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Agent skill

Local Deep Research Guide

by wentorai in wentorai/research-plugins

Deep research agent searching 10+ sources with local or cloud LLMs

MITAuto-check passedResearch & Science

Install Local Deep Research Guide

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

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

GitHub CLI
$ gh skill install wentorai/research-plugins local-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/local-deep-research-guide .claude/skills/local-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
local-deep-research-guide
GitHub stars
298
Used in
1 other repo
Token cost
~2k tokens
SKILL.md length
442 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

Deep research agent searching 10+ sources with local or cloud LLMs

  • Tasks that involve Deep research
  • SKILL.md covers Overview, Installation and Setup, Core Research Capabilities and Web Interface, plus 2 more sections
  • Calls pip, ollama and git; reaches github.com and ollama.com; needs OPENAI_API_KEY and ANTHROPIC_API_KEY
  • Tasks that involve Academic paper search

What it does

Local Deep Research Guide is an agent skill from wentorai/research-plugins. Deep research agent searching 10+ sources with local or cloud LLMs

Its SKILL.md is about 2k 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 and Academic paper search. 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
  • Tasks that involve Academic paper search

Example prompts

  • “/local-deep-research-guide”

Requirements

  • Python 3
  • Docker
  • A credential in OPENAI_API_KEY
  • A credential in ANTHROPIC_API_KEY

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
    • ollama
    • git
    • docker

    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
    • ollama.com

    Also links to:

    • api.openalex.org
    • info.arxiv.org

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

  • Credentials

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

    • OPENAI_API_KEY
    • ANTHROPIC_API_KEY
    • SERPER_API_KEY
    • TAVILY_API_KEY
    • SEMANTIC_SCHOLAR_API_KEY

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

Context cost

Local Deep Research Guide loads about 2k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 442 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
~2k

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). 442 words, ~2,012 tokens.

Download SKILL.mdSave it as .claude/skills/local-deep-research-guide/SKILL.md (or your agent's skills folder).
name
local-deep-research-guide
description
Deep research agent searching 10+ sources with local or cloud LLMs

Local Deep Research Guide

Overview

Local Deep Research is an open-source deep research tool with over 4,000 GitHub stars that conducts comprehensive multi-source research using either local LLMs (via Ollama, LM Studio, or vLLM) or cloud-based models. It searches across 10+ academic and web sources simultaneously, synthesizes the findings, and produces well-cited research reports. The project is designed for researchers who need thorough, multi-perspective research coverage while maintaining the option to keep everything running locally for privacy.

What makes Local Deep Research stand out is its breadth of search integration. Rather than relying on a single search API, it queries multiple sources in parallel -- including Google Scholar, OpenAlex, arXiv, PubMed, Wikipedia, web search engines, and more -- then cross-references and synthesizes the results. This multi-source approach produces more comprehensive and balanced research outputs compared to single-source tools.

The tool is particularly well-suited for academic researchers who need to conduct preliminary literature reviews, verify claims across multiple databases, or explore interdisciplinary topics where relevant work may be scattered across different platforms and publication venues.

Installation and Setup

bash
# Install from PyPI
pip install local-deep-research

# Or clone for development
git clone https://github.com/LearningCircuit/local-deep-research.git
cd local-deep-research
pip install -e .
LLM Backend Configuration

Local Deep Research supports multiple LLM backends. Choose the one that fits your privacy and performance requirements:

bash
# Option 1: Local LLM via Ollama (fully private)
# First, install Ollama: https://ollama.com/
ollama pull llama3.1:70b
export LDR_LLM_PROVIDER=ollama
export LDR_LLM_MODEL=llama3.1:70b

# Option 2: Local LLM via LM Studio
export LDR_LLM_PROVIDER=lmstudio
export LDR_LLM_BASE_URL=http://localhost:1234/v1

# Option 3: Cloud LLM (OpenAI)
export LDR_LLM_PROVIDER=openai
export OPENAI_API_KEY=$OPENAI_API_KEY
export LDR_LLM_MODEL=gpt-4o

