ResearchX

The world's first literature-driven AI research assistant. Methods come from real papers, not hardcoded templates.

GitHub Codex Skill License: MIT

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📋 Table of Contents

• Why ResearchX?
• Architecture Overview
• Core Innovation: Literature-Driven Methods
• Modules Deep Dive
• Workflow Examples
• Quick Start
• Installation
• Output Artifacts
• FAQ
• Contributing

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🎯 Why ResearchX?

The Problem with Existing AI Research Tools

Tool	Limitation
ChatGPT / GPT-4	Uses pre-training cutoff knowledge, can't find latest methods
Paper chatbots	Only summarize, don't help you design new research
Research Copilots	Hardcode methods ("use Transformer"), not grounded in current literature
Literature tools	Find papers but can't synthesize into actionable research plans

What ResearchX Does Differently

Other tools:    "For image segmentation, use U-Net or Transformer."
                     ↑ Pre-defined, may be outdated

ResearchX:      "For crop mapping from satellite imagery (2025),
                 the SOTA methods found via literature search are:
                 - SwinUNet (94.2% OA, 2024)
                 - SSTFormer (95.1% F1, 2025)
                 - GeoFM (emerging foundation model, 2025)
                 The gap is: none of these handle cloud-heavy regions well.
                 Innovation opportunity: [cross-domain idea]"
                     ↑ Grounded in real 2023-2026 literature

Target Users

• PhD students & researchers — From literature review to paper submission
• Masters students — Thesis topic discovery and experimental design
• Professors & PIs — Grant proposals and research planning
• Industry R&D — Method selection and innovation discovery

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🏗 Architecture Overview

System Architecture

graph TB
    subgraph "User Input Layer"
        U[User Request]
        PDF[PDF Upload]
        DOI[DOI / Paper Link]
    end

    subgraph "ResearchX Core"
        R[Task Router]
        R --> P[Paper Analysis]
        R --> G[Gap Mining]
        R --> M[Method Mining]
        R --> E[Experiment Design]
        R --> W[Manuscript Writer]
        R --> S[SCI Upgrade]
        R --> RV[Peer Review]
        R --> V[Visual Generation]
        R --> GR[Grant Proposal]
        R --> LR[Lit Review Builder]
    end

    subgraph "Knowledge Sources"
        WS[web_search ~ 2023-2026]
        LS[Local Scripts]
    end

    U --> R
    PDF --> P
    DOI --> P
    P --> WS; G --> WS; M --> WS; W --> WS; GR --> WS; LR --> WS
    M --> LS; V --> LS

    subgraph "Output"
        O1[analysis_*.md] --> R
        O2[topics_*.md] --> R
        O3[methods_*.md] --> R
        O4[manuscript_*.md] --> R
        O5[review_report.md] --> R
        O6[grant_proposal.md] --> R
    end

    style WS fill:#e1f5fe,stroke:#0288d1
    style R fill:#f3e5f5,stroke:#7b1fa2

Module Chaining (Auto-Integration)

flowchart LR
    subgraph "End-to-End: Vague Idea → Published Paper"
        A1[Clarify Topic] --> A2[Gap Mining]
        A2 --> A3[Method Mining]
        A3 --> A4[Experiment Design]
        A4 --> A5[Manuscript Writing]
        A5 --> A6[SCI Upgrade]
        A6 --> A7[Review Simulation]
        A7 --> A8[Visual Generation]
    end

    subgraph "Paper Analysis → Improvement"
        B1[Paper Analysis] --> B2[Method Comparison]
        B2 --> B3[Missing Experiments]
        B3 --> B4[Upgrade Plan]
    end

    A1 -.->|or| B1

Literature-Driven Methodology Flow

sequenceDiagram
    participant U as User
    participant RX as ResearchX
    participant WS as Web Search
    participant LS as Local Scripts

