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AI Agent Benchmark Register

Designs a structured register for logging and comparing AI agent evaluation runs

Source Download skill
Repository
sickn33/agentic-awesome-skills
Stars
49,023
Licence
MIT
Files
1 file

What it does

When installed, the agent helps you set up a standard record format for tracking how well an AI agent performs on tasks: pass rates, tool-call errors, token use and cost per solved task. It can produce a field list, SQL table definitions, JSON Schema and a Notion-style property list, and it explains how to measure fairly, such as using a fixed denominator and keeping an 'inconclusive' verdict. It does not run any benchmarks itself.

Great for

  • Engineering teams building an internal log for comparing AI agent versions over time
  • Data or QA staff who need a consistent schema for agent evaluation results
  • People who want guidance on fair measurement, such as matched tasks and recorded failures

Skip it if

  • Anyone expecting the skill to actually run benchmarks or measure a real agent, since it only generates templates and guidance (the file says it does not execute benchmarks)
  • Teams wanting proof of compliance or certification, because the skill states it does not prove compliance and the evidence must come from an external, authorized harness
  • Users seeking a ready-made benchmark dataset; the example values are synthetic and not measured results

Try asking

  • “Create a SQL DDL and JSON Schema for tracking agent benchmark runs with pass rate and cost per solved task”
  • “Help me compare a candidate agent against a baseline using paired outcomes and a clear regression verdict”
  • “Define the fields and denominators I need to record for tool hallucination rate”
Step-by-step guide: how to use AI Agent Benchmark RegisterInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 1 original files, the MIT licence and an attribution note.

    ai-agent-evaluation-benchmarking.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

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

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip ai-agent-evaluation-benchmarking.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

  • Give the agent your actual benchmark suite, model and metrics so it fills the schema with your real fields rather than the examples
  • Ask for the denominator definitions explicitly, since pass rate and hallucination rate are only meaningful with them
  • Keep test data synthetic and avoid putting real keys or private endpoints into the generated artifacts

What's inside

  • SKILL.md

Redistributed unmodified from sickn33/agentic-awesome-skills 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.