# Option 4: Cloud LLM (Anthropic)
export LDR_LLM_PROVIDER=anthropic
export ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY
export LDR_LLM_MODEL=claude-sonnet-4-20250514
Search Source Configuration

Configure which search sources to use:

bash
# Web search (at least one required)
export SERPER_API_KEY=$SERPER_API_KEY
# Or
export TAVILY_API_KEY=$TAVILY_API_KEY
# Or
export SEARX_URL=http://localhost:8888  # Self-hosted SearXNG

# Academic sources (optional, enhances academic research)
export SEMANTIC_SCHOLAR_API_KEY=$SEMANTIC_SCHOLAR_API_KEY
# PubMed and arXiv require no API keys

Core Research Capabilities

Running a Research Query

Start a research session from the command line or Python API:

bash
# Command-line interface
local-deep-research "What are the most effective methods for \
  few-shot learning in NLP as of 2024?"
python
# Python API
from local_deep_research import DeepResearcher

researcher = DeepResearcher(
    llm_provider="ollama",
    llm_model="llama3.1:70b",
    search_sources=["google_scholar", "openalex",
                    "arxiv", "web"],
    max_iterations=10,
)

result = researcher.research(
    "What are the most effective methods for few-shot learning "
    "in NLP as of 2024?"
)

print(result.report)
Multi-Source Search Engine

Local Deep Research queries multiple sources in parallel for each research sub-question:

SourceTypeAPI Key RequiredBest For
Google ScholarAcademicNo (via scraping)Broad academic search
OpenAlexAcademicNoCross-disciplinary, citation data
arXivAcademicNoPreprints, ML/physics/math
PubMedAcademicNoBiomedical literature
WikipediaEncyclopediaNoBackground and definitions
Web SearchGeneralYes (Serper/Tavily)Recent developments
SearXNGMeta-searchSelf-hostedPrivacy-focused web search
CrossRefAcademicNoDOI resolution, metadata
COREAcademicOptionalOpen access papers
UnpaywallAcademicNoOpen access PDF links
python
# Customize source priorities for your research domain
researcher = DeepResearcher(
    search_sources={
        "primary": ["openalex", "arxiv"],
        "secondary": ["google_scholar", "web"],
        "reference": ["wikipedia", "crossref"],
    },
    source_weights={
        "openalex": 1.5,  # Prioritize academic sources
        "arxiv": 1.5,
        "web": 0.8,
    },
)
Show full SKILL.md (135 more words)Show less
Research Report Generation

The research pipeline produces structured reports with proper citations:

python
result = researcher.research(
    "Compare reinforcement learning from human feedback (RLHF) "
    "with direct preference optimization (DPO) for LLM alignment"
)

# The report includes:
# - Executive summary
# - Detailed findings organized by sub-topic
# - Inline citations with source URLs
# - Source bibliography
# - Confidence assessment for each claim

# Save the report
result.save_markdown("rlhf_vs_dpo_report.md")
result.save_html("rlhf_vs_dpo_report.html")

Web Interface

Local Deep Research includes a built-in web interface for interactive research sessions:

bash
# Start the web UI
local-deep-research --ui

# Or specify host and port
local-deep-research --ui --host 0.0.0.0 --port 5000

The web interface provides:

  • Interactive research sessions: Submit queries and watch the research process in real-time
  • Source inspection: Click through to original sources for each finding
  • Research history: Browse and re-examine previous research sessions
  • Report export: Download reports in markdown, HTML, or PDF format
  • Configuration panel: Adjust LLM and search settings without editing config files

Advanced Research Workflows

Iterative Research with Follow-Up Questions

Build on previous research with follow-up queries:

python
# Initial research
result1 = researcher.research(
    "Overview of graph neural networks for molecular property prediction"
)

