ADu2021/abc-bench-benchmarking-agentic-backend-coding-in
skills/skillxiv-v0.0.2-claude-opus-4.6/abc-bench-benchmarking-agentic-backend-coding-in/SKILL.md
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we intr...
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SKILL.md
- name:
- abc-bench-benchmarking-agentic-backend-coding-in
- title:
- ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development Scenarios
- version:
- 0.0.2
- engine:
- skillxiv-v0.0.2-claude-opus-4.6
- license:
- MIT
- url:
- https://arxiv.org/abs/2601.11077
- keywords:
- [Agent, Benchmark]
- description:
- The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-driven problem solving. However, current benchmarks predominantly evaluate code logic in static contexts, neglecting the dynamic, full-process requirements of real-world engineering, particularly in backend development which demands rigorous environment configuration and service deployment. To address this gap, we intr...
Problem
ABC-Bench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.
Key Approach
The paper introduces a novel framework, methodology, or benchmark for abc-bench. The core contributions include:
- Systematic framework or benchmark for agent evaluation and development
- Empirical findings on agent performance, efficiency, or capabilities
- Generalizable principles applicable across domains
When to Use
Use this skill when you need to:
- Evaluate or benchmark autonomous agent systems
- Understand best practices in agent design and evaluation
- Learn empirical results on agent performance
- Improve agent efficiency, reasoning, or capabilities
When NOT to Use
- For non-agent-related tasks
- When seeking quick implementation code (see the paper for details)
- For general knowledge unrelated to autonomous agents
Resources
- ArXiv Abstract: https://arxiv.org/abs/2601.11077
- Full PDF: https://arxiv.org/pdf/2601.11077
- HTML Version: https://arxiv.org/html/2601.11077
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
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