Borrowing it
Nothing to install: this file belongs to u9401066/rootcause-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/rootcause-mcp/master/.claude/skills/pubmed-quick-search/SKILL.mdgit clone --depth 1 https://github.com/u9401066/rootcause-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/rootcause-mcp/pubmed-quick-search)<a href="https://agentmods.dev/skills/u9401066/rootcause-mcp/pubmed-quick-search"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pubmed-quick-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/rootcause-mcp/pubmed-quick-search"><img src="https://agentmods.dev/badge/skills/u9401066/rootcause-mcp/pubmed-quick-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 | $0.00040 | $0.01214 |
| Opus 5 | $0.00020 | $0.00607 |
| Sonnet 5 | $0.00008 | $0.00243 |
| Haiku 4.5 | $0.00004 | $0.00121 |
Grade A, and why
pubmed-quick-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 8d 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-quick-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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
快速文獻搜尋
描述
用 unified_search 做第一輪文獻探索。它會自動分析查詢、選擇來源、合併去重,適合快速確認某個主題是否有研究、有哪些代表性文章,以及是否值得進一步做系統性搜尋。
觸發條件
- 「幫我找...的論文」
- 「有沒有關於...的文章」
- 「先快速搜尋一下...」
- 「search papers about...」
- 「find articles on...」
核心工具
unified_search(
query="remimazolam ICU sedation",
limit=10,
ranking="balanced",
output_format="markdown"
)
參數重點
query: 自然語言、Boolean 查詢、或包含 ICD 代碼的查詢都可以limit: 每個來源最多取回幾篇sources: 可指定pubmed,openalex,semantic_scholar,europe_pmc,crossrefranking:balanced,impact,recency,qualityfilters: 以逗號分隔的篩選條件options: 額外旗標,例如預印本、是否深搜等
快速搜尋時,優先使用自然語言 +
filters。只有在需要精細組裝查詢時,才切到系統性搜尋 skill。
最常用範例
1. 一般主題搜尋
unified_search(query="remimazolam ICU sedation", limit=10)
2. 最近幾年的臨床研究
unified_search(
query="diabetes treatment",
limit=20,
ranking="quality",
filters="year:2020-2025, species:humans, clinical:therapy"
)
3. 看最新研究
unified_search(
query="lung cancer liquid biopsy",
limit=15,
ranking="recency"
)
4. 看高影響力文獻
unified_search(
query="CAR-T therapy lymphoma",
limit=15,
ranking="impact"
)
5. 限定來源
unified_search(
query="CRISPR gene therapy",
sources="pubmed,openalex,europe_pmc",
limit=20
)
6. 納入預印本
unified_search(
query="COVID-19 vaccine efficacy",
limit=20,
options="preprints"
)
filters 速查
filters="year:2020-2025, age:aged, sex:female, species:humans, lang:english, clinical:therapy"
可用欄位
year:2020-2025,2020-,-2025,2024age:newborn,infant,preschool,child,adolescent,young_adult,adult,middle_aged,aged,aged_80sex:male,femalespecies:humans,animalslang:english,chinese等clinical:therapy,therapy_narrow,diagnosis,diagnosis_narrow,prognosis,etiology等
options 速查
options="preprints, shallow"
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.
- 8d ago First seen · 171 lines · 40 tokens per session scan A f7d734f738d9
pubmed-quick-search is a skill published in the GitHub repository u9401066/rootcause-mcp (0 stars, last pushed 6d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,214 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-quick-search, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
detecting-pv-signals
Computes disproportionality signals — PRR, ROR, EBGM, and IC (BCPNN) — over FAERS / OpenFDA drug-event data to flag potential safety signals. Use when the user wants to mine spontaneous-report data for drug-reaction associations, build a 2x2 contingency table, compute a Proportional Reporting Ratio or Reporting Odds…
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…
structuring-radiology-reports
Converts free-text radiology narratives into structured findings and impression — with measurements, laterality, anatomy, and follow-up recommendations — after OpenMed NER. Use when the user has a CT/MRI/X-ray/ultrasound/mammography report and needs the sections split (technique, comparison, findings, impression)…
coding-icd10
Suggests candidate ICD-10-CM diagnosis codes (and ICD-10-PCS procedure codes) for diagnoses and procedures extracted by OpenMed, with rationale and a human-coder caveat. Use when the user wants to code a problem list, map a diagnosis span to a billable ICD-10-CM code, route a finding to the right chapter, cross-walk…
deidentifying-clinical-text
Remove, mask, or replace PHI/PII in clinical free text on-device with OpenMed's deidentify(). Use when the user needs to de-identify medical notes, strip patient identifiers, redact PHI before sharing or analysis, anonymize discharge summaries, or pick a de-id method (mask vs remove vs replace vs hash vs shiftdates).…
evaluating-with-leakage-gates
Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or…