AgentSkillsCN

tao-deepresearch

深入进行AI/ML研究。在分析arXiv论文、开展文献综述、寻找有影响力的研究论文、识别研究趋势，或探索各类实现方案时使用此功能。支持LaTeX源码分析、网络搜索论文，以及GitHub代码发现。

SKILL.md
--- frontmatter
name: tao-deepresearch
description: Deep research for AI/ML research. Use when analyzing arxiv papers, conducting literature reviews, finding influential papers, identifying research trends, or exploring implementations. Handles LaTeX source analysis, web search for papers, and GitHub code discovery.
argument-hint: [topic]
allowed-tools: Read, Grep, Glob, WebSearch, WebFetch, Bash(gh *)

Tao Deep Research

Conduct comprehensive AI/ML research by analyzing provided materials and searching for related literature.

Arguments

  • •$ARGUMENTS[0] - Research topic or question
  • •--depth - Research depth: quick (overview), medium (detailed), thorough (comprehensive)
    • •Default: thorough
  • •--focus - Research focus: methods (algorithms/techniques), experiments (benchmarks/results), theory (foundations/proofs)
    • •Default: methods

When no arguments are provided, use depth=thorough and focus=methods.

Input Types

The user may provide:

  1. •LaTeX source from arxiv papers (analyze the "Related Works" section as starting point)
  2. •Code for implementation context
  3. •Topic description for open-ended research

Research Workflow

Step 1: Analyze Provided Materials

If LaTeX source is provided:

  • •Parse the "Related Works" or "Related Work" section
  • •Extract cited papers, authors, and key themes
  • •Identify the research area and subfields
  • •Note any code repositories mentioned

If code is provided:

  • •Identify the methods/models implemented
  • •Check for paper references in comments or README

Step 2: Search for Related Literature

Use web search to find:

  • •Recent papers on the identified topics (prioritize last 2 years)
  • •Highly-cited foundational papers
  • •Survey papers for comprehensive coverage
  • •Arxiv preprints for cutting-edge work

Search queries should include:

  • •Key method names and algorithms
  • •Problem domain keywords
  • •Author names from seminal works

Step 3: Fetch and Analyze Papers

For important papers found:

  • •Fetch arxiv abstracts and metadata
  • •Identify citation counts and influence (when available)
  • •Note publication venues (NeurIPS, ICML, ICLR, ACL, CVPR, etc.)
  • •Track chronological development

Step 4: Search for Implementations

Search GitHub for:

  • •Official implementations of key papers
  • •Popular reimplementations with high stars
  • •Relevant libraries and frameworks
  • •Benchmark repositories

Step 5: Synthesize Findings

Analyze the collected information to identify:

  • •Evolution of methods over time
  • •Current state-of-the-art approaches
  • •Emerging trends and directions
  • •Open problems and challenges

Output Format

Generate a structured research report with these sections:

Key Papers

List the most important papers with:

  • •Title, authors, year, venue
  • •Arxiv link (if available)
  • •Brief description of contribution
  • •Relative influence/importance

Format:

code
**[Paper Title]** (Year)
Authors: [Names]
Venue: [Conference/Journal] | [arxiv:XXXX.XXXXX](https://arxiv.org/abs/XXXX.XXXXX)
Contribution: [1-2 sentence summary]

Methods/Approaches Overview

  • •Categorize methods by type/approach
  • •Explain key techniques and innovations
  • •Compare strengths and limitations
  • •Note computational requirements

Timeline/Trends Analysis

  • •Chronological development of the field
  • •Key breakthroughs and when they occurred
  • •Current dominant approaches
  • •Emerging directions and recent shifts

Open Problems/Future Directions

  • •Unsolved challenges in the field
  • •Limitations of current methods
  • •Promising research directions
  • •Potential applications

Code/Implementation References

  • •Official repositories with links
  • •Popular frameworks and libraries
  • •Benchmark datasets and evaluation code
  • •Format: [Repo Name](GitHub URL) - Brief description (stars if notable)

Depth Levels

Quick (5-10 papers):

  • •Focus on most influential recent papers
  • •Brief overview of methods
  • •Key trends only

Medium (15-25 papers):

  • •Comprehensive coverage of recent work
  • •Detailed methods comparison
  • •Full trend analysis

Thorough (30+ papers):

  • •Exhaustive literature review
  • •Historical context and evolution
  • •Deep technical analysis
  • •Multiple subcategories explored

Focus Areas

Methods: Prioritize algorithmic innovations, architectural designs, training techniques Experiments: Prioritize benchmarks, datasets, evaluation metrics, empirical results Theory: Prioritize mathematical foundations, convergence proofs, theoretical guarantees

Example Usage

code
/tao-deepresearch "GRPO training for reasoning models" --depth thorough --focus methods
code
/tao-deepresearch "efficient attention mechanisms" --depth quick

When LaTeX is provided:

code
User: [pastes LaTeX source]
/tao-deepresearch --depth medium --focus experiments

Research Best Practices

  1. •Recency: Prioritize papers from the last 2 years for current state-of-the-art
  2. •Influence: Weight highly-cited papers and papers from top venues
  3. •Diversity: Include different approaches and perspectives
  4. •Verification: Cross-reference claims across multiple sources
  5. •Completeness: Cover both foundational work and recent advances