claude-skill

local-deep-research – Accelerate scientific answers with any LLM

Leverage 10+ academic search engines and private docs, all locally encrypted, to power RAG for founders building research‑heavy AI products. 9.2k+ GitHub stars.

From the repo · “ ~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.”

advanced⏱ 30 minutes💵 Free (self-hosted)
9,395 stars+143★ this week882 forksPythonGuide quality 9/10 · reviewed Oct 14Updated 10/23/2026100% free · open source
What it is

Asks an AI to find answers or data for you from the internet or your own computer files.

Real use case example

"A founder named Emma wants to understand a new market trend. She asks the AI to search across academic papers and news articles. After answering a few prompts, the AI gives Emma a concise summary of the trend and the most relevant supporting data. Emma can now use this information to inform her business strategy."

Best insideClaude Desktop
When to use it
  • •You need to conduct research on sensitive or private documents without relying on cloud services
  • •You want to utilize multiple search engines and databases, including local and cloud-based LLMs, in one platform
  • •You require a high level of encryption and security for your research data and results
Ready-to-paste prompt
python app.py --query 'climate change mitigation' --search-engines arXiv,PubMed --private-docs /path/to/private/documents
Heads up: Make sure you have the necessary dependencies, including Python 3.8 or later and a compatible GPU, as the tool relies on GPU acceleration for optimal performance
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

⚡ Runs out of the box

A prompt/skill package — nothing to install beyond adding it to your AI tool.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/local-deep-research && curl -fsSL https://workflowstacks.com/api/skills/local-deep-research/claude-skill -o ~/.claude/skills/local-deep-research/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How local-deep-research – Accelerate scientific answers with any LLM works
Codeflow
Free to inspect

Local-deep-research – Accelerate scientific answers with any LLM is a very large Python project (~300k lines across 804 code files, plus 2488 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.

Size
Very large codebase
~300k lines · 804 code files · days to read — use, don't read
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Python · Node.js · Docker
No API keys detected
Python 80%JavaScript 17%HTML 2%CSS 1%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    package.json
    Dependencies and the commands it exposes
  3. 3
    src/local_deep_research/__init__.py
    Inside src/ — the main logic begins here
  4. 4
    examples/2008-finicial-crisis.md
    A worked example — copy this to get going
What's in each folder
src/Core code — the actual logic821 files
docs/Documentation102 files
examples/Examples you can copy65 files
changelog.d/Folder48 files
scripts/Helper scripts23 files
unraid-templates/Folder1 files
READMEHas testsDocumentedCI checksDocker readyExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
LearningCircuit
Language
Python
Category
claude-skill
Published
2/9/2025

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