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Agent skill

Tongyi Deep Research Guide

by wentorai in wentorai/research-plugins

Open-source deep research agent by Alibaba for scholarly research

MITAuto-check passedResearch & Science

Install Tongyi Deep Research Guide

skills CLI
$ npx skills add wentorai/research-plugins --skill tongyi-deep-research-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install wentorai/research-plugins tongyi-deep-research-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/deep-research/tongyi-deep-research-guide .claude/skills/tongyi-deep-research-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tongyi-deep-research-guide
GitHub stars
298
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
473 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Open-source deep research agent by Alibaba for scholarly research

  • Works in 5 steps: Think: Analyze the research question and… → Search: Formulate search queries and… → Read: Extract and comprehend key… → …
  • Tasks that involve Deep research
  • SKILL.md covers Overview, Installation and Setup, Core Research Pipeline and Advanced Features, plus 2 more sections
  • Calls pip, conda and git; reaches github.com; needs LLM_API_KEY and SEARCH_API_KEY

What it does

Tongyi Deep Research Guide is an agent skill from wentorai/research-plugins. Open-source deep research agent by Alibaba for scholarly research

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Deep research. The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Deep research

Example prompts

  • “/tongyi-deep-research-guide”

Requirements

  • Python 3
  • A credential in LLM_API_KEY
  • A credential in SEARCH_API_KEY

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Think: Analyze the research question and identify what information is needed
  2. Search: Formulate search queries and retrieve relevant documents
  3. Read: Extract and comprehend key information from retrieved documents
  4. Reflect: Evaluate whether enough information has been gathered or if further research is needed
  5. Synthesize: Compile findings into a structured, cited report

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • conda
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • api.openalex.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LLM_API_KEY
    • SEARCH_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tongyi Deep Research Guide loads about 1.9k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 473 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 473 words, ~1,855 tokens.

Download SKILL.mdSave it as .claude/skills/tongyi-deep-research-guide/SKILL.md (or your agent's skills folder).
name
tongyi-deep-research-guide
description
Open-source deep research agent by Alibaba for scholarly research

Tongyi Deep Research Guide

Overview

Tongyi DeepResearch is an open-source deep research agent developed by Alibaba's NLP team, with over 18,000 stars on GitHub. It implements an agentic research pipeline that iteratively searches, reads, reasons, and synthesizes information to produce comprehensive research reports. The system is designed to handle complex, multi-faceted research questions that require gathering evidence from multiple sources and reasoning across diverse information.

Unlike simpler RAG (Retrieval-Augmented Generation) systems that perform a single search-and-answer cycle, DeepResearch uses an iterative approach where the agent dynamically decides what to search next based on what it has already found. This makes it particularly effective for research questions that require building up understanding incrementally, following citation chains, or exploring multiple angles of a topic.

The project is notable for being one of the leading open-source alternatives to proprietary deep research tools. It supports multiple LLM backends, various search APIs, and can be customized for domain-specific research needs. For academic researchers, it offers a transparent and modifiable research pipeline where every step can be inspected, reproduced, and adapted.

Installation and Setup

bash
# Clone the repository
git clone https://github.com/Alibaba-NLP/DeepResearch.git
cd DeepResearch

# Install dependencies
pip install -r requirements.txt

# Or install with conda
conda create -n deepresearch python=3.10
conda activate deepresearch
pip install -r requirements.txt

Configure your environment for the LLM and search backends:

bash
# LLM configuration (supports multiple providers)
export LLM_API_KEY=$LLM_API_KEY
export LLM_BASE_URL=$LLM_BASE_URL
export LLM_MODEL=qwen-max

# Search API configuration
export SEARCH_API_KEY=$SEARCH_API_KEY
export SEARCH_ENGINE=bing  # or google, serper, tavily

For a fully local deployment with Ollama:

bash
# Use local models
export LLM_BASE_URL=http://localhost:11434/v1
export LLM_MODEL=qwen2.5:72b
export LLM_API_KEY=ollama

Core Research Pipeline

The Iterative Research Loop

DeepResearch follows a think-search-read-reflect loop that mimics how a human researcher works:

  1. Think: Analyze the research question and identify what information is needed
  2. Search: Formulate search queries and retrieve relevant documents
  3. Read: Extract and comprehend key information from retrieved documents
  4. Reflect: Evaluate whether enough information has been gathered or if further research is needed
  5. Synthesize: Compile findings into a structured, cited report
python
from deep_research import DeepResearch

# Initialize the research agent
agent = DeepResearch(
    llm_model="qwen-max",
    search_engine="bing",
    max_iterations=10,
    max_sources=30,
)

# Run a research query
result = agent.research(
    query="What are the latest advances in multimodal large language models "
          "and their applications in scientific research?",
    output_format="markdown",
)

print(result.report)
print(f"Sources consulted: {len(result.sources)}")
print(f"Research iterations: {result.iterations}")
Research Configuration

Fine-tune the research behavior for different types of queries:

python
config = {
    "max_iterations": 15,          # Maximum research cycles
    "max_sources_per_query": 10,   # Sources per search query
    "min_relevance_score": 0.7,    # Minimum source relevance threshold
    "enable_citation_tracking": True,  # Follow citation chains
    "language": "en",              # Output language
    "report_length": "detailed",   # brief, standard, or detailed
}

agent = DeepResearch(config=config)
Show full SKILL.md (194 more words)Show less
Supported Search Backends

