scholaraio

Name: scholaraio
Author: ZimoLiao

by ZimoLiao

Verified

Scholar All-In-One: A research infrastructure for AI agents

561stars

76forks

Python

Added 4/7/2026

Installation

# Add to your Claude Code skills
git clone https://github.com/ZimoLiao/scholaraio

Getting Started

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Security ReportVerified

Last scanned: 5/18/2026

{
  "issues": [],
  "status": "PASSED",
  "scannedAt": "2026-05-18T08:04:05.456Z",
  "semgrepRan": false,
  "npmAuditRan": true,
  "pipAuditRan": true
}

README.md

Frequently Asked Questions

What is scholaraio?

scholaraio is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by ZimoLiao. Scholar All-In-One: A research infrastructure for AI agents. It has 561 GitHub stars.

Is scholaraio safe to use?

Yes. scholaraio passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.

How do I install scholaraio?

Clone the repository with "git clone https://github.com/ZimoLiao/scholaraio" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is scholaraio written in?

scholaraio is primarily written in Python. It is open-source under ZimoLiao on GitHub, so you can review or fork the full source.

Are there alternatives to scholaraio?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh scholaraio against similar tools.

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⚠️ Third-Party Software Notice

This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.

Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.

Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.

Read the Terms of Service

ScholarAIO

Scholar All-In-One — an academic harness for AI agents.

English | 中文

Your coding agent already reasons, plans, browses, writes code, and uses tools. ScholarAIO adds the academic harness around it, so the same agent can carry evidence, project state, repeatable workflows, and reviewable outputs across the whole research process.

Your paper library becomes a reusable knowledge base for the same agent.
Skills and CLI contracts give the agent stable ways to search, read, organize, cite, write, and verify.
Optional tools are integrated selectively when they strengthen that workflow and degrade cleanly when unavailable.

Here, All-in-One means one coherent academic workflow, not every scientific package in one distribution. The active agent supplies reasoning and orchestration; ScholarAIO supplies the durable academic context and operational contracts around it.

ScholarAIO offers more than search. It gives an AI coding agent a stable academic substrate for evidence, project memory, tool use, research outputs, and verification without trying to replace the agent itself.

flowchart LR
    R[Researcher] <--> A[Coding agent]
    A <--> H[ScholarAIO academic harness]
    H --> E[Evidence and context]
    H --> W[Skills, CLI, and workspaces]
    H --> O[Outputs and verification]
    H -. optional .-> X[Bounded external adapters]

Quick Start

The default and recommended way to use ScholarAIO is simple: install it, configure it once, and open this repository directly with your coding agent.

git clone https://github.com/ZimoLiao/scholaraio.git
cd scholaraio
pip install -e .
scholaraio setup

Then open the repository in Codex, Claude Code, or another supported agent. In this setup, the agent gets the fullest experience: bundled instructions, local skills, the CLI, the repository knowledge map in docs/DESIGN.md, and the complete codebase context are all available directly. For Claude Code plugins, Codex/OpenClaw skill registration, and other setup paths, see docs/getting-started/agent-setup.md.

Upgrading To 2.0

ScholarAIO 2.0 is a product-boundary and compatibility release; it does not change the current data layout for 1.4 or 1.5 users. Upgrade the package, run scholaraio setup check, and rebuild indexes when appropriate. Users coming from 1.3 or earlier must still complete the explicit runtime migration.

See docs/getting-started/upgrading-to-2.0.md for removed surfaces, migration guidance, and the 2.x compatibility promise.

