Deep Research Agent

Deep Science Writer is an industrial-grade, end-to-end scientific research pipeline and AI agent skill. Designed for the Hermes/ECC framework, it completely automates the literature review process: from background full-text database querying down to anti-hallucination verification, Nature/Science-level peer review, .docx compilation, and Obsidian/NotebookLM knowledge base ingestion.

🌟 About

An autonomous AI agent pipeline for rigorous academic research, featuring strict DOI verification, multi-agent Scopus/OpenAlex/Semantic Scholar retrieval, and APA 7th .docx generation.

🌟 Key Features

• Precision Background Sourcing (Two-Stage Pipeline): Subagents first fetch and screen a large pool of abstracts (e.g., 100+). For the highly relevant subset, the agent downloads and deeply reads the full text (Methodology, Results) to prevent abstract-induced hallucination. FULL-TEXT verification of final claims is absolutely mandatory.
• Strict Quality Control: Explicitly targets Q1-Q2 journals. Marks Q3 when strictly necessary. Bans all Q4 and MDPI publications.
• Multi-Agent Discovery: Spawns exactly three concurrent subagents to query Scopus, Exa, OpenAlex, and Semantic Scholar simultaneously for exhaustive mapping.
• Strict Compliance: The agent is hard-coded to strictly follow every step in order. Skipping phases or taking shortcuts (like drafting without full-text verification) is strictly forbidden.
• Zero-Hallucination Guarantee (Phase 4.5): Automatically runs live HTTP requests tests against every generated DOI to ensure 100% validity. Cross-references generated claims against raw full texts to prevent AI overstatement.
• Automated Peer Review: Integrates the remi peer-review skill (named in tribute to my academic advisor, Remi Chauvy) to aggressively strip "AI fluff" (e.g., "delve", "tapestry") and enforce rigorous academic tone.
• English Output Only: Enforces strict English language generation for all academic reports and drafts, regardless of conversational language.
• Hands-Free Output: Programmatically builds a fully formatted Microsoft Word document (.docx) with APA 7th hanging indents and auto-generated data visualizations.
• Knowledge Management Loop: Automatically saves research summaries to your local Obsidian Vault and explicitly uploads every individually cited reference as a separate source into Google NotebookLM for precise audio overviews and cross-referencing.

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📋 Prerequisites

To run this skill successfully, your host environment must be configured with the following dependencies and Model Context Protocol (MCP) servers:

1. System Requirements

• Hermes Agent (or a compatible ECC/Claude Code runner).
• Node.js (v18+ recommended) and npx for running MCP servers.
• Python 3.10+ (in your system PATH or agent's virtual environment).

2. Python Dependencies

The agent relies on Python to generate documents and verify links.

pip install python-docx PyMuPDF requests matplotlib seaborn pandas

3. Required MCP Servers

Add these to your config.yaml or claude_desktop_config.json:

• Scopus MCP (scopus-mcp)
  • Required for premium literature retrieval.
  • API Key: Requires a free Elsevier Scopus API Key (SCOPUS_API_KEY). Apply at the Elsevier Developer Portal.
• NotebookLM MCP (notebooklm-mcp-server)
  • Required for Phase 7 knowledge ingestion.
  • Auth: Must run npx notebooklm-mcp-server auth once in your terminal to authenticate your Google Account locally.
• Exa Search MCP (Highly Recommended)
  • Used for neural search fallback and broad open-access discovery.
• GitHub MCP & Playwright MCP (Optional but recommended for broader functionality).

4. Local Environment

• Obsidian: The skill looks for %OBSIDIAN_VAULT_PATH%\Hermes\ (fallback: %USERPROFILE%\Documents\Obsidian Vault\Hermes\). Update SKILL.md Phase 7 or set the environment variable if your vault is located elsewhere.
• Output Drive: All outputs strictly save to D:\Tommy (or your configured environment).

5. Network Environment (Important)

• University/Academic Network Recommended: For Phase 4.6 (Zotero Archiving & Full-Text PDF Download), the agent utilizes cloakbrowser to bypass Cloudflare and publisher paywalls (e.g., Wiley, Elsevier). Connecting via a university or academic institution's network is highly recommended as it provides native IP-based authorization to access these restricted databases, ensuring a 100% success rate for automated PDF extraction.

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🚀 Installation

Clone this repository into your agent's skills directory:

cd <AGENT_SKILLS_DIR>
git clone https://github.com/CYC2002tommy/Deep-Research-Agent.git

(Replace <AGENT_SKILLS_DIR> with your agent's skills path, e.g., ~/.hermes/skills/ or .agents/skills/ for the ECC framework).

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🧠 The 7-Phase Architecture

1. Phase 0 and 0.5 (Plan & Background Execution): The agent formulates a search plan and halts for your explicit approval. Once approved, it launches a local background process (terminal(background=true)) to scrape the full text of exactly 30 high-impact papers via APIs.
2. Phase 1 (Strict Multi-Agent Discovery): Deploys exactly three concurrent subagents utilizing scopus-mcp, exa-search, OpenAlex, and Semantic Scholar. Enforces journal quality limits (Q1-Q2 only, bans MDPI).
3. Phase 2 (Deep Extraction): Consolidates metadata, full texts, and key findings.
4. Phase 3 (Structural Drafting): Outlines the article with evidence-backed claims and APA 7th citations.
5. Phase 4 and 4.5 (Anti-Hallucination): Strips AI vocabulary. Pings all DOIs to ensure they resolve (404 = citation deleted).
6. Phase 5 (Remi Review): An internal peer-review loop that critiques and rewrites the draft until academic standards are met. (Named in tribute to my academic advisor, Remi Chauvy).
7. Phase 6 (Compilation): Python scripts draw Mermaid/Matplotlib charts and compile the final .docx (ONLY allowed after Phase 4, 4.5, and 5 are passed).
8. Phase 7 (Knowledge Graph): Updates Obsidian and pushes papers to NotebookLM.

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💻 Usage Example

Simply trigger the agent with a research prompt:

"Please use the deep-science-writer skill to research the sociological and psychological acceptance of sustainability policies, segmented by age (public) and firm size (SMEs vs Large Enterprises)."

The agent will take over, present a blueprint, ask for your approval, and then execute the entire pipeline autonomously.

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⚖️ Academic Ethics & AI Usage Disclaimer

While Deep Science Writer implements rigorous anti-hallucination checks, DOI verification, and strict Q1/Q2 filtering, it remains an AI assistant, not an author. By using this tool, you agree to the following principles of academic integrity:

1. Human Accountability: The human researcher assumes 100% responsibility for the final manuscript's accuracy, scientific validity, and originality. AI-generated syntheses must be critically reviewed, not blindly accepted.
2. Evidence Verification: Though the agent downloads and parses full-text PDFs to prevent hallucination, you must personally verify that the cited literature supports the claims in the exact context you intend. Do not rely solely on the agent's interpretation.
3. Transparency & Disclosure: You must comply with your target journal's or institution's policies regarding AI usage. Transparently declare the use of AI tools (e.g., Hermes Agent, LLMs) in the methodology or acknowledgments if required by the publisher.
4. Originality over Automation: Using AI is not a violation of ethics if used correctly as a scaffolding tool. True research requires your unique insights, critical thinking, and intellectual contribution. This tool is designed to eliminate tedious formatting and literature gathering, not to bypass the core cognitive work of science.

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License: MIT