Agent skill

Khoj Research Guide

by wentorai in wentorai/research-plugins

AI second brain for deep research and personal knowledge management

MITAuto-check passedResearch & Science

Install Khoj Research Guide

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

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

GitHub CLI
$ gh skill install wentorai/research-plugins khoj-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/khoj-research-guide .claude/skills/khoj-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
khoj-research-guide
GitHub stars
298
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
564 words
Files
1
Skills in repo
405
Repo updated
First seen
Licence
MIT

At a glance

AI second brain for deep research and personal knowledge management

  • Works in 3 steps: Content sources: Point Khoj to your… → LLM configuration: Set up your preferred… → Search settings: Configure web search…
  • Tasks that involve Deep research
  • SKILL.md covers Overview, Installation and Setup, Core Research Features and Advanced Research Workflows, plus 2 more sections
  • Calls docker and pip; needs OPENAI_API_KEY

What it does

Khoj Research Guide is an agent skill from wentorai/research-plugins. AI second brain for deep research and personal knowledge management

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 and Second brain. 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 Second brain

Example prompts

  • “/khoj-research-guide”

Requirements

  • Python 3
  • Docker
  • A credential in OPENAI_API_KEY

Workflow steps

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

  1. Content sources: Point Khoj to your document directories
  2. LLM configuration: Set up your preferred language model backend
  3. Search settings: Configure web search and document search parameters

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:

    • docker
    • pip

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

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com
    • docs.khoj.dev

    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

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

Context cost

Khoj Research Guide loads about 1.9k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 564 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
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). 564 words, ~1,869 tokens.

Download SKILL.mdSave it as .claude/skills/khoj-research-guide/SKILL.md (or your agent's skills folder).
name
khoj-research-guide
description
AI second brain for deep research and personal knowledge management

Khoj Research Guide

Overview

Khoj is an open-source AI personal research assistant with over 33,000 GitHub stars that acts as a second brain for researchers, students, and knowledge workers. It can search and chat with your personal notes, documents, and the web to help you find information, synthesize knowledge, and conduct deep research. Khoj combines personal knowledge management with AI-powered research capabilities, making it a unique tool for academic researchers who need to work with large collections of papers, notes, and data.

Unlike general-purpose AI assistants, Khoj is designed to work with your own data. It indexes your documents -- including PDFs, markdown files, org-mode notes, plaintext, and images -- and provides an AI interface that can reason over this personal knowledge base alongside web search results. This means you can ask questions that require combining information from your personal research notes with the latest findings from the web.

Khoj supports both cloud-hosted and fully self-hosted deployments. The self-hosted option is particularly attractive for researchers working with sensitive data, unpublished manuscripts, or proprietary datasets that cannot be sent to third-party services. It supports multiple LLM backends including OpenAI, Anthropic, and local models via Ollama.

Installation and Setup

bash
# Using Docker (recommended)
docker run -d \
  --name khoj \
  -p 42110:42110 \
  -v ~/.khoj:/root/.khoj \
  ghcr.io/khoj-ai/khoj:latest

# Or using pip
pip install khoj

# Start the server
khoj --host 0.0.0.0 --port 42110
Configuration

After starting Khoj, configure it through the web interface at http://localhost:42110/config:

  1. Content sources: Point Khoj to your document directories
  2. LLM configuration: Set up your preferred language model backend
  3. Search settings: Configure web search and document search parameters
bash
# Set environment variables for LLM access
export OPENAI_API_KEY=$OPENAI_API_KEY
# Or for local models
export OLLAMA_HOST=http://localhost:11434
Client Integrations

Khoj provides clients for multiple platforms to integrate into your existing workflow:

  • Web interface: Full-featured browser UI at http://localhost:42110
  • Obsidian plugin: Search and chat from within Obsidian
  • Emacs package: Native integration for Emacs/org-mode users
  • Desktop app: Cross-platform Electron app
  • WhatsApp/Telegram: Chat with Khoj via messaging apps
bash
# Install the Obsidian plugin
# In Obsidian: Settings > Community Plugins > Search "Khoj"
# Configure server URL: http://localhost:42110

Core Research Features

Khoj indexes your research documents and makes them searchable using semantic search:

python
# Supported document types
# - PDF files (research papers, textbooks)
# - Markdown files (notes, drafts)
# - Org-mode files (structured notes)
# - Plaintext files (data, logs)
# - Images (diagrams, figures)
# - GitHub repositories (code, documentation)
# - Notion pages (collaborative notes)

# Configure content sources via the web UI or API
import requests

# Add a document directory
requests.post("http://localhost:42110/api/config/data/source", json={
    "type": "folder",
    "path": "/path/to/research/papers",
    "file_types": ["pdf", "md"],
    "recursive": True,
})
Deep Research Mode

Khoj includes a dedicated research mode that goes beyond simple question-answering. It iteratively searches, reads, and synthesizes information from both your personal knowledge base and the web:

python
# Trigger deep research via the API
response = requests.post("http://localhost:42110/api/chat", json={
    "q": "Synthesize the key findings from my notes on transformer "
         "efficiency and relate them to recent papers on sparse attention",
    "research_mode": True,
    "max_iterations": 8,
})

# The response includes:
# - Synthesized answer drawing from personal notes and web sources
# - Citations to specific documents and web pages
# - Follow-up questions for further exploration
Show full SKILL.md (226 more words)Show less
Conversational Research

Chat with Khoj about your research, and it will draw on your indexed documents:

User: What are the main arguments in the papers I've saved about
      few-shot learning?