DeepResearch integrates with multiple search providers to cast a wide net:

  • Bing Search API: General web search with academic content
  • Google Custom Search: Configurable search with domain restrictions
  • Tavily: AI-optimized search API designed for research agents
  • Serper: Fast Google search results API
  • SearXNG: Self-hosted meta-search engine for privacy-focused deployments
  • OpenAlex API: Direct academic paper search (free, no API key required)
python
# Configure multiple search backends for comprehensive coverage
agent = DeepResearch(
    search_engines=["bing", "openalex"],
    search_strategy="parallel",  # Search all engines simultaneously
)

Advanced Features

Citation Chain Following

DeepResearch can follow citation chains to discover related work:

python
result = agent.research(
    query="Foundational papers on attention mechanisms in neural networks",
    enable_citation_tracking=True,
    citation_depth=2,  # Follow citations up to 2 levels deep
)
Domain-Specific Research Profiles

Create research profiles optimized for specific academic domains:

python
# Biomedical research profile
bio_config = {
    "preferred_sources": ["pubmed", "biorxiv", "nature", "science"],
    "search_engines": ["openalex", "bing"],
    "terminology_mode": "technical",
    "citation_format": "apa",
}

agent = DeepResearch(config=bio_config)
result = agent.research(
    "Recent developments in mRNA vaccine delivery mechanisms"
)
Streaming Progress

Monitor the research process in real-time:

python
async def stream_research():
    agent = DeepResearch(llm_model="qwen-max")

    async for event in agent.research_stream(
        query="Quantum computing applications in drug discovery"
    ):
        if event.type == "thinking":
            print(f"Thinking: {event.content}")
        elif event.type == "searching":
            print(f"Searching: {event.query}")
        elif event.type == "reading":
            print(f"Reading: {event.url}")
        elif event.type == "report":
            print(f"Final report:\n{event.content}")

Research Workflow Integration

Combining with Academic Tools

DeepResearch output can be integrated with standard academic tools:

  • Export reports as BibTeX-compatible references for LaTeX papers
  • Feed results into Zotero or Mendeley for reference management
  • Use the structured output as input for systematic review tools
  • Combine with local document collections for comprehensive literature coverage
Reproducibility

Every research session can be fully reproduced:

python
# Save the complete research trace
result = agent.research(query="...", save_trace=True)
result.save_trace("research_trace.json")

# Replay a research session
replayed = DeepResearch.replay("research_trace.json")

The trace includes all search queries, retrieved documents, LLM prompts and responses, and reasoning steps, enabling full transparency and reproducibility of the research process.

References

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/research/deep-research/tongyi-deep-research-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Tongyi Deep Research Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tongyi Deep Research Guide compared with similar skills
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Deep Research WorkflowTokenRhythm/opensquilla7.1k—~1.3kAutomated safety check: PassApache-2.0
Deep Researchsanjay3290/ai-skills4329 repos~683Automated safety check: NotesApache-2.0
Horizontal-Vertical Deep ResearchKKKKhazix/khazix-skills21k—~2.1kAutomated safety check: PassMIT
Academic Research PipelineImbad0202/academic-research-skills51k—~15kAutomated safety check: PassCustom licence

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Categories

Questions about Tongyi Deep Research Guide

What does Tongyi Deep Research Guide do?

Open-source deep research agent by Alibaba for scholarly research. Tongyi Deep Research Guide is an agent skill from wentorai/research-plugins.

When should I use Tongyi Deep Research Guide?

Tongyi Deep Research Guide fits situations like: tasks that involve Deep research.

How do I install Tongyi Deep Research Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill tongyi-deep-research-guide -a claude-code`. Or copy the skill folder (skills/research/deep-research/tongyi-deep-research-guide in wentorai/research-plugins) into .claude/skills/tongyi-deep-research-guide in your project. Claude Code loads it when a task matches its description.

How do I install Tongyi Deep Research Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill tongyi-deep-research-guide -a codex`. Or copy the skill folder (skills/research/deep-research/tongyi-deep-research-guide in wentorai/research-plugins) into .agents/skills/tongyi-deep-research-guide in your project. Codex loads it when a task matches its description.

Can I use Tongyi Deep Research Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill tongyi-deep-research-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tongyi-deep-research-guide, .gemini/skills/tongyi-deep-research-guide, .github/skills/tongyi-deep-research-guide and .opencode/skills/tongyi-deep-research-guide in your project.

What does Tongyi Deep Research Guide need to run?

Going by SKILL.md and its folder, Tongyi Deep Research Guide needs the command-line tools its instructions call (pip, conda and git) and credentials named LLM_API_KEY and SEARCH_API_KEY. Our summary lists: Python 3; A credential in LLM_API_KEY; A credential in SEARCH_API_KEY.

Does Tongyi Deep Research Guide access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: api.openalex.org. This is read from the text; nothing was executed.

Is Tongyi Deep Research Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tongyi Deep Research Guide use?

Tongyi Deep Research Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tongyi Deep Research Guide use?

About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tongyi Deep Research Guide?

Skills that share tags, products or a category with Tongyi Deep Research Guide: GitHub Deep Research (bytedance/deer-flow, 84k stars), Deep Research Workflow (TokenRhythm/opensquilla, 7.1k stars), Deep Research (sanjay3290/ai-skills, 432 stars) and Horizontal-Vertical Deep Research (KKKKhazix/khazix-skills, 21k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tongyi Deep Research Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.