    U->>RX: "I want to study [topic]"
    RX->>WS: Search reviews + surveys
    RX->>WS: Search challenges + gaps
    RX->>WS: Search SOTA methods
    WS-->>RX: Paper data
    RX->>LS: analyze_methods.py
    LS-->>RX: Method taxonomy + trends
    RX->>RX: Identify research gaps
    RX->>U: 5 topics with literature-backed methods
    U->>RX: "I like topic 3, write it up"
    RX->>WS: Targeted method search
    RX->>LS: Generate method landscape
    RX->>U: Manuscript + citation list
    RX->>U: "Shall I continue to SCI upgrade?"
    U->>RX: "Yes"
    RX->>U: Upgrade plan + peer review

---

💡 Core Innovation: Literature-Driven Methods

This is the single most important design decision in ResearchX.

How It Works

User says: "I want to study crop mapping using deep learning"

STEP 1 — web_search: "crop mapping deep learning review 2024 2025"
  → Finds: 2024 survey in RSE, 2025 benchmark paper

STEP 2 — web_search: "crop mapping challenge limitation"
  → Finds: Cloud cover problem, limited generalization, label scarcity

STEP 3 — web_search: "crop mapping state-of-the-art 2025"
  → Finds: SSTFormer, CropFormer, GeoFM foundation model

STEP 4 — Method landscape built:
  | Method Family     | Best Performer  | Year | Limitation           |
  |-------------------|----------------|------|----------------------|
  | CNN-based         | DeepCropNet    | 2023 | Limited context      |
  | Transformer-based | SSTFormer      | 2025 | Heavy computation    |
  | Foundation Models | GeoFM          | 2025 | Data-hungry          |

STEP 5 — Gap + Innovation identified:
  "None of these methods handle heavy cloud cover. 
   Innovation: Combine SSTFormer's accuracy with a cloud-imputation module from meteorology literature."
                        ↑ Cross-domain transfer from meteorology

Why This Matters

• Always current — Not limited to pre-training data
• Always grounded — Every claim cites a real paper
• Cross-domain — Can borrow methods from any adjacent field
• Falsifiable — User can check the cited papers

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🧩 Modules Deep Dive

1. 📄 Paper Analysis Engine

Input: PDF / DOI / Paper title
Output: analysis_[Paper]_YYYYMMDD.md

- One-sentence summary
- Research logic chain (RQ → Data → Method → Results)
- Academic thought analysis (Why done? Why this approach?)
- Strengths & Weaknesses table
- Missing experiments detected
- Literature context (latest SOTA comparison)
- Improvement suggestions

2. 🔍 Research Gap Mining

Input: Research domain or topic
Output: topics_[Topic]_YYYYMMDD.md

Produces a gap taxonomy (Solved / Unsolved / Controversial / Unexplored) and generates 5 concrete SCI topic proposals with:

• Specific research question
• Innovation statement (citing concrete papers)
• Data requirements
• Literature-backed method recommendation
• Target journal & feasibility score

3. ⚙️ Method Mining

Input: Research topic
Output: methods_[Topic]_YYYYMMDD.md

Component	Description
Method Families	Extracted from literature, not assumed
Evolution Timeline	2022 → 2023 → 2024 → 2025-2026
Method Landscape Table	Performance, pros, cons (from papers)
Innovation Opportunity	What's missing? What can be transferred?

4. 🧪 Experiment Design

Input: Proposed method
Output: Full experimental protocol

Designs: Baseline comparison (3-5 SOTA), Ablation study, Parameter sensitivity, Generalization tests (×3), Robustness tests, Statistical significance.
Then detects: "What experiments are missing that reviewers would ask for?"

5. ✍️ Manuscript Writing

Input: Research plan
Output: manuscript_[Topic]_YYYYMMDD.md

Generates any section on demand with journal-adapted style:

Section	Template
Abstract	Background(1s) → Problem(1s) → Method(2-3s) → Results(2s) → Implication(1s)
Introduction	Broad → Specific → Gap → Our work → Contributions
Methods	Data → Model → Implementation → Evaluation
Results	Overall → Ablation → Parameter → Generalization
Discussion	Findings → Comparison → Limitations → Future

6. 📈 SCI Upgrade System

Input: Draft paper
Output: Upgrade action plan

Scores 4 dimensions (Novelty, Experiments, Writing, Related Work) and produces a ranked priority action list with specific literature citations. Recommends target journals with predicted acceptance rates.

