deep-review
Deep Review is a Claude Code plugin for rigorous review of papers and technical artifacts. Aiming to reduce "edit volume" for researchers an
Open source Open in the app JSON README (API)
About
Deep Review is a Claude Code plugin for rigorous review of papers and technical artifacts. Aiming to reduce "edit volume" for researchers and paper-reviewers, it adds a skill that interviews the user first, reads the artifact carefully, decomposes the review into specialist passes, and verifies claims against sources or computations where possible. It also self-critiques findings, and returns a severity-ranked markdown review with evidence. It is especially useful for pre-submission paper review, theorem/proof stress tests, empirical or methods audits, literature positioning, technical memos, and AI-generated draft review.
Details
- Kind
- Plugins
- Topic
- Files & documents
- Publisher
- dipakkrishnan
- Origin
- marketplace
- Category
- ferramentas
- Last push
- 2026-07-24T02:04:04Z
- Repository state
- ativo
- Language
- Python
- License
- Apache-2.0
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
dipakkrishnan/deep-review-skill/deep-review
README
# Deep Review Deep Review is an experimental skill for rigorous review of papers and technical artifacts. It asks a short calibration interview, reads the artifact carefully, decomposes the review into specialist passes, checks claims against sources and computations where possible, self-critiques its own findings, and returns a severity-ranked review. The goal is simple: find the errors a generic chat-with-file pass is likely to miss. Deep Review can also maintain optional review memory — user-level taste in `~/.deep-review/` and project-level history in `.deep-review/` — so future reviews can pick up the user's preferred standards, recurring concerns, and review taste. It only writes memory after explicit user approval. ## What It Is Good For - pre-submission paper review - theorem or proof stress tests - empirical / methods audits - literature positioning checks - technical memo review - AI-generated draft review ## Status Deep Review is now live in Anthropic's [Claude Community marketplace](https://github.com/anthropics/claude-plugins-community). It is still early, and I am learning where it is most useful. If you want to try it on a real paper or artifact, install the skill in your coding agent. ## Install ### Claude Code Install Deep Review from the Claude Community marketplace: ```text /plugin marketplace add anthropics/claude-plugins-community /plugin install deep-review@claude-community /reload-plugins ``` The repository-hosted marketplace is also available as a fallback: ```text /plugin marketplace add dipakkrishnan/deep-review-skill /plugin install deep-review@deep-review-marketplace /reload-plugins ``` Or copy the skill folder into a project directly: ```bash mkdir -p .claude/skills cp -R /path/to/deep-review-skill/skills/deep-review .claude/skills/deep-review ``` Then ask Claude Code: ```text Use $deep-review to review this paper for theorem correctness and literature positioning. ``` ### Claude.ai Download [`deep-review.zip`](https://github.com/dipakkrishnan/deep-review-skill/releases/latest/download/deep-review.zip) (always points at the latest release), then in Claude.ai go to **Customize → Skills → + → Upload a skill** and drop the zip in. To build the zip yourself from a checkout: ```bash scripts/package-skill.sh # produces dist/deep-review.zip ``` ### Codex Codex does not yet expose a marketplace-style installer for third-party plugins. Copy the skill into a Codex skills directory and restart Codex: ```bash cp -R skills/deep-review "$CODEX_HOME/skills/deep-review" ``` ## Example Prompts ```text Use $deep-review to review this PDF as a skeptical economics referee. Focus on model correctness, calibration, and missing literature. ``` ```text Use $deep-review to stress-test the proof of Theorem 2 and identify assumptions that do not hold. ``` ```text Use $deep-review to review this technical memo. Skip copy edits and focus on unsupported claims. ``` ## What To Expect Deep Review will usually ask a few calibration questions first: - What is your relationship to the artifact? - What kind of criticism is most useful? - Which parts worry you most? - What should it avoid spending time on? Then it produces a markdown review with: - overall assessment - critical issues - significant concerns - minor issues - questions for the authors Optionally, Deep Review can save local memory for future runs: - standing review preferences - target venues or standards - recurring issue patterns - lightweight history of reviewed artifacts ## Requirements The skill itself is instructions and references. The helper scripts require Python. PDF extraction uses PyMuPDF: ```bash pip install pymupdf ``` ## Privacy Deep Review does not collect telemetry or send data to any third-party service controlled by this plugin. See [PRIVACY.md](PRIVACY.md). ## Notes Deep Review is not a replacement for expert human judgment. It is a way to make review cheaper, sharper, and more adversarial before you ask humans to spend scarce attention on the artifact.