🛠️ Literatureレビュー
学術論文や専門分野の情報を効率的に集め
📺 まず動画で見る(YouTube)
▶ 【衝撃】最強のAIエージェント「Claude Code」の最新機能・使い方・プログラミングをAIで効率化する超実践術を解説! ↗
※ jpskill.com 編集部が参考用に選んだ動画です。動画の内容と Skill の挙動は厳密には一致しないことがあります。
📜 元の英語説明(参考)
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
🇯🇵 日本人クリエイター向け解説
学術論文や専門分野の情報を効率的に集め
※ jpskill.com 編集部が日本のビジネス現場向けに補足した解説です。Skill本体の挙動とは独立した参考情報です。
⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。
🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
- 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
- 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-17
- 取得日時
- 2026-05-17
- 同梱ファイル
- 1
💬 こう話しかけるだけ — サンプルプロンプト
- › Literature Review を使って、最小構成のサンプルコードを示して
- › Literature Review の主な使い方と注意点を教えて
- › Literature Review を既存プロジェクトに組み込む方法を教えて
これをClaude Code に貼るだけで、このSkillが自動発動します。
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
Literature Review
Use this skill when the task is to find, screen, synthesize, and cite a body of academic or technical literature.
When to Use
- Building a systematic, scoping, or narrative literature review.
- Synthesizing the state of the art for a research question.
- Finding gaps, contradictions, or future-work directions.
- Preparing citation-backed background sections for papers or reports.
- Comparing evidence across peer-reviewed papers, preprints, patents, and technical reports.
Review Types
- Narrative review: broad synthesis; useful for orientation.
- Scoping review: maps concepts, methods, and evidence gaps.
- Systematic review: predefined protocol, reproducible search, explicit screening and exclusion.
- Meta-analysis: systematic review plus quantitative effect aggregation.
Ask the user which level of rigor is needed. If unspecified, default to a scoping review for exploratory work and a systematic review for publication or clinical claims.
Workflow
1. Define the Question
Convert the prompt into a searchable research question.
For clinical or biomedical work, use PICO:
- Population
- Intervention or exposure
- Comparator
- Outcome
For technical work, use:
- system or domain
- method or intervention
- comparison baseline
- evaluation metric
2. Plan the Search
Create a search protocol before collecting sources:
- databases to search
- date range
- languages
- publication types
- inclusion criteria
- exclusion criteria
- exact search strings
Minimum useful database set:
- PubMed for biomedical and life-sciences literature.
- arXiv for CS, math, physics, quantitative biology, and preprints.
- Semantic Scholar or Crossref for broad academic discovery.
- Domain-specific sources when relevant, such as clinical-trial registries, patent databases, standards bodies, or official technical docs.
3. Search and Log Evidence
Keep a search log that makes the review reproducible:
| Database | Date searched | Query | Filters | Results | Export |
| --- | --- | --- | --- | ---: | --- |
| PubMed | 2026-05-11 | `("CRISPR"[tiab] OR "Cas9"[tiab]) AND "sickle cell"[tiab]` | 2020:2026, English | 86 | PMID list |
| arXiv | 2026-05-11 | `CRISPR sickle cell gene editing` | q-bio, 2020:2026 | 9 | BibTeX |
Save raw IDs, URLs, DOIs, abstracts, and notes separately from the final prose.
4. Deduplicate
Deduplicate in this order:
- DOI
- PMID or arXiv ID
- exact title
- normalized title plus first author and year
Record how many duplicates were removed.
5. Screen Sources
Screen in stages:
- title
- abstract
- full text
For systematic work, record exclusion reasons:
- wrong population
- wrong intervention
- wrong outcome
- not primary research
- duplicate
- unavailable full text
- outside date range
6. Extract Data
Use a structured extraction table:
| Study | Design | Population/Data | Method | Comparator | Outcome | Key finding | Limitations |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Author Year | RCT/cohort/review/etc. | sample or corpus | method | baseline | measured outcome | result | caveat |
For technical papers, include dataset, benchmark, metric, baseline, and reproducibility notes.
7. Synthesize
Group evidence by theme rather than summarizing papers one by one.
Useful synthesis lenses:
- strongest evidence
- conflicting evidence
- methodological weaknesses
- population or dataset limits
- recency and replication
- practical implications
- unanswered questions
Separate claims by confidence:
- High confidence: replicated, high-quality evidence across sources.
- Medium confidence: plausible but limited by sample, method, or recency.
- Low confidence: early, speculative, single-source, or weakly measured.
8. Verify Citations
Before finalizing:
- verify DOI, PMID, arXiv ID, or official URL
- check author names and publication year
- do not cite a paper for a claim it does not make
- mark preprints as preprints
- distinguish reviews from primary evidence
Output Template
# Literature Review: <Topic>
Generated: <date>
Review type: <narrative | scoping | systematic | meta-analysis>
Search window: <dates>
Databases: <list>
## Research Question
## Search Strategy
## Inclusion and Exclusion Criteria
## Evidence Summary
## Thematic Synthesis
## Gaps and Limitations
## References
## Search Log
Pitfalls
- Do not treat search snippets as evidence.
- Do not mix preprints, reviews, and primary studies without labeling them.
- Do not omit negative or conflicting findings.
- Do not claim systematic-review rigor without a reproducible protocol.
- Do not use a single database for a broad claim unless the scope is explicitly limited to that database.