How do I install the Reviewsearch skill?

Run npx skillmds@latest add yjoonjang/reviewsearch in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Reviewsearch safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Reviewsearch?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Reviewsearch free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Reviewsearch?

yjoonjang (@yjoonjang) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Reviewsearch by yjoonjang — Agent Skill

Name: Reviewsearch
Author: yjoonjang

Reviewsearch

Search 200k+ ML/NLP/CV peer reviews (ICLR/ICML/NeurIPS/COLM) with their author rebuttals. Use when the user wants real examples of reviewer concerns, how a kind of paper gets critiqued, or how authors rebutted — e.g. "find reviews asking for significance tests", "how did papers rebut novelty concerns", "what weaknesses do reviewers raise about diffusion models".

yjoonjang Updated Sep 22, 2026

File contents

ReviewSearch

Search a corpus of 205,988 peer reviews (with author rebuttals) from ICLR / ICML / NeurIPS / COLM, served by the ReviewSearch hybrid-search API.

When to use

Reach for this when the user wants real peer-review evidence: what reviewers criticize about a kind of paper, examples of a specific concern, or how authors rebutted a critique. Not for searching paper content — this searches reviews.

How to search

Run the bundled script (Python 3, no dependencies). Paths here are relative to this skill's own directory, the one this SKILL.md was loaded from, so they resolve in any host that discovers skills:

python "<this skill dir>/scripts/search_reviews.py" "<query>" [options]

Options: --top-k N (default 10), --accepted-only, --year-min YYYY, --year-max YYYY (corpus spans 2023–2026).

Example:

python "<this skill dir>/scripts/search_reviews.py" \
  "reviews asking for a statistical significance test" --top-k 5 --accepted-only

Writing good queries

Queries are English and work best as concern-style natural language, e.g. "missing ablation on learning rate", "reviewers doubting reproducibility", "papers criticized for weak baselines".
The primary search target is each review's concern text (weaknesses + questions).

Reading results

The script prints JSON: { count, results: [...] }. Each result has:

venue, year, title, decision (Accept/Reject variants)
summary — the reviewer's summary of the paper
concern — the reviewer's weaknesses + questions (what to focus on)
rebuttal — the author's response (every result has one)
score — relevance

When you present results to the user, cite the venue/year and note the source (see attribution below).

Notes

The API is a free demo and may cold-start (take a minute) after being idle — if a call times out, wait ~1 minute and retry once.
Point at a different backend with the RS_API_URL env var if needed.

Attribution

Data: peer reviews from OpenReview (ICLR / ICML / NeurIPS / COLM), licensed CC-BY-4.0.

---name: reviewsearchdescription: Search 200k+ ML/NLP/CV peer reviews (ICLR/ICML/NeurIPS/COLM) with their author rebuttals. Use when the user wants real examples of reviewer concerns, how a kind of paper gets critiqued, or how authors rebutted — e.g. "find reviews asking for significance tests", "how did papers rebut novelty concerns", "what weaknesses do reviewers raise about diffusion models".---# ReviewSearchSearch a corpus of **205,988 peer reviews** (with author rebuttals) from ICLR / ICML / NeurIPS / COLM,served by the ReviewSearch hybrid-search API.## When to useReach for this when the user wants **real peer-review evidence**: what reviewers criticize about a kindof paper, examples of a specific concern, or how authors rebutted a critique. Not for searching paper*content* — this searches *reviews*.## How to searchRun the bundled script (Python 3, no dependencies). **Paths here are relative to this skill's owndirectory**, the one this SKILL.md was loaded from, so they resolve in any host that discovers skills:```bashpython "<this skill dir>/scripts/search_reviews.py" "<query>" [options]```Options: `--top-k N` (default 10), `--accepted-only`, `--year-min YYYY`, `--year-max YYYY` (corpus spans 2023–2026).Example:```bashpython "<this skill dir>/scripts/search_reviews.py" \ "reviews asking for a statistical significance test" --top-k 5 --accepted-only```## Writing good queries- Queries are **English** and work best as **concern-style natural language**, e.g. "missing ablation on learning rate", "reviewers doubting reproducibility", "papers criticized for weak baselines".- The primary search target is each review's **concern** text (weaknesses + questions).## Reading resultsThe script prints JSON: `{ count, results: [...] }`. Each result has:- `venue`, `year`, `title`, `decision` (Accept/Reject variants)- `summary` — the reviewer's summary of the paper- `concern` — the reviewer's weaknesses + questions (what to focus on)- `rebuttal` — the author's response (every result has one)- `score` — relevanceWhen you present results to the user, cite the venue/year and note the source (see attribution below).## Notes- The API is a free demo and may **cold-start** (take a minute) after being idle — if a call times out, wait ~1 minute and retry once.- Point at a different backend with the `RS_API_URL` env var if needed.## AttributionData: peer reviews from **OpenReview** (ICLR / ICML / NeurIPS / COLM), licensed **CC-BY-4.0**.

Frequently asked questions