AI Evaluation Benchmark Designer
Design practical evaluation benchmarks for AI assistants and agents using test sets, scoring rubrics, failure categories, and release thresholds.
AI Evaluation Benchmark Designer is a free AI skill for agent systems & llm workflows. Design practical evaluation benchmarks for AI assistants and agents using test sets, scoring rubrics, failure categories, and release thresholds. It works with Claude, ChatGPT, Cursor and is ready to use out of the box.
About this skill
AI Evaluation Benchmark Designer helps teams assess whether an AI feature performs reliably enough for its intended use. It defines representative tasks, test data, expected behavior, scoring criteria, failure taxonomies, safety and robustness checks, human-review procedures, and decision thresholds.
What it does
The skill clarifies the AI system's job and risk, identifies critical capabilities and failure modes, designs representative and adversarial test cases, creates scoring rubrics, defines reference answers where appropriate, separates deterministic checks from human judgment, and produces an evaluation protocol and reporting template.
What is included
- Evaluation objective
- Capability and risk map
- Test-set design
- Failure taxonomy
- Scoring rubrics
- Human-review protocol
- Release thresholds
- Evaluation report template
How to use it
1. Download the ai-evaluation-benchmark-designer-SKILL.md file 2. Upload it to your AI or development workspace 3. Describe the assistant, users, tools, and important failure risks 4. Provide sample tasks and known errors when available 5. Use the benchmark for development, regression testing, and release decisions
Examples
Design an evaluation benchmark for an AI assistant that analyzes PV plant data and explains possible inverter underperformance without claiming a confirmed fault when evidence is weak.
A benchmark with capability areas, representative and edge-case prompts, data scenarios, hallucination and overconfidence failure categories, scoring rubrics, confidence and citation checks, human-review guidance, release thresholds, and reporting template.
FAQ
What is this skill for?
Do I need reference answers?
Can it test hallucinations?
Can it evaluate tool use?
Does a high benchmark score guarantee production safety?
How is this different from ordinary software tests?
Related Skills
LLM Evaluation Benchmark and Regression Designer
Design LLM evaluation benchmarks with task sets, reference criteria, graders, safety cases, variability, baselines, regression gates, and reporting.
RAG Retrieval Quality Engineer
Design and evaluate RAG retrieval with chunking, metadata, hybrid search, reranking, citations, freshness, and failure analysis.
Agent Prompt & Tool Spec Designer
Designs a complete system prompt and tool specification for an LLM agent from a description of what the agent should do.
Related Prompts
AI Prompt Evaluation Suite Designer
Design a rigorous evaluation suite for testing a prompt or AI feature before shipping, covering edge cases, grading criteria, and regression tracking.
Agent Evaluation Benchmark Builder
Create a representative benchmark that tests an AI agent's task success, tool use, safety, recovery, and efficiency before release.
Agent Cost and Latency Optimizer
Reduce an agent's response time and operating cost while protecting task quality, safety controls, and critical reasoning steps.
Related Articles
AI Agent Evaluation Skill: What to Test Before Using an Agent at Work
Learn what to test before using an AI agent at work, including success criteria, failure cases, and human review points.
AI Governance Is Moving from Principles to Controls
AI governance is shifting from broad principles toward operational controls such as inventories, evaluations, permissions, incident response, evidence, and human approval.
GPT-5.6 Hype Shows Why Independent Model Testing Matters
New model launches are accelerating competition, but the useful response is task-based evaluation rather than hype-driven switching.