Create a GitHub Pages project page for a paper — a public landing page with title, authors, abstract, highlights, figures, links to code/data, and optionally an embedded agent chat. Use when a researcher wants a web presence for their paper beyond just a GitHub repo.
Draft and iterate the APP paper-agent documentation in publication-staging, including AGENTS.md, CLAUDE.md, and README.md, using the author-approved staging tree and reproduction report.
Extract publication-safe research context from Claude Code or Codex chat/session history and prepare it as supplementary material. Use when a researcher wants to capture the reasoning behind their work — key decisions, methodology choices, debugging insights — from their actual sessions. Can be used standalone or as part of /publish-paper.
Load a published paper repository, local publication-staging tree, non-APP paper repository, or arXiv paper into your current project. Use when a user wants to consult, build on, test, import, or discuss a paper. For arXiv IDs or URLs, fetch metadata/source, search for associated public code, and create a protocol-shaped local import before continuing with paper classification.
Build or revise an APP publication-staging tree from a pre-staging reproduction report, organizing paper, code, data, environment, supplementary materials, licenses, and figure/table reproduction scripts under the protocol layout.
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In scientific research, the main product, the scientific paper, often contains only incomplete information about the work. Readers often need substantial effort to understand a paper and reproduce its results before using it in their own research. Important author know-how from the research process is often absent from the manuscript.
Recent progress in AI agents creates a new way to address this problem: instead of only publishing a paper, publish an agent. If every publication comes with an agent that can explain the paper, help readers reproduce the results, and even support follow-up work, each user can access a faithful and thorough representation of the scientific work. This can accelerate research and enable forms of scientific collaboration that were not previously possible.
APP is an interactive format for authors to publish an AI agent together with research artifacts such as the paper, code, data, and related context. The bundle is a GitHub repository that users can open with any AI coding agent that reads AGENTS.md. The repo carries the paper alongside the code, data, and context needed for the agent to explain the work, reproduce figures, run experiments, and answer questions — more of what the research actually contains than a static manuscript can convey. The repo may also contain author-developed agent skills that are useful for understanding the work. A verified APP publication is a tagged public release with AGENTS.md plus an APP_PUBLICATION.json release manifest tying the release to validation and author approval. The structure of the published repository is illustrated below.

Readers clone the repo, open it in Claude Code, Codex, or any other agent that reads AGENTS.md, and the agent speaks for the paper.
Authors who plan to publish their paper in APP format can use the publish-paper skill. It orchestrates modular step skills that help authors reproduce/check existing results, organize publication materials, draft the AGENTS.md instructions, validate the publication, and carry out the final release or developer-sandbox outcome. The publish-paper workflow is shown below.

APP helps organize and reproduce/check results authors have already produced. It does not provide AI tools for improving the paper's scientific claims, adding new experiments, or carrying out new research.
This repository contains:
PROTOCOL.md — the specification of what an APP publication looks like.skills/ — official APP skills, including publish-paper, reproduce-results, prepare-staging, define-paper-agent, validate-publication, release-outcome, extract-chat-context, create-paper-page, and load-paper.template/ — starter files the skills adapt:
template/AGENTS.md — starter for the publication's AGENTS.md.template/README.md — starter for the publication's human-facing README.md.template/CLAUDE.md — one-line Claude Code import (@AGENTS.md).template/publications.md — template for the working repo's .publications.md release log.assets/readme/ — images used by this README..agents/, .claude-plugin/, and .codex-plugin/ — marketplace and plugin metadata for Codex and Claude Code.CONTRIBUTING.md — how to propose changes to the protocol, templates, and official skills.LICENSE — license terms for the protocol, skills, and templates.First make sure the agent itself is installed. APP runs inside an AI coding agent — it is not a standalone program. Install Claude Code or Codex first, then follow the matching section below.
These are slash commands. Type them inside a running Claude Code session (at the Claude Code prompt), not in your shell. Press Enter after each line to install APP:
/plugin marketplace add LionSR/AgenticPublicationProtocol
/plugin install paper-protocol@paper-protocol
/reload-plugins
npx claudepluginhub lionsr/agenticpublicationprotocol --plugin paper-protocolDetect vacuous Lean 4 proofs — formalizations that compile but don't establish what they claim (fake P=NP and friends)
Specialized research analysis agents for critical thinking, evidence verification, synthesis, and parallel paper analysis
Research integrity plugin for Claude Code — paper auditing, citation verification, experiment analysis, and methodology-first skills for academic workflows.
Research-team agents for Claude Code: supervisor, analysis-implementer, paper-writer, figure-descriptor, reviewer, literature-curator.
Oh My Paper research harness: memory system, Codex delegation, and pipeline commands for academic research projects.
Production-grade academic research pipeline for Claude Code: research → write → review → revise → finalize. 4 skills, 27 modes, 39-agent ensemble, v3.7.3 + v3.8 L3 claim-faithfulness gate, v3.9.0 cross-index triangulation, v3.10 triangulation policy layer, v3.11 deterministic citation verification gate (#182).
A research infrastructure for AI agents. Search, read, and analyze papers from your local knowledge base while coding. Includes arXiv discovery, layered reading, ingestion, topic modeling, citation graphs, insights analytics, Office document inspection, scientific tool docs, and academic writing workflows. Requires Python 3.10+ and pip install.