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paper2codepaper2code 搜索

Agent Skill

paper2code 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

517

周安装

22

GitHub Stars

22

下载量

181
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paper2code(paper2code 搜索)
来源仓库:https://github.com/proyecto26/sherlock-ai-plugin
仓库路径:skills/paper2code
安装命令:
npx skills add https://github.com/proyecto26/sherlock-ai-plugin --skill paper2code
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/proyecto26/sherlock-ai-plugin --skill paper2code

简介

用于查找、检索和筛选相关信息,支持关键词快速定位候选结果。

  • 适用于需要根据任务场景或来源线索在 Codex、Claude 等平台中获取信息时。
  • 可通过 GitHub 仓库路径和 README 进一步核验具体用法和功能范围。
  • 安装方式:通过 npx 从 proyecto26/sherlock-ai-plugin 仓库添加技能。
  • 注意:使用前请确认权限范围、维护状态及是否触发联网或命令执行操作。

SKILL.md

Paper2Code: AI Agent for Converting Research Papers into Code

Overview

This Skill executes a 4+2 stage pipeline effectively systematically analyzing research papers and converting them into executable code.

Core Principle: Do not simply read the paper and generate code; generate a structured intermediate representation (YAML) first, then write the code.


⚠️ Critical Behavioral Control Rules (CRITICAL)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ MANDATORY BEHAVIORAL RULES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

1. Implement one file at a time
2. Proceed to the next file only after completing the current file, without asking for confirmation
3. Original paper specifications always take precedence over reference code
4. Perform a Self-Check for each Phase before completion
5. Save all intermediate results as YAML files

DO:
✓ Implementing exactly what is stated in the paper
✓ Write simple and direct code
✓ Working code first, elegant code later
✓ Test each component immediately
✓ Move to the next file immediately after implementation is complete

DON'T:
✗ Do not ask "Shall I implement the next file?" between files
✗ Extensive documentation not required for core functionality
✗ Optimization not needed for reproducibility
✗ Excessive abstraction or design patterns
✗ Providing instructions without writing actual code
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Input Processing

Supported Formats

  1. arXiv URL: https://arxiv.org/abs/xxxx.xxxxx or https://arxiv.org/pdf/xxxx.xxxxx.pdf
  2. PDF File Path: /path/to/paper.pdf
  3. Converted Text/Markdown: When paper content is provided as text

Input Processing Method

For arXiv URL:

# Convert to PDF URL and download
curl -L "https://arxiv.org/pdf/xxxx.xxxxx.pdf" -o paper.pdf

# Convert PDF to text (using pdftotext)
pdftotext -layout paper.pdf paper.txt

For PDF File:

pdftotext -layout "/path/to/paper.pdf" paper.txt

Pipeline Overview

[User Input: Paper URL/File]
        │
        ▼
┌─────────────────────────────────────────────┐
│ Step 0: Acquire Paper Text                  │
│ - arXiv URL → Download PDF                  │
│ - PDF → Convert to Text                     │
└─────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────────────┐
│ Phase 0: Search Reference Code (Optional)   │
│ @[05_reference_search.md]                   │
│ Output: reference_search.yaml               │
└─────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────────────┐
│ Phase 1: Algorithm Extraction               │
│ @[01_algorithm_extraction.md]               │
│ Output: 01_algorithm_extraction.yaml        │
└─────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────────────┐
│ Phase 2: Concept Analysis                   │
│ @[02_concept_analysis.md]                   │
│ Output: 02_concept_analysis.yaml            │
└─────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────────────┐
│ Phase 3: Implementation Plan                │
│ @[03_code_planning.md]                      │
│ Output: 03_implementation_plan.yaml         │
└─────────────────────────────────────────────┘
        │
        ▼
┌─────────────────────────────────────────────┐
│ Phase 4: Code Implementation                │
│ @[04_implementation_guide.md]               │
│ Output: Complete Project Directory          │
└─────────────────────────────────────────────┘

Data Transfer Format Between Stages

Phase 1 → Phase 2 Transfer

phase1_to_phase2:
  algorithms_found: "[Number of found algorithms]"
  key_algorithms:
    - name: "[Algorithm Name]"
      section: "[Paper Section]"
      complexity: "[Simple/Medium/Complex]"
  hyperparameters_count: "[Number of collected hyperparameters]"
  critical_equations: "[List of critical equation numbers]"
  missing_info: "[List of missing information]"

