challenge

Name: challenge
Availability: InStock
Author: tokyo-megacorp

Benchmark debate agents' bug detection accuracy on curated code challenges with F1 scores.

Updated Aug 27, 2026

Search Tags:#benchmark #assessment #code #bug detection #F1 score #debaters

Ready to run

npx skills add https://github.com/tokyo-megacorp/autoimprove --skill challenge-tokyo-megacorp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: challenge
Source: https://github.com/tokyo-megacorp/autoimprove/tree/main/skills/challenge
Command: npx skills add https://github.com/tokyo-megacorp/autoimprove --skill challenge-tokyo-megacorp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Facilitates testing and benchmarking debate agents' accuracy in identifying bugs within curated code challenge datasets.

Core Features & Use Cases

Challenge Evaluation: Runs debate agents against specific coding challenges and scores their bug detection using F1 metrics.
Filtering and Customization: Supports filtering challenges by suite, language, difficulty, or tags, enabling targeted assessments.
Use Case: Ideal for quantifying the bug-finding performance of debate agents on Python challenges, guiding prompt improvements and model tuning.

Quick Start

Use the challenge skill to run a benchmark on a set of curated code issues and evaluate agent bug-finding accuracy.

Frequently Asked Questions about challenge

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

FAQPage Schema

How do I benchmark an AI agent's bug detection accuracy on code challenges?▼

To benchmark bug detection accuracy, run debate agents against curated code challenges to calculate F1 scores and evaluate performance across different challenge subsets. This provides detailed metrics for quantifying bug-finding performance and guiding prompt improvements.

Can I filter code challenges by programming language or difficulty for targeted agent assessment?▼

Yes, you can filter code challenges by suite, language, difficulty, or tags for targeted agent assessment. This enables precise evaluation of bug detection accuracy on specific subsets, such as focusing solely on Python challenges of a certain difficulty level.

What metrics are used to evaluate debate agents finding bugs in code?▼

F1 scores are used to evaluate debate agents finding bugs in code. The benchmark provides detailed F1 metrics and insights into performance across different challenge subsets, aiding prompt tuning and system evaluation for bug detection accuracy.

Does benchmarking debate agents require structured challenge datasets?▼

Yes, benchmarking debate agents requires structured challenge benchmarks to evaluate bug detection accuracy. The system runs agents against these curated code challenge datasets and scores their performance using F1 metrics to provide actionable insights.

How do I evaluate prompt tuning improvements for bug detection agents?▼

Evaluate prompt tuning improvements by running bug detection agents on curated code challenges and comparing their F1 scores. The benchmark provides detailed performance insights across different challenge subsets, directly guiding and quantifying model tuning efforts.

What is the best way to assess code bug detection performance across different challenge subsets?▼

The best way to assess bug detection performance across different challenge subsets is using a benchmark that calculates F1 scores. It runs debate agents on curated code challenges and provides detailed insights into accuracy across filtered subsets.