pubmed-systematic-search
Comprehensive search using generate_search_queries and unified_search. Triggers: 系統性搜尋, 完整搜尋, 文獻回顧, systematic search, comprehensive, MeSH expansion, 同義詞
Best use case
pubmed-systematic-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive search using generate_search_queries and unified_search. Triggers: 系統性搜尋, 完整搜尋, 文獻回顧, systematic search, comprehensive, MeSH expansion, 同義詞
Teams using pubmed-systematic-search should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pubmed-systematic-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pubmed-systematic-search Compares
| Feature / Agent | pubmed-systematic-search | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Comprehensive search using generate_search_queries and unified_search. Triggers: 系統性搜尋, 完整搜尋, 文獻回顧, systematic search, comprehensive, MeSH expansion, 同義詞
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 系統性文獻搜尋
## 描述
這個 workflow 用在「要找得完整」而不是「先快速看一下」的情境。核心做法是先用 generate_search_queries 取得 MeSH、同義詞與建議查詢,再由 Agent 或使用者組裝成明確的 Boolean 查詢,最後用 unified_search 執行。
## 觸發條件
- 「系統性搜尋」
- 「完整搜尋」
- 「文獻回顧」
- 「comprehensive search」
- 「systematic review」
- 提到 MeSH、同義詞擴展、搜尋策略
---
## 正確工作流程
```text
generate_search_queries
→ 整理 MeSH / 同義詞 / suggested_queries
→ 手動或由 Agent 組 Boolean 查詢
→ analyze_search_query
→ unified_search
→ fetch_article_details / prepare_export / save_pipeline
```
> 目前沒有公開的獨立合併工具工作流。每一次 `unified_search` 本身就會做多來源整合與去重;如果你跑多輪策略,做法應該是比較各輪結果、調整查詢,或把流程保存成 pipeline,而不是依賴舊版 merge 思路。
---
## Step 1: 取得搜尋素材
```python
generate_search_queries(
topic="remimazolam ICU sedation",
strategy="comprehensive"
)
```
### `strategy` 選項
- `comprehensive`: 預設,適合完整搜尋
- `focused`: 收斂到較高證據等級
- `exploratory`: 放寬,找更多變體與同義詞
### 你真正要用的欄位
- `mesh_terms`: 標準詞彙與對應同義詞
- `all_synonyms`: 可直接組 OR 群組
- `suggested_queries`: 當作參考,不是最後答案
- `pubmed_translation`: 檢查 PubMed 實際如何理解查詢
---
## Step 2: 組裝 Boolean 查詢
### 範例:從素材組出可執行查詢
```python
query = '''
("Intensive Care Units"[Title/Abstract] OR ICU[Title/Abstract] OR "critical care"[Title/Abstract])
AND
(remimazolam[Title/Abstract] OR "CNS 7056"[Title/Abstract] OR "ONO 2745"[Title/Abstract])
AND
(sedation[Title/Abstract] OR "procedural sedation"[Title/Abstract])
'''
```
### 兩個原則
1. 主概念之間通常用 `AND`
2. 同義詞與別名通常用 `OR`
---
## Step 3: 執行前先分析
```python
analyze_search_query(query=query)
```
這一步用來確認:
- 查詢是否太寬或太窄
- PubMed translation 是否符合預期
- 有沒有拼字或概念錯置
---
## Step 4: 執行搜尋
```python
unified_search(
query=query,
sources="pubmed,europe_pmc,openalex",
limit=50,
ranking="quality",
filters="year:2020-2025, species:humans, clinical:therapy",
output_format="json"
)
```
### 常用調整方式
- 想更完整:加入 `options="preprints"`
- 想更快:加入 `options="shallow"`
- 不要自動放寬:加入 `options="no_relax"`
- 重視新近性:`ranking="recency"`
- 重視證據品質:`ranking="quality"`
---
## 完整範例
### 情境:完整搜尋 remimazolam ICU sedation
```python
# Step 1: 取得 MeSH 與同義詞素材
materials = generate_search_queries(
topic="remimazolam ICU sedation",
strategy="comprehensive"
)
# Step 2: 組裝查詢
query = '''
("intensive care"[Title/Abstract] OR ICU[Title/Abstract] OR "critical care"[Title/Abstract])
AND
(remimazolam[Title/Abstract] OR "CNS 7056"[Title/Abstract] OR "ONO 2745"[Title/Abstract])
AND
(sedation[Title/Abstract] OR "procedural sedation"[Title/Abstract])
'''
# Step 3: 先分析
analyze_search_query(query=query)
# Step 4: 再執行
unified_search(
query=query,
limit=50,
ranking="quality",
filters="year:2020-2025, species:humans, clinical:therapy",
output_format="json"
)
```
---
## 如果結果太少或太多
### 結果太少
- 把 `strategy` 改成 `exploratory`
- 移除部分限制條件
- 減少 `AND`、增加同義詞 `OR`
- 移除 `clinical` 或過窄的年齡/性別限制
### 結果太多
- 把 `strategy` 改成 `focused`
- 增加主題限定詞
- 加上 `filters="year:..., clinical:..."`
- 把 sources 收斂到 `pubmed,europe_pmc`
---
## 可重複使用時
當搜尋策略已經穩定,不要每次重組:
```python
save_pipeline(name="icu_sedation_review", pipeline_config="...")
```
之後可以:
```python
unified_search(pipeline="saved:icu_sedation_review")
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