Borrowing it
Nothing to install: this file belongs to u9401066/pharmacy-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/u9401066/pharmacy-mcp/main/.claude/skills/pubmed-systematic-search/SKILL.mdgit clone --depth 1 https://github.com/u9401066/pharmacy-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/u9401066/pharmacy-mcp/pubmed-systematic-search)<a href="https://agentmods.dev/skills/u9401066/pharmacy-mcp/pubmed-systematic-search"><img src="https://agentmods.dev/badge/skills/u9401066/pharmacy-mcp/pubmed-systematic-search/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/u9401066/pharmacy-mcp/pubmed-systematic-search"><img src="https://agentmods.dev/badge/skills/u9401066/pharmacy-mcp/pubmed-systematic-search.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01297 |
| Opus 5 | $0.00024 | $0.00648 |
| Sonnet 5 | $0.00010 | $0.00259 |
| Haiku 4.5 | $0.00005 | $0.00130 |
Grade A, and why
pubmed-systematic-search scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to pubmed-systematic-search — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
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
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
- 主概念之間通常用
AND - 同義詞與別名通常用
OR
Step 3: 執行前先分析
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
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
# 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"
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 184 lines · 48 tokens per session scan A 7cdd71a5794a
pubmed-systematic-search is a skill published in the GitHub repository u9401066/pharmacy-mcp (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,297 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pubmed-systematic-search, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…