Claude Skill MartBrowse skillsQuick linesLearn by videoTerminal guideWhat is a Skill?
 
← Back to list

Agent Evaluation Design

A skill for designing reproducible AI agent evaluations with representative datasets, rubrics, graders, regression gates, and a release decision memo.

Dev & CodingAdvanced★ 154⑂ 32AI score 9/10Last updated: Aug 9, 2026

What it does

Turns "the agent feels better" into evidence a release owner can act on.

  • Defines the unit under test and separates model quality from tool, retrieval, policy, and infrastructure failures
  • Builds representative cases plus boundary, long-tail, malformed-input, tool-failure, and adversarial cases with an isolated holdout set
  • Picks the least subjective reliable grader: deterministic checks, rubric-bound model graders, or blinded human review
  • Freezes prompts, model versions, seeds, retries, and timeouts so baseline and candidate run under identical conditions
  • Produces a failure taxonomy, uncertainty estimates, a release gate (critical-case minimum + non-regression + operational limits), and a decision memo
  • Uses scripts/aggregate_results.py to validate score bounds, missing labels, and pass-rate denominators

Who it's for

  • Engineers shipping LLM agents or copilots who need defensible go/no-go evidence
  • Teams comparing prompts, models, tools, memory, or orchestration patterns
  • QA and platform owners converting production incidents into regression fixtures

Examples

  • "Compare v1 and v2 of a support agent": define resolution correctness, citation fidelity, policy compliance, escalation judgment, latency, cost; blind the version labels; run three times; return ship / hold / limited rollout
  • "Did this prompt change regress anything?": compute baseline deltas on the holdout set and slice results by task, language, and tool
  • "Turn 20 production failures into fixtures": strip private data and wire the cases into the release gate

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/seb1n/awesome-ai-agent-skills/HEAD/agent-engineering/agent-evaluation/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the agent-engineering/agent-evaluation folder from the GitHub repo seb1n/awesome-ai-agent-skills into my ~/.claude/skills/agent-evaluation/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/seb1n/awesome-ai-agent-skills.git && mkdir -p ~/.claude/skills && cp -r awesome-ai-agent-skills/agent-engineering/agent-evaluation ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repo: git clone https://github.com/seb1n/awesome-ai-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy this skill: cp -r awesome-ai-agent-skills/agent-engineering/agent-evaluation ~/.claude/skills/
  5. Confirm the bundled references/ and scripts/ folders came along: ls ~/.claude/skills/agent-evaluation
  6. If you plan to run the aggregation script, check Python: python3 --version
  7. Restart Claude Code and ask something like "design an evaluation plan for my agent" to trigger the skill.
View source on GitHub ↗License: MIT