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-fulltext-access/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-fulltext-access)<a href="https://agentmods.dev/skills/u9401066/pharmacy-mcp/pubmed-fulltext-access"><img src="https://agentmods.dev/badge/skills/u9401066/pharmacy-mcp/pubmed-fulltext-access/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-fulltext-access"><img src="https://agentmods.dev/badge/skills/u9401066/pharmacy-mcp/pubmed-fulltext-access.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 | $0.00045 | $0.01125 |
| Opus 5 | $0.00023 | $0.00562 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00112 |
Grade A, and why
pubmed-fulltext-access 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 12d 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-fulltext-access — 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
全文取得指南
描述
目前的全文 workflow 以 get_fulltext 為核心。它會自動嘗試 Europe PMC、Unpaywall、CORE，必要時可擴展到更多來源。若需要圖表、文字探勘或機構訂閱連結，則搭配其他專用工具。
觸發條件
- 「我要看全文」
- 「有 PDF 嗎？」
- 「這篇有 open access 嗎？」
- 「幫我抓方法或結果段落」
- 提到 PMC、全文、PDF、開放取用
核心工具
1. get_fulltext
get_fulltext(identifier="PMC7096777")
get_fulltext(pmid="12345678")
get_fulltext(doi="10.1038/s41586-021-03819-2")
參數重點
identifier: 自動判斷 PMID、PMCID、DOIpmcid/pmid/doi: 也可分開傳sections: 只抓特定段落，例如"introduction,methods,results"include_pdf_links: 是否回傳 PDF 連結include_figures: 是否一起帶 figure metadataextended_sources: 是否擴展到更多來源
最常用範例
1. 直接抓 PMC 全文
get_fulltext(identifier="PMC7096777")
2. 只看方法與結果
get_fulltext(
pmid="12345678",
sections="methods,results"
)
3. 用 DOI 找 OA 版本
get_fulltext(
doi="10.1038/s41586-021-03819-2",
extended_sources=True
)
4. 全文連同 figures
get_fulltext(
pmcid="PMC7096777",
include_figures=True
)
圖表與視覺資料
取得文章圖表
get_article_figures(identifier="PMC12086443")
get_article_figures(pmid="40384072")
適合用在：
- 要單獨抽 figure caption 與 image URL
- 想快速找到流程圖、結果圖、顯微圖
- 需要比全文更結構化的圖像資料
文字探勘
取得 text-mined terms
get_text_mined_terms(pmcid="PMC7096777")
get_text_mined_terms(pmid="12345678", semantic_type="CHEMICAL")
常用 semantic_type:
GENE_PROTEINDISEASECHEMICALORGANISMGO_TERM
沒有 open access 時
機構訂閱工作流
list_resolver_presets()
configure_institutional_access(preset="exlibris_sfx", base_url="https://your-library...")
test_institutional_access()
get_institutional_link(pmid="12345678")
這一組工具適合：
- 已知機構有訂閱，但文章不是 OA
- 想把 PubMed/DOI 轉成圖書館 resolver 連結
建議工作流程
情境 1：從文章直接拿全文
fetch_article_details(pmids="12345678")
get_fulltext(pmid="12345678", sections="abstract,results")
情境 2：搜尋後挑代表性文章讀全文
unified_search(
query="remimazolam ICU sedation",
limit=10,
ranking="quality"
)
# 對選中的 PMID 再做全文抓取
get_fulltext(pmid="12345678", extended_sources=True)
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.
- 12d ago First seen · 187 lines · 45 tokens per session scan A d3724eec0353
pubmed-fulltext-access is a skill published in the GitHub repository u9401066/pharmacy-mcp (3 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,125 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-fulltext-access, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
pdf-extract-create-workflow
Complete PDF lifecycle: download, extract, and generate structured documents with reportlab.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.