swe_bench

Automate SWE-bench benchmark execution and validation in Docker containers.

Updated Jun 22, 2026
One-click install
npx skills add https://github.com/alangeb/tau --skill swe-bench-alangeb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swe_bench
Source: https://github.com/alangeb/tau/tree/main/src/skills/swe_bench
Command: npx skills add https://github.com/alangeb/tau --skill swe-bench-alangeb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines the complex, multi-step process of running SWE-bench evaluations, managing containerized environments, and analyzing agent-generated patches.

Core Features & Use Cases

  • Automated Pipeline: Orchestrates the full lifecycle from container preparation and agent execution to patch extraction and test evaluation.
  • Artifact Management: Provides structured logging and audit trails for every test run, including stdout, context files, and patch diffs.
  • Use Case: Use this to run a batch of GitHub issues through an AI agent, automatically verify the generated fixes against the project test suite, and generate a summary report of pass/fail results.

Quick Start

Use the swe_bench skill to start the evaluation pipeline for the next five test cases using the cuda llm group.

Frequently Asked Questions about swe_bench

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

FAQPage Schema
How do I automate SWE-bench evaluation for AI-generated code patches?▼

To automate SWE-bench evaluation, this Skill orchestrates the full lifecycle from container preparation and agent execution to patch extraction and test validation against existing project test harnesses.

Do I need Docker to run SWE-bench patch testing workflows?▼

Yes, you need a configured Docker environment to run SWE-bench patch testing workflows, as the Skill executes and validates software engineering benchmarks within isolated container environments.

How does automated benchmark validation handle test artifacts and logs?▼

Automated benchmark validation provides structured logging and artifact management for every test run, capturing stdout, context files, and patch diffs to generate a comprehensive audit trail.

Can I batch process GitHub issues through an AI agent for automatic fix verification?▼

You can batch process GitHub issues through an AI agent to automatically verify generated fixes against the project test suite and generate a summary report of pass/fail results.

What is the tau agent framework's role in software engineering benchmark execution?▼

The tau agent framework manages test lifecycles and artifact generation during software engineering benchmark execution, requiring configured access to automate the end-to-end evaluation pipeline.