What It Does

	Feature	Details
PDF Parsing	Deep structure extraction	Convert PDFs into structured Markdown while preserving formulas, figures, and layout as much as possible
Not Just Papers	More than papers	Journal articles, theses, patents, technical reports, standards, and lecture notes — four inbox categories with tailored metadata handling
Hybrid Search	Keyword + semantic fusion	Combine full-text and vector retrieval, with optional line-addressable evidence chunk search for precise source snippets
Topic Discovery	See what your library is about	Automatically group papers into research themes and use interactive views to grasp the overall structure quickly
Literature Exploration	Multi-dimensional discovery	Explore a research direction through journal, topic, author, institution, keyword, year, citation impact, and more
Citation Graph	References & impact	Forward citations, backward citations, and shared-reference analysis
Layered Reading	Read on demand	Start with metadata or the abstract, then move into conclusions or full text only when you need to
Local Library WebUI	Search, cite, and read	Filter records by field, run keyword/semantic/unified retrieval, copy canonical BibTeX, inspect audit status and Markdown summaries, and open PDFs inline or in the OS default viewer; WSL launches a stable Windows edit mirror and automatically persists embedded annotations back to the canonical library PDF
Publisher PDF Fetch	Use your current access	Fetch DOI or publisher-page PDFs through the user's legal network context, with direct campus-network mode and selected/all-library PDF refetch
Multi-Source Import	Connect your existing library	Import directly from reference managers, fetched PDFs, local PDFs, and Markdown without rebuilding your library from scratch
Workspaces	Organize by project	Manage paper subsets with scoped search and BibTeX export
Multi-Format Export	BibTeX, RIS, Markdown, DOCX	Export your full library or a workspace for Zotero, Endnote, submission, or sharing
Metadata Scrub	Incremental cleanup after enrich	Review and repair low-quality titles, authors, and years for non-standard documents, then mark reviewed records to skip future passes
Persistent Notes	Cross-session memory	Keep analysis notes for each paper so future sessions can reuse them instead of starting over
Research Insights	Reading behavior analytics	Search hot keywords, most-read papers, reading trends, and semantic neighbor recommendations for papers you haven't read yet
Federated Discovery	Cross-library search	Search your main library, exploration libraries, and arXiv from one entry point instead of hopping across tools
Backup & Restore	Portable instance recovery	Keep legacy data-only rsync plans or opt into a manifest-validated full-instance backup and one-click restore covering local config, data, workspaces, published outputs, and control state
Grounded Scientific Tool Use	Consult exact interfaces	Use versioned official documentation at runtime instead of guessing scientific-software commands and parameters
Bounded Tool Adapters	Integrate only when justified	Keep external tools optional, isolated, testable, and subject to the 2.x integration gate
Academic Writing	AI-assisted writing	Router-first workflows for literature review, guided single-paper reading, paper sections, citation check, rebuttal, gap analysis, poster packages, and technical reports — with every citation traceable to your own library

For writing tasks, start with the router-style writing entry when the deliverable is clear but the workflow is not. The current writing stack is organized around:

academic-writing: route by deliverable and writing stage
nature-workflow: bridge to the upstream nature-skills bundle for Nature/high-impact figures, polishing, writing, reviewer critique, citation, Data Availability, paper reading, reviewer response, paper-to-PPT, and academic search; direct upstream skills are preferred when available
literature-review: long-form review and survey writing
paper-guided-reading: guided deep reading of a single paper from fuzzy search to full-text analysis
paper-writing: manuscript sections and paper-focused drafting
review-response: rebuttal and response-letter workflows
research-gap: gap analysis and open-question reports
technical-report: technical briefings and topic reports
poster: poster-oriented content packaging
document: final DOCX / PPTX packaging

See docs/guide/writing.md for the full writing map.

Works With Your Agent

ScholarAIO is designed to be agent-agnostic, but different agents expose different integration paths. Some work best when you open this repository directly; others are easier to use through plugins.

Agent / IDE	Open this repo directly	Reuse from another project
Claude Code	`CLAUDE.md` + `.claude/skills/`	Claude plugin marketplace
Codex / OpenClaw	`AGENTS.md` + `.agents/skills/`	`scholaraio setup agent`
Cline	`.clinerules` + `.claude/skills/`	`scholaraio setup agent --target-project ...`
Qwen	`.qwen/QWEN.md` + `.qwen/skills/`	`scholaraio setup agent --target-project ...`
Cursor	`.cursor/rules/scholaraio.mdc` + `AGENTS.md` (`.cursorrules` legacy fallback)	`scholaraio setup agent --target-project ...`
Windsurf	`.windsurfrules`	`scholaraio setup agent --target-project ...`
GitHub Copilot	`.github/copilot-instructions.md`	`scholaraio setup agent --target-project ...`

Skills follow the open AgentSkills.io standard, and .agents/skills/ and .qwen/skills/ are symlinks to .claude/skills/ so different agents can discover and reuse the same skills. Qwen-specific project context lives in .qwen/QWEN.md.

For reuse from another project, run scholaraio setup agent to preview shell, skill-discovery, and project-wrapper changes; add --apply to perform the automatic steps.

Wrappers created with --target-project include local machine paths; review the managed block before committing