# Co Scientist Literature Review

> Systematic literature review and synthesis skill. PRISMA-compliant screening, source evaluation, gap identification, and evidence synthesis from multiple databases. Use when COLLECTING prior research, searching databases, screening papers, synthesizing findings, or identifying research gaps in existing literature.

- Skill: `nahisaho/co-scientist-literature-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add nahisaho/co-scientist-literature-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/co-scientist-literature-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: nahisaho (https://skillmd.com/u/nahisaho)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/nahisaho/co-scientist-literature-review

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# Literature Review

Systematic literature search, screening, and evidence synthesis.

## Use This Skill When

- Searching for prior research on a topic.
- Conducting a PRISMA-compliant systematic review.
- Screening and evaluating source quality.
- Synthesizing findings across multiple studies.
- Identifying research gaps.

## Workflow

1. Define search strategy:
   - Keywords and Boolean operators
   - Target databases (PubMed, Scopus, Web of Science, Google Scholar)
   - Inclusion/exclusion criteria
   - Date range and language filters

2. Execute search and screen results:
   - Title/abstract screening
   - Full-text screening
   - Quality assessment (risk of bias)

3. Extract and synthesize:
   - Data extraction table
   - Thematic analysis or narrative synthesis
   - Gap identification

4. Generate PRISMA flow diagram data

5. Save all outputs to files

## Deliverables

- `report.md`: synthesis narrative with key findings.
- `results/search-strategy.md`: documented search methodology.
- `results/screening-table.csv`: inclusion/exclusion decisions.
- `results/extraction-table.csv`: extracted data from included studies.
- `figures/prisma-flow.md`: PRISMA flow diagram data.

## Available Tools (MCP)

> External tools available via [ToolUniverse](https://github.com/mims-harvard/ToolUniverse) MCP server.
> Falls back to Python `requests` + public REST APIs when MCP is unavailable.

| Source | Tool | Description |
|--------|------|-------------|
| PubMed | `PubMed_search` | PubMed API |
| PubMed | `PubMed_get_article` | PubMed API |
| Semantic Scholar | `SemanticScholar_search` | Semantic Scholar API |
| Semantic Scholar | `SemanticScholar_get_paper` | Semantic Scholar API |
| Crossref | `Crossref_search_works` | Crossref API |

- Read `references/prisma-guide.md` when conducting PRISMA-compliant systematic reviews.

## Quality Gates

- [ ] Search strategy is documented and reproducible.
- [ ] Inclusion/exclusion criteria are explicit.
- [ ] At least 3 sources are cross-validated for key claims.
- [ ] Single-source findings are marked with ⚠️.
- [ ] Research gaps are identified with supporting evidence.

If any gate fails: identify the specific failing check, fix the issue, and re-validate before proceeding.

## Gotchas

- Google Scholar の検索結果は網羅性が低い。必ず PubMed/Scopus と併用すること
- 単一ソースの情報は ⚠️ マークを付け、断定的な結論に使用しないこと
- プレプリント（bioRxiv, arXiv）は査読前であることを明記すること
- 検索語の日英バイリンガル展開を行うと、非英語圏の重要な研究を見落とさない
- スクリーニング結果は逐次ファイルに保存すること。コンパクションで中間結果が消失する

## Validation Loop

1. 検索戦略とスクリーニング結果を生成
2. チェック:
   - 検索語が研究目的をカバーしているか
   - 2つ以上のデータベースを使用しているか
   - 単一ソースの主張に ⚠️ が付いているか
3. 不合格なら検索戦略を修正して再実行
4. 合格後のみ合成フェーズへ進む