Khoj: Based on your indexed papers, I found 7 documents related to
      few-shot learning. The main arguments include:
      1. [From paper_x.pdf] Meta-learning approaches...
      2. [From notes/ml-review.md] Prototypical networks...
      ...
Automated Research Agents

Khoj supports automated agents that can perform scheduled research tasks:

python
# Create a research agent that monitors new papers
requests.post("http://localhost:42110/api/agents", json={
    "name": "paper-monitor",
    "schedule": "daily",
    "task": "Search for new papers on 'graph neural networks for "
            "molecular property prediction' published in the last "
            "24 hours and summarize the key findings",
    "notify": True,
})

Advanced Research Workflows

Literature Review Pipeline

Use Khoj to build a structured literature review:

  1. Collect: Index your downloaded papers and notes in Khoj
  2. Explore: Ask broad questions to understand the landscape
  3. Synthesize: Request comparative analyses across papers
  4. Identify gaps: Ask Khoj to find areas where your collection lacks coverage
  5. Expand: Use web search to find additional papers on identified gaps
Knowledge Graph Building

Khoj maintains connections between your documents, allowing you to discover relationships:

User: What connections exist between my notes on attention mechanisms
      and my notes on computational efficiency?

Khoj: I found several connections:
      - 3 papers discuss efficient attention variants
      - Your notes from 2024-03 mention linear attention
      - The survey paper you saved covers both topics in Section 4
Multi-Modal Research

Khoj can index and reason about images alongside text, which is useful for researchers working with figures, diagrams, and visual data:

  • Index experiment result plots and ask questions about trends
  • Upload architecture diagrams and ask for explanations
  • Search for specific visual patterns across your document collection

Privacy and Data Sovereignty

For researchers handling sensitive or unpublished data, Khoj offers strong privacy guarantees in self-hosted mode:

  • All data stays on your local machine or institutional server
  • No telemetry or data sharing with third parties
  • Compatible with fully local LLMs via Ollama (no external API calls)
  • Encrypted storage for indexed documents
  • Fine-grained access controls for multi-user deployments

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/khoj-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

Khoj 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.

Khoj Research Guide compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Khoj Research Guide this skillwentorai/research-plugins2981 repos~1.9kAutomated safety check: PassMIT
Bounded AutoresearchAgriciDaniel/claude-obsidian15k—~1.6kAutomated safety check: PassMIT
Personal Cfocoreyhaines31/makerskills851—~2.3kAutomated safety check: PassMIT
Radarcoreyhaines31/makerskills851—~4kAutomated safety check: WarnMIT
Karpathy WikiSherwinQ/karpathy-wiki114—~967Automated safety check: PassMIT
Second Brain Reportundefined-ui/second-brain-os1k—~474Automated safety check: PassMIT

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Questions about Khoj Research Guide

What does Khoj Research Guide do?

AI second brain for deep research and personal knowledge management. Khoj Research Guide is an agent skill from wentorai/research-plugins.

When should I use Khoj Research Guide?

Khoj Research Guide fits situations like: tasks that involve Deep research; tasks that involve Second brain.

How do I install Khoj Research Guide in Claude Code?

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

How do I install Khoj Research Guide in Codex?

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

Can I use Khoj 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 khoj-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/khoj-research-guide, .gemini/skills/khoj-research-guide, .github/skills/khoj-research-guide and .opencode/skills/khoj-research-guide in your project.

What does Khoj Research Guide need to run?

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

Does Khoj Research Guide access the network?

SKILL.md names 2 domains. As links in the text: github.com and docs.khoj.dev. This is read from the text; nothing was executed.

Is Khoj 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 Khoj Research Guide use?

Khoj 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 Khoj Research Guide use?

About 1.9k tokens (SKILL.md is roughly 7.5k 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 Khoj Research Guide?

Skills that share tags, products or a category with Khoj Research Guide: Bounded Autoresearch (AgriciDaniel/claude-obsidian, 15k stars), Personal Cfo (coreyhaines31/makerskills, 851 stars), Radar (coreyhaines31/makerskills, 851 stars) and Karpathy Wiki (SherwinQ/karpathy-wiki, 114 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Khoj 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.