Deeply understand PDF papers, extract key information, critically analyze and generate structured in-depth reading reports, and correlate user research directions.
Data sourced from ClawHub. View on ClawSkills
Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install paper-reader-deepOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install paper-reader-deepβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/paper-reader-deep/π‘Extract and place the folder at the path above, then restart your agent.
Category
πDocuments & NotesPlatforms
What Paper Reader Deep can do for your AI workflow
Deeply understand pdf papers, directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Paper Reader Deep
Help me get started with Paper Reader Deep
Explains what Paper Reader Deep does, walks through the setup, and runs a quick demo based on your current project
Use Paper Reader Deep to deeply understand PDF papers, extract key information, critically a...
Invokes Paper Reader Deep with the right parameters and returns the result directly in the conversation
What can I do with Paper Reader Deep in my documents & notes workflow?
Lists the top use cases for Paper Reader Deep, with example commands for each scenario
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Paper Reader Deep extends your AI assistant with the ability to deeply understand PDF papers, extract key information, critically analyze and generate structured in-depth reading reports, and correlate user research directions. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Paper Reader Deep as its underlying capability.
Paper Reader Deep works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Paper Reader Deep once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
Paper Reader Deep installs like any other MCP skill: drop the folder into `~/.claude/skills/paper-reader-deep/` for global access, or `.claude/skills/paper-reader-deep/` to keep it scoped to one project. After a quick restart of Claude, you can trigger it explicitly with `/paper-reader-deep`, or let the AI decide when it's the right tool for your request.
Paper Reader Deep has 274 installs and is part of the growing Documents & Notes skill ecosystem on DiscoverAISkills. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/paper-reader-deep/ for personal use (all projects), or .claude/skills/paper-reader-deep/ for project-specific use. Restart your AI client, then invoke with /paper-reader-deep or let the AI discover it automatically.
Paper Reader Deep supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Paper Reader Deep is free to install. Check the repository for licensing information.
Deeply understand PDF papers, extract key information, critically analyze and generate structured in-depth reading reports, and correlate user research directions.
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Identifies repetitive steps in your workflow and sets up Paper Reader Deep to handle them automatically
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