the final outcome is correct rather than the exact steps taken, scores results across several dimensions such as accuracy, completeness, citations and tool efficiency, and runs cheap deterministic checks before any subjective model-based judging. It also helps set up regression suites, baseline comparisons and ongoing production monitoring. A bundled Python file provides starter classes for rubrics, test sets, a runner and a production monitor.

Great for

  • Developers building agent pipelines who need to catch regressions before deploying changes
  • Teams comparing two agent configurations or prompts and want a consistent scoring method
  • Engineers setting up quality gates or monitoring for agents running in production

Skip it if

  • Designing or calibrating an LLM judge itself; the skill points to a separate advanced-evaluation skill for that work
  • Simple one-off checks of a single answer, where a full evaluation framework adds unnecessary overhead
  • Autonomous agents that change files or systems without human review, since evaluation alone does not provide rollback or approval controls, which belong to a separate harness-engineering skill

Try asking

  • “Create a regression test suite for my support agent and run it before each release”
  • “Design a multi-dimensional rubric for scoring research summaries on accuracy, completeness and citations”
  • “Set up a production monitor that tracks my agent's quality scores over time”
  • “Compare the baseline configuration of my agent against a version with a larger token budget”
Step-by-step guide: how to use Agent Evaluation MethodsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    evaluation.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip evaluation.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip evaluation.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Start with deterministic checks such as schema validity and required fields before adding LLM-based scoring, since they are cheaper and more reliable.
  • Use test cases at several complexity levels so you can see where the agent starts to fail.
  • Store per-dimension scores alongside the overall score so you can see which dimension needs work.

What's inside

  • references/metrics.md
  • scripts/evaluator.py
  • SKILL.md

Redistributed unmodified from muratcankoylan/Agent-Skills-for-Context-Engineering under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.