Benchmark Sweep

Name: Benchmark Sweep
Availability: InStock
Author: Pthahnix

Analyze benchmark sets to generate cross-benchmark improvement ideas.

393|34|Updated Feb 10, 2026

Search Tags:#automation #performance #analysis #benchmark #sweep #cross-benchmark

Ready to run

npx skills add https://github.com/Pthahnix/De-Anthropocentric-Research-Engine --skill benchmark-sweep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Benchmark Sweep
Source: https://github.com/Pthahnix/De-Anthropocentric-Research-Engine/tree/main/skills/sop/benchmark-sweep
Command: npx skills add https://github.com/Pthahnix/De-Anthropocentric-Research-Engine --skill benchmark-sweep

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Benchmark Sweep addresses the need for consistent, repeatable analysis across multiple benchmarks to uncover cross-benchmark improvements and reduce ad-hoc evaluation.

Core Features & Use Cases

Systematic benchmark enumeration and comparison across multiple benchmarks.
Generation of cross-benchmark ideas to improve performance or quality across domains.
Integration with dare-agents toolchain to produce structured results (BenchmarkSweepResult) including benchmarkAnalysis[] and crossBenchmarkIdeas[].

Quick Start

Provide benchmarks and context to the dare-agents tool to run the sweep and obtain cross-benchmark insights.

Frequently Asked Questions about Benchmark Sweep

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I analyze performance across multiple benchmarks to find cross-benchmark improvements?▼

Cross-benchmark analysis requires systematically enumerating and comparing benchmark sets to identify improvements. You provide a configured benchmark dataset and context to generate structured results containing benchmark analysis and cross-benchmark ideas.

What is a cross-benchmark sweep in software performance or ML model evaluation?▼

A cross-benchmark sweep is a systematic analysis of multiple benchmark sets to uncover cross-domain insights. It replaces ad-hoc evaluation by generating structured cross-benchmark ideas to improve performance or quality across evaluated domains.

How do I run a benchmark sweep using the dare-agents toolchain?▼

To run a benchmark sweep, provide your benchmark datasets and context field to the dare-agents tool. The tool executes the sweep and outputs a BenchmarkSweepResult containing structured benchmarkAnalysis and crossBenchmarkIdeas arrays.

Can I use benchmark sweep analysis for both ML model evaluation and software performance metrics?▼

Yes, benchmark sweep analysis applies to evaluation workflows across multiple benchmarks in software performance, ML model evaluation, or design metrics. The systematic comparison yields cross-domain insights for any configured benchmark dataset.

What inputs do I need to generate cross-benchmark ideas for my evaluation workflow?▼

Generating cross-benchmark ideas requires a configured benchmark dataset input and a context field. These inputs integrate with the dare-agents tool to systematically analyze benchmarks and produce structured cross-benchmark improvement suggestions.

Does benchmark sweep analysis eliminate ad-hoc evaluation across multiple benchmarks?▼

Yes, benchmark sweep analysis addresses the need for consistent, repeatable analysis across multiple benchmarks. By systematically enumerating and comparing benchmark sets, it reduces ad-hoc evaluation and uncovers cross-benchmark improvements.