7. 📝 Peer Review Simulation

Input: Paper draft
Output: review_report.md + response letter

Generates 3-5 major comments + 2-3 minor comments from a selected reviewer persona. Includes a revision strategy and a point-by-point response letter.

8. 🎨 Visual Generation

Input: Research topic
Output: Prompts for GPT Image / Midjourney / Flux

Generates:

• Graphical abstract prompts (3 platforms, 3 journal styles)
• Research poster layouts (A0, A1, 9:16, conference)
• Mermaid workflow diagram code

9. 📚 Literature Review Builder

Input: Research topic
Output: literature_matrix_[Topic]_YYYYMMDD.md

Builds a structured literature matrix table → generates a coherent "Related Work" section with thematic grouping and critical comparison.

10. 🏛 Grant Proposal Builder

Input: Research topic
Output: grant_proposal_[Topic]_YYYYMMDD.md

Full proposal structure (NSFC-compatible and international) with literature-backed claims, scientific questions, technical route, innovation points, and expected outcomes.

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🔄 Workflow Examples

Example 1: PhD Student Starting a New Topic

User: "I'm a PhD student in remote sensing. I want to study crop type mapping
      using deep learning, but I need a specific topic and method."

ResearchX auto-chains: Gap Mining → Method Mining → Experiment Design

1. Clarify: "What region? What crops? What data do you have access to?"
2. Search: "crop type mapping deep learning review 2024 2025"
          "crop mapping challenge limitation"
          "crop mapping state-of-the-art 2025"
3. Gap analysis: identifies "few-shot learning for rare crops" as an open problem
4. Propose 5 topics with literature-backed methods
5. User picks one: "Few-shot crop mapping using foundation models"
6. Search methods specifically for this topic
7. Design full experimental protocol
8. Save: topics_crop_mapping_20250610.md, methods_few_shot_crop_20250610.md

Example 2: Upgrading a Q2 Paper to Q1

User: "I have a draft paper on urban land use classification using ResNet.
      I want to publish in RSE (Q1). Help me upgrade it."

ResearchX auto-chains: Paper Analysis → Method Mining → SCI Upgrade

1. Analyze the paper, extract its logic chain
2. Search: "urban land use classification 2024 2025"
3. Compare: The paper uses ResNet-50, while SOTA now uses Vision Transformers
4. Identify: Missing experiments (cross-city generalization, ablation)
5. Generate upgrade plan:
   Priority 1: Replace backbone with Swin Transformer (cite 3 papers)
   Priority 2: Add cross-city generalization test
   Priority 3: Add ablation study for each module
   Priority 4: Update related work with 8 missing 2024 papers
6. Simulate a review from RSE reviewer perspective
7. Save: upgrade_plan_urban_land_use_20250610.md, review_report.md

Example 3: Grant Proposal Writing

User: "I need to write an NSFC grant on ecological monitoring with AI."

ResearchX auto-chains: Gap Mining → Method Mining → Grant Proposal

1. Search: "ecological monitoring deep learning 2024 2025" (English)
          "生态监测 深度学习 2024 2025" (Chinese)
2. Find current gaps: lack of multi-sensor fusion methods
3. Propose research focus
4. Search methods for the chosen focus
5. Generate full grant proposal with:
   - 立项依据 (Research Rationale) — literature cited
   - 科学问题 (Scientific Questions)
   - 技术路线 (Technical Route) — Mermaid diagram
   - 创新点 (Innovation Points) — compared to existing work
   - 预期成果 (Expected Outcomes)
6. Save: grant_ecological_monitoring_20250610.md

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🚀 Quick Start

In Codex CLI / Desktop App

Just mention any research-related need:

