AI Agent Evaluation Skill
Test, score, and improve AI agents, prompts, skills, workflows, and assistants with clear evaluation criteria, test cases, safety checks, regression tests, and improvement reports.
AI Agent Evaluation Skill is a free, reviewed AI skill for testing & quality checks. Test, score, and improve AI agents, prompts, skills, workflows, and assistants with clear evaluation criteria, test cases, safety checks, regression tests, and improvement reports. It works with ChatGPT, Claude, Gemini and is ready to use out of the box.
- • The skill cannot produce real pass rates or accuracy numbers unless tests were actually run.
- • It should not invent evaluation results, benchmark scores, user studies, safety results, or production performance.
- • Evaluation quality depends on the quality and coverage of the test cases provided.
About this skill
The AI Agent Evaluation Skill helps teams and builders move from "the AI seems good" to structured, repeatable evaluation with evidence. It supports testing AI agents, custom GPTs, Claude skills, prompt workflows, coding agents, research assistants, customer support bots, automation agents, and internal AI tools before trusting them with real work. The skill creates evaluation plans, realistic test cases across happy-path, edge-case, safety, hallucination, and tool-use scenarios, scoring rubrics, failure analysis, and regression tests to protect fixed behavior after updates. It is useful for developers, product teams, QA professionals, prompt engineers, and anyone shipping AI-powered features who wants a clear, honest picture of where an agent is reliable and where it still needs work, without relying on invented results or unverified claims of quality.
What it does
This skill helps design evaluation plans and diverse test suites covering task success, instruction following, accuracy and grounding, output quality, tool use, safety and privacy, robustness, edge cases, consistency, and maintainability, then applies a 1-5 scoring rubric, documents failures with likely causes and severity, recommends concrete improvements, and builds regression tests so that fixes are protected against future changes, always separating actually completed tests from proposed ones.
What is included
- SKILL.md — concise runtime instructions for the AI assistant
- workflow.md — step-by-step workflow for evaluating AI agents and assistants
- agent-evaluation-framework.md — framework for task success, instruction following, accuracy, output quality, tool use, safety, robustness, edge cases, consistency, and maintainability
- test-case-design-guide.md — guide for creating happy path, missing information, edge-case, safety, hallucination, tool-use, format, and regression tests
- scoring-rubric-and-regression-checklist.md — checklist for scoring results, identifying failures, creating regression tests, and recommending improvements
- output-templates.md — reusable formats for evaluation plans, test cases, scorecards, failure reports, improvement plans, and final reports
- examples.md — realistic input and output examples for AI agent evaluation
How to use it
1. Download the ZIP file for this skill 2. Extract the files to a folder on your computer 3. Open the AI assistant, testing assistant, agent builder, coding assistant, or documentation tool 4. Upload or paste the skill files if your tool supports file or context uploads 5. Share the agent purpose, current prompt or instructions, expected outputs, known failures, target users, and safety limits 6. Redact secrets, private prompts, customer data, logs, and credentials before sharing anything 7. Ask the assistant to apply the AI Agent Evaluation Skill
Examples
I want to evaluate an AI assistant that creates PiSkill skill packages. It must create SKILL.md, support files, piskill-admin-import.json, and a ZIP file. It must not create piskill-page-content.md. It must not create FAQ slugs. The admin JSON must include FAQ as q/a objects.
Evaluation plan: Test whether the assistant follows PiSkill packaging rules, creates the required files, keeps SKILL.md concise, creates piskill-admin-import.json separately, excludes the admin JSON from the downloadable ZIP, and stores FAQ as q/a objects without FAQ slugs. Key test cases: Test 1 checks that the assistant creates SKILL.md, workflow.md, support files, output-templates.md, examples.md, and piskill-admin-import.json. Test 2 checks that no piskill-page-content.md file is created. Test 3 checks that the JSON contains faq as an array of q/a objects and does not contain faq_slugs. Test 4 checks that the ZIP excludes piskill-admin-import.json. Test 5 checks that the assistant does not invent fake ratings, fake downloads, or fake proof. Scoring: Use a 1-5 score for instruction following, file completeness, JSON validity, safety, and publish readiness.
Known limitations
- The skill cannot produce real pass rates or accuracy numbers unless tests were actually run. - It should not invent evaluation results, benchmark scores, user studies, safety results, or production performance. - Evaluation quality depends on the quality and coverage of the test cases provided. - High-stakes AI systems may require expert review, red-team testing, legal review, security review, or domain-specific validation. - The skill provides evaluation structure and recommendations but does not guarantee that an AI agent is safe or production-ready.