Skills

Weavebench Cua Reproduce

Reproduce CUA-Harness experiments on WeaveBench from a GitHub checkout.

Install

npx skills add AMAP-ML/LongHorizon-Harness --skill weavebench-cua-reproduce -g -a claude-code -y
Category
Testing
Updated
14d ago
License
MIT
Context cost
Mediumabout 1,537 tokens
Runs scripts
Yes, review before use

What it does

Reproduce CUA-Harness experiments on WeaveBench from a GitHub checkout. Use when the user wants an AI coding agent to set up dependencies, download WeaveBench assets, prepare the 120G VM, configure Qwen/Anthropic-compatible APIs, run smoke tests, launch full or subset evaluations, inspect logs, or summarize scores for this repository.

WeaveBench CUA-Harness Reproduction

Use this skill to guide an agent through a complete, reproducible WeaveBench CUA-Harness run. The expected repository layout is:

WeaveBench-harness/
  WeaveBench/
  cua-harness/
  skills/weavebench-cua-reproduce/

Do not invent alternate launch commands. Prefer the bundled helper script and the project scripts under WeaveBench/scripts/.

Supported Scope

Supported automation:

  • Local Docker/KVM WeaveBench evaluation.
  • Qwen 3.7-Plus through an Anthropic-compatible endpoint.
  • Claude Code execution backend through cua_harness_claudecode.
  • OpenClaw judge setup and verification.
  • Official WeaveBench assets downloaded from the HuggingFace dataset.
  • A 120G copy of the official Ubuntu qcow2 plus per-task rootfs growth.

Not automated:

  • Cloud VM provisioning.
  • Public image-hosting service deployment.
  • New model-provider adapters beyond the existing Anthropic-compatible path.
  • Non-Claude-Code execution backend reproduction.
  • Deleting or pruning existing results.

If the user requests unsupported automation, explain the boundary and provide the closest supported local Docker/KVM path.

Start With Intake

Before doing setup work on a new machine, run or mentally follow:

./skills/weavebench-cua-reproduce/scripts/reproduce.sh intake

Confirm:

  • Linux Docker/KVM is available or installable.
  • The user accepts large downloads and creation of Ubuntu_120G.qcow2.
  • The model endpoint and API key are available.
  • Image proxy should be enabled with public upload/show URLs, or disabled because the endpoint accepts large base64 screenshots.
  • The desired scope is doctor, smoke, subset, or full 114-task evaluation.
  • Long-running/full evaluation is allowed, especially if other experiments may already be active.

Workflow

Read the whole SKILL.md on GitHub

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