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
Install
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.
npx agentmarket install skill/adu2021-abc-bench-benchmarking-agentic-backend-coding-in
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About
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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Signals
- Stars
- 6
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Mar 24, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/ADu2021/skillXiv
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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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)