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literature-review

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更新日2026年7月6日 09:33

Searches online for ML/AI papers relevant to a topic. Triggers on "find papers about X", "recent research on X", "any papers on this topic?".

インストール

Codex または Claude でインストールこの Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。

Manusで実行

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epicsagas

epicsagas/byoh-paper-whisperer

GitHub リポジトリを開く Creator のリポジトリを見る

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Manusで実行

literature_review — paper search

Process

Decompose the query. Break the user's topic into 2-4 core keyword combinations (e.g. "LLM inference efficiency" → "speculative decoding", "KV cache compression").
Run online search. Use WebSearch targeting arXiv, Semantic Scholar, OpenReview, and conference proceedings (NeurIPS/ICML/ACL/CVPR etc.) first. No local PDF scanning — online only.
Screen candidates. For each candidate paper, capture at minimum: publication year, venue (or arXiv-only status), authors/affiliation if available, one-line core contribution.
Rank by relevance. Order by direct topical relevance → citation impact → recency. Don't bias purely toward newest — include at least one foundational paper for the area.
Record output. Under _papers/, create a search session note and record each paper as a Markdown link citation:
```
- [Authors et al., Year — Paper Title](arXiv/venue link): one-line contribution
```
Add any new terminology to _papers/glossary.md.
Verify via source_verification. Don't finalize the output until each link is confirmed to actually point to the claimed paper.

Anti-Rationalization

"The title looks relevant enough" — don't add to the list without at least checking the abstract.
"Newest is always most important" — recency bias misses foundational prior work; always include a foundational paper.
"It's an arXiv link so it's verified" — arXiv preprints may be un-peer-reviewed; state this explicitly.

Evidence

Search is complete when: every candidate has year/venue/contribution recorded, Markdown link citations actually resolve to the claimed paper, and at least one foundational paper is included.

Red Flags

Adding to the list without reading the abstract
Listing paper names with no link
Presenting only a narrow time window (e.g. last 3 months) without disclosing that bias

このリポジトリの他の Skills

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check

epicsagas/byoh-paper-whisperer

Analyzes or compares 2+ papers or methods. Triggers on "compare these papers", "analyze this method", "which is better?".

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citation-accuracy

epicsagas/byoh-paper-whisperer

Verifies every Markdown link citation in `_papers/` and `_ideas/` notes resolves to the correct paper and that the attributed claim actually matches what the paper says. Gate skill — run before finalizing literature_review/go/check outputs.

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critic

epicsagas/byoh-paper-whisperer

Adversarial self-review gate that re-reads a literature review, comparison, or idea list before it's presented as final, hunting for overclaiming, cherry-picking, and gaps.

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document

epicsagas/byoh-paper-whisperer

Documentation generator for public APIs, functions, and modules. Use when exports, signatures, or new modules are added or changed.

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evidence

epicsagas/byoh-paper-whisperer

Evidence-hierarchy enforcer for research and analysis. Rates every claim by its evidence tier (primary > peer-reviewed > secondary > anecdotal) and refuses to overstate. Use when synthesizing findings or answering factual questions.

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epicsagas/byoh-paper-whisperer

Reads and summarizes ONE specific paper. Triggers on "read this paper", "summarize X", or when the user gives a specific paper title/link.

2026-07-060

literature_review — paper search

Process

Decompose the query. Break the user's topic into 2-4 core keyword combinations (e.g. "LLM inference efficiency" → "speculative decoding", "KV cache compression").

Run online search. Use WebSearch targeting arXiv, Semantic Scholar, OpenReview, and conference proceedings (NeurIPS/ICML/ACL/CVPR etc.) first. No local PDF scanning — online only.

Screen candidates. For each candidate paper, capture at minimum: publication year, venue (or arXiv-only status), authors/affiliation if available, one-line core contribution.

Rank by relevance. Order by direct topical relevance → citation impact → recency. Don't bias purely toward newest — include at least one foundational paper for the area.

Record output. Under _papers/, create a search session note and record each paper as a Markdown link citation:

- [Authors et al., Year — Paper Title](arXiv/venue link): one-line contribution

Add any new terminology to _papers/glossary.md.

Verify via source_verification. Don't finalize the output until each link is confirmed to actually point to the claimed paper.

Anti-Rationalization

"The title looks relevant enough" — don't add to the list without at least checking the abstract.

"Newest is always most important" — recency bias misses foundational prior work; always include a foundational paper.

"It's an arXiv link so it's verified" — arXiv preprints may be un-peer-reviewed; state this explicitly.

Evidence

Search is complete when: every candidate has year/venue/contribution recorded, Markdown link citations actually resolve to the claimed paper, and at least one foundational paper is included.

Red Flags

Adding to the list without reading the abstract

Listing paper names with no link

Presenting only a narrow time window (e.g. last 3 months) without disclosing that bias

name	literature_review
description	Searches online for ML/AI papers relevant to a topic. Triggers on "find papers about X", "recent research on X", "any papers on this topic?".