# Follow-up that builds on context from the first query
result2 = researcher.follow_up(
    "Which of these approaches handle 3D molecular geometry?",
    context=result1,
)
Batch Research

Run multiple research queries in batch for systematic investigations:

python
queries = [
    "Attention mechanisms in protein structure prediction",
    "Graph neural networks for drug-target interaction",
    "Transfer learning approaches in computational chemistry",
    "Benchmarks for molecular property prediction models",
]

results = researcher.batch_research(
    queries,
    parallel=True,
    max_workers=4,
)

# Generate a comparative summary across all queries
summary = researcher.synthesize(results)
Fully Private Research Pipeline

For maximum privacy, run everything locally with no external API calls:

bash
# Use Ollama for LLM
ollama pull llama3.1:70b

# Use SearXNG for search (self-hosted)
docker run -d --name searxng -p 8888:8080 searxng/searxng

# Configure Local Deep Research
export LDR_LLM_PROVIDER=ollama
export LDR_LLM_MODEL=llama3.1:70b
export SEARX_URL=http://localhost:8888
export LDR_SEARCH_SOURCES=searxng,arxiv,pubmed,wikipedia

# All queries now stay on your local machine
local-deep-research "Your sensitive research query here"

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/local-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

Local 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.

Local Deep Research Guide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Local Deep Research Guide this skillwentorai/research-plugins2981 repos~2kAutomated safety check: PassMIT
Scientific Writingneflibata-feng/MyArxiv-Agent12618 repos~8.4kAutomated safety check: NotesMIT
Paper Expert Generatorguhaohao0991/PaperClaw250—~2kAutomated safety check: PassNone
Rival Search MCPdamionrashford/RivalSearchMCP132—~796Automated safety check: PassMIT
Deep Research Literature SurveyHKUSTDial/Supervisor-Skills8.8k—~2.4kAutomated safety check: PassCC-BY-NC-SA-4.0
Argo Search and Verificationtaxueseek/argo188—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Local Deep Research Guide

What does Local Deep Research Guide do?

Deep research agent searching 10+ sources with local or cloud LLMs. Local Deep Research Guide is an agent skill from wentorai/research-plugins.

When should I use Local Deep Research Guide?

Local Deep Research Guide fits situations like: tasks that involve Deep research; tasks that involve Academic paper search.

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

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

How do I install Local Deep Research Guide in Codex?

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

Can I use Local 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 local-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/local-deep-research-guide, .gemini/skills/local-deep-research-guide, .github/skills/local-deep-research-guide and .opencode/skills/local-deep-research-guide in your project.

What does Local Deep Research Guide need to run?

Going by SKILL.md and its folder, Local Deep Research Guide needs the command-line tools its instructions call (pip, ollama, git and docker) and credentials named OPENAI_API_KEY, ANTHROPIC_API_KEY, SERPER_API_KEY and TAVILY_API_KEY. Our summary lists: Python 3; Docker; A credential in OPENAI_API_KEY; A credential in ANTHROPIC_API_KEY.

Does Local Deep Research Guide access the network?

SKILL.md names 4 domains. In commands or code: github.com and ollama.com; the agent is likely to contact these when it follows the instructions. As links in the text: api.openalex.org and info.arxiv.org. This is read from the text; nothing was executed.

Is Local 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 Local Deep Research Guide use?

Local 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 Local Deep Research Guide use?

About 2k tokens (SKILL.md is roughly 8k 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 Local Deep Research Guide?

Skills that share tags, products or a category with Local Deep Research Guide: Scientific Writing (neflibata-feng/MyArxiv-Agent, 126 stars), Paper Expert Generator (guhaohao0991/PaperClaw, 250 stars), Rival Search MCP (damionrashford/RivalSearchMCP, 132 stars) and Deep Research Literature Survey (HKUSTDial/Supervisor-Skills, 8.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Local 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.