# Analyze a paper
"Please analyze this paper for me: [paste DOI or upload PDF]"

# Find a research topic
"I need a research topic in [your domain]. Find me gaps and propose 5 topics."

# Find methods
"What methods should I use for [your problem]?"

# Write a paper
"Write the introduction and methods sections for a paper on [topic]."

# Upgrade to Q1
"I have a paper draft. Help me assess its quality and plan Q1 upgrades."

# Generate visuals
"Create a graphical abstract prompt for my paper on [topic]."

Sample Output

Every module produces a structured markdown file. Example: topics_crop_mapping_20250610.md

## Topic 3: Few-Shot Crop Mapping via Foundation Model Fine-Tuning

**Research Question**: Can a pre-trained foundation model (e.g., GeoFM) achieve
>90% crop classification accuracy with fewer than 50 labeled samples per class?

**Innovation**: First systematic evaluation of foundation model fine-tuning
strategies for crop mapping in data-scarce regions (cf. [Liu, 2024] which
requires 500+ samples).

**Data Needed**: Sentinel-2 imagery + Cropland Data Layer (CDL) labels

**Proposed Methods**: 
- Baseline: SSTFormer (SOTA at 95.1% with full data) [Chen, 2025]
- Proposed: GeoFM + LoRA fine-tuning [adapting from NLP, 2024]
- Also evaluate: Prompt-based learning [new, not yet applied to crops]

**Target Journal**: Remote Sensing of Environment, IF 13.5, Q1
**Feasibility**: ★★★★☆

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🔍 How to Find & Install ResearchX

ResearchX is available on 10+ platforms and registries:

Platform	How to Find	Install Command
GitHub	Search "xingguangYan/ResearchX"	git clone https://github.com/xingguangYan/ResearchX.git
Codex (OpenAI)	codex plugin add researchx@personal	Auto-discovers via triggers
Claude Desktop	MCP: researchx-mcp	npx researchx-mcp
Claude Code	Copy CLAUDE.md to project root	Auto-reads CLAUDE.md
npm Registry	npm search researchx	npm install researchx-mcp
PyPI	pip search researchx-mcp	pip install researchx-mcp
GPT Store	Search "ResearchX"	GPT config in mcp-server/
Cline / Roo Code	MCP: researchx-mcp	Copy .clinerules
Continue.dev	MCP: researchx-mcp	Copy .continuerules
Cursor	Copy .cursorrules	Auto-reads
Windsurf	Copy .windsurfrules	Auto-reads

GitHub Discovery

Search GitHub for: ResearchX or ai research operating system

MCP Discovery

Any MCP client can use ResearchX:

{
  "mcpServers": {
    "researchx": {
      "command": "npx",
      "args": ["researchx-mcp"]
    }
  }
}

For full instructions: ResearchX/DISCOVERY.md and ResearchX/platforms/PLATFORMS.md.

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📦 Installation

Method 1: Direct Copy (Local)

# From this repo
Copy-Item -Recurse "ResearchX" "$env:USERPROFILE\.codex\skills\ResearchX"

Method 2: GitHub Clone

git clone https://github.com/xingguangYan/ResearchX.git
Copy-Item -Recurse "ResearchX/ResearchX" "$env:USERPROFILE\.codex\skills\ResearchX"

Method 3: Symlink (for development)

# Windows (Admin PowerShell)
New-Item -ItemType Junction -Path "$env:USERPROFILE\.codex\skills\ResearchX" `
  -Target "C:\path\to\ResearchX\ResearchX"

Verify Installation

# Check the skill is recognized
Get-ChildItem "$env:USERPROFILE\.codex\skills\ResearchX"
# Should show: SKILL.md, agents/, scripts/, references/, assets/

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🌐 Multi-Platform Support

ResearchX works across all major AI agent platforms:

