cooperbench-why-coding-agents-cannot-be-your-teamm

Implement techniques from CooperBench: Why Coding Agents Cannot be Your Teammates Yet. Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus

Ir para a instalação

Repositório: ADu2021/skillXiv
Última atividade na origem: 24 de março de 2026 às 19:42
Idioma detectado do SKILL.md: inglês
Estrelas: 6
Forks: 0

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.

npx skills add https://github.com/ADu2021/skillXiv --skill cooperbench-why-coding-agents-cannot-be-your-teamm

O comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.

Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

SKILL.md

Instruções da origem · Visualização somente leitura

Voltar à pesquisa Voltar à instalação

meaningful-kebab-case-name

Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.

2026-03-26

action-quantization-behavior-cloning

Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.

2026-03-26

adaptive-lora-personalized-ranks

Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.

2026-03-26

Ver todas deste repositório

name	cooperbench-why-coding-agents-cannot-be-your-teamm
title	CooperBench: Why Coding Agents Cannot be Your Teammates Yet
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.13295
keywords	["agent"]
description	Implement techniques from CooperBench: Why Coding Agents Cannot be Your Teammates Yet. Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus

Overview

This skill implements concepts from the research paper [2601.13295].

When to Use

When you need to implement techniques described in this paper
When working on problems that this research addresses
When you want to understand the core concepts and methodology

When NOT to Use

This skill provides research-level insights; production implementations may require additional engineering
Some concepts may require significant tuning for specific use cases
Always evaluate applicability to your specific problem domain

Key Concepts

The paper addresses: Resolving team conflicts requires not only task-specific competence, but also social intelligence to find common ground and build consensus. As AI agents increasingly collaborate on complex work, they must develop coordination capabilities to functio...

For detailed methodology, refer to the full paper.

cooperbench-why-coding-agents-cannot-be-your-teamm

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Overview

When to Use

When NOT to Use

Key Concepts

Mais deste repositório

Overview

When to Use

When NOT to Use

Key Concepts