Guide · Agent skill
How to use the AI Agent Benchmark Register agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The ai-agent-evaluation-benchmarking skill is a Claude agent skill that helps you set up a standard record format for tracking how well an AI agent performs on tasks. It is aimed at engineering, data and QA teams who want a consistent way to compare agent versions over time. After following this guide you will know how to install the skill, ask for the artifacts it produces, and use its guidance on fair measurement. The skill generates templates and guidance only; it does not run benchmarks.
AI Agent Benchmark Registersickn33/agentic-awesome-skills · MIT Download the skill Quick start
- 1Download the skill. Get the ZIP of ai-agent-evaluation-benchmarking from this directory page.
- 2Unzip into the skills folder. Extract the folder into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder.
- 3Or upload it in the apps. In the Claude apps, go to Settings → Capabilities → Skills and upload the skill.
- 4Ask for the artifact you need. Describe the runs you want to track and request a field list, SQL DDL, JSON Schema or Notion property list.
- 5Define your fields and rules. Answer the agent's questions about your denominators, baseline, and verdict options before using the schema.
- 6Check the output against your evidence. Confirm that results come from your own authorized harness, since the skill itself does not run benchmarks.
What does this skill make the agent do?
The skill covers the record-keeping side of agent evaluation. It tracks pass rates, tool-call errors, token use and cost per solved task. When you ask, the agent can produce a field list, SQL table definitions, a JSON Schema and a Notion-style property list for the same fields.
It also explains how to measure fairly. The guidance includes using a declared denominator for pass rate, defining an invalid-call denominator for tool hallucination, comparing candidates against a baseline under matched tasks, and keeping an 'inconclusive' verdict instead of forcing a yes or no.
- Field reference with SQL, JSON Schema and Notion property types for each field
- Select options for benchmark suite domain and regression verdict
- Measurement guidance: matched tasks, recorded failures, cost accounting
- Recording counts and uncertainty, not only a percentage
How do you install the skill?
Download the ZIP of the skill from this directory page. The skill is a folder that contains a single file, SKILL.md, so unzip the download and place the folder in one of two locations. Use ~/.claude/skills/ to make it available across your projects, or use a project's .claude/skills/ folder to limit it to that project.
If you use the Claude apps instead, you can upload the skill under Settings → Capabilities → Skills.
- Personal install: unzip into ~/.claude/skills/
- Project install: unzip into the project's .claude/skills/ folder
- Claude apps: upload under Settings → Capabilities → Skills
How do you trigger the skill with a natural request?
You do not need a special command. Describe what you are trying to record or compare, and the agent will use the skill when the request matches its purpose. Be specific about the output you want, such as a SQL table, a JSON Schema or a field list, so the agent knows which artifact to produce.
The skill's own example prompt asks how to configure and track an evaluation for a production environment. You can adapt that wording to your own system.
- Ask for a field list for logging agent evaluation runs
- Ask for SQL DDL for a table that stores pass rate and cost per solved task
- Ask for a JSON Schema to validate evaluation records in CI
- Ask how to measure an agent fairly against a baseline
What does a good request look like?
The skill asks you to define your parameters before generating artifacts, so give the agent context about what you are measuring. Mention your target system, whether you want a database table, a JSON Schema or a Notion database, and whether you are comparing two agent versions.
The reusable prompt below is taken from the skill. Replace the bracketed wording with your own context if needed.
I want to establish a verified AI Agent Capability Evaluation & Benchmarking register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.What should you check before relying on the output?
The skill is explicit about its limits. A register does not prove compliance, and the evidence for any result has to come from an external, authorized evaluation harness. The example values in the skill are synthetic and are not measured results, so do not treat them as benchmark data.
Define cost per solved task as total attributable run cost divided by solved tasks. If there are zero solves, the cost is unavailable under an explicit policy, not zero. Token estimates alone leave out provider and tool charges.
- Use the exact model, provider and deployment revision you actually ran
- Record completed, failed and skipped counts
- Keep example data synthetic and out of production secrets or private endpoints
- Re-run your validation tests when the schema or parameters change
What people use it for
- An engineering team building an internal log that compares AI agent versions over time
- A QA or data analyst who needs a consistent schema for storing agent evaluation results
- A team drafting a SQL table and JSON Schema for validating evaluation records in CI
- Someone who wants guidance on matched tasks, recorded failures and inconclusive verdicts before running a comparison
Frequently asked questions
Is the ai-agent-evaluation-benchmarking skill free?
The skill is published under the MIT licence, as listed on this page. Check the licence file in the source repository for the full terms.
What do I need to install this skill?
The listing states that no requirements are needed and that the skill runs no scripts and uses no network. You only need a Claude setup that supports agent skills, with the skill unzipped into ~/.claude/skills/ or a project's .claude/skills/ folder, or uploaded under Settings → Capabilities → Skills.
Does the skill actually run benchmarks on my agent?
No. The skill only generates templates and guidance. It does not execute benchmarks or measure a real agent, and it does not prove compliance. You need an external, authorized harness to produce the evidence.
Does the skill include a ready-made benchmark dataset?
No. The example values are synthetic and are not measured results, so you will need to supply your own task data and run results.
How is this different from other evaluation tools or frameworks?
This skill focuses on the record format and measurement guidance: fields, SQL DDL, JSON Schema, and fair-comparison rules. It is not an evaluation runner, so you would pair it with a harness that actually executes your tasks. The skill's related skills include ai-agent-tool-routing, ai-prompt-regression-testing and ai-code-generation-guardrails.
Why does the skill say my result is inconclusive?
The skill recommends keeping an inconclusive verdict so a weak or ambiguous comparison is not reported as a clear improvement or regression. Use paired outcomes or a justified test and interval before choosing a stronger verdict.
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Our verdict
Use this skill if you need a consistent record format and fair-measurement guidance for comparing AI agent versions, and you already have a way to run tasks. Pick something else if you expect it to execute benchmarks, produce compliance certification, or supply a benchmark dataset.
Download the skill