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abc-bench-benchmarking-agentic-backend-coding-in

The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex…

Skill by ADu2021

IndexedMIT★ 6
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

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About

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...

Use cases: backend

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/abc-bench-benchmarking-agentic-backend-coding-in

What's inside

Skill name
abc-bench-benchmarking-agentic-backend-coding-in
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...
License
MIT
Instructions
35 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

## 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:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities  
3. 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.

From ADu2021/skillXiv, MIT.

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Last commit (this folder)
Mar 24, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

abc-bench-benchmarking-agentic-backend-coding-in skill. Agent Mart. https://agentmart.dev/skills/adu2021-abc-bench-benchmarking-agentic-backend-coding-in (last updated 2026-10-24)