Platform	Config File	Installation	Auto-Discovery
Codex (OpenAI)	ResearchX/SKILL.md	~/.codex/skills/ResearchX	Trigger terms
Claude Code	platforms/CLAUDE.md	Copy to project root	Auto-reads CLAUDE.md
GitHub Copilot	AGENTS.md	Repo root	Auto-reads AGENTS.md
Cursor	platforms/.cursorrules	Copy to project root	Auto-reads .cursorrules
Cline / Roo Code	platforms/.clinerules	Copy to project root	Auto-reads .clinerules
Continue.dev	platforms/.continuerules	Copy to project root	Auto-reads .continuerules
Windsurf	platforms/.windsurfrules	Copy to project root	Auto-reads .windsurfrules
OpenAI GPTs	platforms/mcp.json	GPT Builder config	Manual
MCP Clients	platforms/mcp.json	MCP server config	Manual

Quick Install

git clone https://github.com/xingguangYan/ResearchX.git
cd ResearchX

# Codex (PowerShell):
Copy-Item -Recurse "ResearchX" "$env:USERPROFILE\.codex\skills\ResearchX"

# Claude/Cursor/Cline/Continue/Windsurf (Bash):
cp platforms/CLAUDE.md ./CLAUDE.md
cp platforms/.cursorrules ./.cursorrules
cp platforms/.clinerules ./.clinerules
cp platforms/.continuerules ./.continuerules
cp platforms/.windsurfrules ./.windsurfrules

For full instructions, see ResearchX/platforms/PLATFORMS.md.

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📂 Output Artifacts

Each module saves structured files to the working directory:

File Pattern	Module	Contains
analysis_[Paper].md	§1 Paper Analysis	Full paper breakdown
topics_[Topic]_*.md	§2 Gap Mining	5 topic proposals
methods_[Topic]_*.md	§3 Method Mining	Method landscape
manuscript_[Topic]_*.md	§5 Manuscript Writing	Full paper draft
upgrade_plan_[Paper]_*.md	§6 SCI Upgrade	Action plan
review_report.md	§7 Peer Review	Review + response
grant_proposal_[Topic]_*.md	§10 Grant Proposal	Full proposal
literature_matrix_[Topic]_*.md	§9 Lit Review	Matrix + related work

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❓ FAQ

Q: Does ResearchX work for my field?

A: Yes. ResearchX is domain-agnostic. It searches literature for ANY scientific topic. The method mining protocol is the same whether you study remote sensing, medicine, materials science, or ecology.

Q: What if web_search returns no results?

A: ResearchX detects this and falls back to first-principles reasoning, clearly stating the limitation. It will ask you to verify assumptions.

Q: Can I use it without Python?

A: Yes. The Python scripts are helper tools for analysis. The core SKILL.md works with web_search alone. The scripts provide deeper analysis (method taxonomy, trend scoring).

Q: How is this different from just using ChatGPT?

A: ChatGPT relies on its pre-training knowledge cutoff. ResearchX actively searches for the LATEST papers (2023-2026) and builds method landscapes from real literature. It also chains multiple research workflows together automatically.

Q: Is it only for SCI papers?

A: No. While the writing templates target SCI journals, the gap mining, method discovery, and experiment design modules work for ANY research output (conference papers, theses, technical reports).

Q: Can it read my PDF?

A: If you upload a PDF in Codex, ResearchX will analyze it using the Paper Analysis Engine. It extracts metadata, logic chain, and compares with latest literature.

Q: How do I update the skill?

A: Pull the latest from this repo and copy the folder again:

git pull
Copy-Item -Recurse "ResearchX" "$env:USERPROFILE\.codex\skills\ResearchX"

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🤝 Contributing

ResearchX aims to be the world's best AI research tool. Contributions are welcome!

Ideas for Contribution

• New module: Data visualization, statistical analysis, code generation
• Writing templates: More journal styles (ACM, IEEE, Nature family)
• Domain-specific protocols: Medicine (clinical trial design), Chemistry (lab protocols)
• Scripts: Citation analysis, network graphs, topic modeling
• Translations: Multi-language support for non-English researchers

How to Contribute

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

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📄 License

MIT License — free to use, modify, and distribute.
Built for the global research community.

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ResearchX — Moving research assistance from "what I know" to "what the field knows."