AI-powered academic paper reviewer. Uses a multi-agent system (Deconstructor, Devil's Advocate, Judge) to analyze papers for logical flaws, contradictions, and empirical validity.
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Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install peer-reviewerOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install peer-reviewerβ 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/peer-reviewer/π‘Extract and place the folder at the path above, then restart your agent.
Category
π»Developer & DevOpsPlatforms
What Peer Reviewer can do for your AI workflow
Ai-powered academic paper directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
Trusted by 1,829+ developers worldwide
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 Peer Reviewer
Help me get started with Peer Reviewer
Explains what Peer Reviewer does, walks through the setup, and runs a quick demo based on your current project
Use Peer Reviewer to aI-powered academic paper reviewer
Invokes Peer Reviewer with the right parameters and returns the result directly in the conversation
What can I do with Peer Reviewer in my developer & devops workflow?
Lists the top use cases for Peer Reviewer, with example commands for each scenario
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Peer Reviewer extends your AI assistant with the ability to aI-powered academic paper reviewer. Uses a multi-agent system (Deconstructor, Devil's Advocate, Judge) to analyze papers for logical flaws, contradictions, and empirical validity. 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 Peer Reviewer as its underlying capability.
Peer Reviewer 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 Peer Reviewer 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.
Getting started with Peer Reviewer takes about two minutes. Place the skill at `~/.claude/skills/peer-reviewer/` (personal, all projects) or `.claude/skills/peer-reviewer/` (project-specific), then restart your AI client. From that point, typing `/peer-reviewer` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.
Peer Reviewer has been installed 1,829 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. 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/peer-reviewer/ for personal use (all projects), or .claude/skills/peer-reviewer/ for project-specific use. Restart your AI client, then invoke with /peer-reviewer or let the AI discover it automatically.
Peer Reviewer supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Peer Reviewer is free to install. Check the repository for licensing information.
AI-powered academic paper reviewer. Uses a multi-agent system (Deconstructor, Devil's Advocate, Judge) to analyze papers for logical flaws, contradictions, and empirical validity.
Peer Review
Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output. Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus. Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy, checking any high-stakes Claude output before publishing or acting on it. Don't use when: simple fact-checking (just search the web), tasks that don't benefit from multi-model consensus, time-critical decisions where 60s latency is unacceptable, reviewing trivial or low-stakes content. Negative examples: - "Check if this date is correct" β No. Just web search it. - "Review my grocery list" β No. Not worth multi-model inference. - "I need this answer in 5 seconds" β No. Peer review adds 30-60s latency. Edge cases: - Short text (<50 words) β Models may not find meaningful issues. Consider skipping. - Highly technical domain β Local models may lack domain knowledge. Weight flags lower. - Creative writing β Factual r
Peer Review Response Drafter
Assist in drafting professional peer review response letters. Trigger when user mentions "reviewer comments", "response letter", "peer review", "revise and r...
Automate my developer & devops tasks using Peer Reviewer
Identifies repetitive steps in your workflow and sets up Peer Reviewer to handle them automatically
Peer Reviewer is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.
reviewer-rebuttal-coach
Read review comments, instructor comments or review feedback from the clipboard, and generate item-by-item responses, modification plans and priority suggestions.