论文测试
- 作者仓库星标 0
- 作者更新于 实时读取
- 作者仓库 FarmFriend-Terminal-React
- 领域
- 通用
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @0-CYBERDYNE-SYSTEMS-0 · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 即装即用
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- Python
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: Hugging Face Paper Publisher
description: This skill helps you publish and manage research papers on the Hugging Face Hub, enabling seamle…
category: 通用
runtime: Python
---
# Hugging Face Paper Publisher 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Overview / Core Capabilities / 1. Paper Creation and Publishing”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Overview / Core Capabilities / 1. Paper Creation and Publishing”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 先确认触发方式
原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
给清楚输入和边界
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件。
小样例验证后再放大
先用一个小任务确认它会围绕“Overview / Core Capabilities / 1. Paper Creation and Publishing”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
复核后再交付
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: Hugging Face Paper Publisher
description: This skill helps you publish and manage research papers on the Hugging Face Hub, enabling seamle…
category: 通用
source: 0-CYBERDYNE-SYSTEMS-0/FarmFriend-Terminal-React
---
# Hugging Face Paper Publisher
## 什么时候使用
- Hugging Face Paper Publisher 是一个通用扩展技能,按 SKILL 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Overview / Core Capabilities / 1. Paper Creation and Publishing」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 证据边界与执行链路
作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "Hugging Face Paper Publisher" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Overview / Core Capabilities / 1. Paper Creation and Publishing
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python | 读取文件、写入/修改文件 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Hugging Face Paper Publisher Skill
Overview
This skill helps you publish and manage research papers on the Hugging Face Hub, enabling seamless integration with models, datasets, and reproducible research.
Core Capabilities
1. Paper Creation and Publishing
- Create new paper pages on Hugging Face Hub
- Format paper content with proper markdown structure
- Add paper metadata (title, authors, abstract, keywords)
- Upload PDF versions and supplementary materials
- Set paper visibility (public/private)
2. Model and Dataset Integration
- Link papers to associated models on the Hub
- Connect papers with relevant datasets
- Create reproducible research workflows
- Add code snippets and implementation details
- Set up automatic model card generation
3. Authorship and Attribution
- Claim and verify authorship
- Manage contributor lists and affiliations
- Add ORCID integration for author identification
- Handle citation and reference management
- Update author information over time
4. Paper Management
- Update existing papers with new versions
- Add errata and corrections
- Manage paper discussions and community feedback
- Track paper metrics and usage statistics
- Handle paper withdrawal or retraction
Usage Instructions
Publishing a New Paper
- Prepare your paper content in markdown format
- Gather paper metadata (title, authors, abstract, etc.)
- Create a new paper repository on Hugging Face Hub
- Upload PDF and supplementary materials
- Link associated models and datasets
- Set appropriate visibility and licensing
Linking Models and Datasets
- Identify relevant models and datasets on the Hub
- Create proper linking in paper metadata
- Add reproducible code snippets
- Set up automatic model card references
- Ensure version compatibility
Managing Authorship
- Add all contributors with proper attribution
- Include institutional affiliations
- Add ORCID IDs for author verification
- Update author information as needed
- Handle author order and contributions
Dependencies
- huggingface_hub
- datasets (for dataset integration)
- transformers (for model integration)
- PyPDF2 or pdfplumber (for PDF processing)
- python-markdown (for markdown processing)
- requests (for API interactions)
Best Practices
- Use clear, descriptive titles and abstracts
- Include proper citations and references
- Provide reproducible code when possible
- Link to all relevant models and datasets
- Use appropriate open-source licenses
- Keep paper information up to date
- Engage with community feedback and discussions
Integration Notes
- Works seamlessly with Hugging Face Hub API
- Supports integration with GitHub for code linking
- Compatible with various citation formats (APA, MLA, IEEE)
- Can be used with manuscript preparation tools
- Supports automated paper updates from version control
Paper Structure Template
# Paper Title
## Abstract
[Brief summary of the paper]
## Authors
- [Author Name](https://huggingface.co/author-username) - Institution
## Paper Content
[Full paper content in markdown format]
## Models Used
- [Model Name](https://huggingface.co/model-repo) - Description
## Datasets
- [Dataset Name](https://huggingface.co/dataset-repo) - Description
## Code Repository
[Link to GitHub or other code repository]
## Citation
```bibtex
@paper{citation-key,
title={Paper Title},
author={Author Names},
year={Year},
journal={Journal/Conference}
}
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核