Phase 2 → Phase 3 Transfer

phase2_to_phase3:
  components_count: "[Number of identified components]"
  implementation_complexity: "[Low/Medium/High]"
  key_dependencies:
    - "[Component A] → [Component B]"
  experiments_to_reproduce:
    - "[Experiment Name]: [Expected Result]"
  success_criteria:
    - "[Specific Success Criteria]"

Phase 3 → Phase 4 Transfer

phase3_to_phase4:
  file_order: "[List of files in implementation order]"
  current_file: "[Currently implementing file]"
  completed_files: "[List of completed files]"
  blocking_dependencies: "[Dependencies to resolve]"

Detail of Each Phase

Phase 0: Reference Code Search (Optional)

Using the @05_reference_search.md prompt:

  • Search for and evaluate 5 similar implementations
  • Secure references to improve implementation quality
  • Output: Reference list in YAML format

Phase 1: Algorithm Extraction

Using the @01_algorithm_extraction.md prompt:

  • Extract all algorithms, equations, and pseudocode
  • Collect hyperparameters and configuration values
  • Organize training procedures and optimization methods
  • Output: Complete algorithm specification in YAML format

Phase 2: Concept Analysis

Using the @02_concept_analysis.md prompt:

  • Map paper structure and sections
  • Analyze system architecture
  • Identify component relationships and data flow
  • Organize experiment and validation requirements
  • Output: Implementation requirements specification in YAML format

Phase 3: Establish Implementation Plan

Using the @03_code_planning.md prompt:

  • Integrate results from Phase 1 and 2
  • Generate detailed implementation plans for 5 essential sections:

1. file_structure: Project file structure 2. implementation_components: Implementation component details 3. validation_approach: Validation and testing methods 4. environment_setup: Environment and dependencies 5. implementation_strategy: Step-by-step implementation strategy

  • Output: Complete YAML implementation plan (8000-10000 characters)

Phase 4: Code Implementation

Following the guide @04_implementation_guide.md:

  • Generate code file by file according to the plan
  • Implement in dependency order
  • Each file must be complete and executable
  • Output: Executable codebase

Memory Management

Refer to the guide @06_memory_management.md:

  • Context management when processing long papers
  • Saving step-by-step outputs
  • Recovery protocol in case of interruption

Quality Standards

Principles that Must Be Followed

  • Completeness: Complete implementation without placeholders or TODOs
  • Accuracy: Accurately reflect equations and parameters specified in the paper
  • Executability: Code that can be executed immediately
  • Reproducibility: Must be able to reproduce the results of the paper

File Implementation Order

  1. Configuration and environment files (config, requirements.txt initialization)
  2. Core utilities and base classes
  3. Main algorithm/model implementation
  4. Training and evaluation scripts
  5. Documentation (README.md, requirements.txt finalization)

✅ Final Completion Checklist (MANDATORY)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ BEFORE DECLARING COMPLETE - ALL MUST BE YES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

□ All algorithms in the paper implemented?       → YES / NO
□ Correct versions of environment/datasets set?  → YES / NO
□ All comparison methods referenced implemented? → YES / NO
□ Working integration to run paper experiments?  → YES / NO
□ All metrics, figures, tables reproducible?     → YES / NO
□ Basic docs explaining how to reproduce?        → YES / NO
□ Code runs without errors?                      → YES / NO

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
⚠️ If even one is NO, it is NOT complete!
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Usage Examples

Example 1: arXiv Paper

User: Implement this paper https://arxiv.org/abs/2301.12345

Claude: I will analyze the paper and convert it to code.

[Phase 0: Reference Code Search (Optional)...]
[Phase 1: Algorithm Extraction...]
[Phase 2: Concept Analysis...]
[Phase 3: Establish Implementation Plan...]
[Phase 4: Code Generation...]

Example 2: PDF File

User: Implement the algorithms from this paper /home/user/papers/attention.pdf

Example 3: Specific Request

User: Implement only the algorithm in Section 3 of this paper

Related Files


Precautions

⚠️ REMEMBER:

1. Read the paper thoroughly: Start implementation after understanding the entire content
2. Save detailed results: Save YAML output of each Phase as a file
3. Incremental implementation: Do not generate all code at once, proceed file by file
4. Include verification: Include simple test code if possible
5. Reference is inspiration: Reference code is for understanding and application, not copying

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.99%
按下载量换算65

Claude

31.51%
按下载量换算57

Cursor

17.9%
按下载量换算32

Gemini CLI

10.78%
按下载量换算20

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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