Borrowing it
Nothing to install: this file belongs to u9401066/academic-figures-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/academic-figures-mcp/main/.claude/skills/pubmed-gene-drug-research/SKILL.mdgit clone --depth 1 https://github.com/u9401066/academic-figures-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/academic-figures-mcp/pubmed-gene-drug-research)<a href="https://agentmods.dev/skills/u9401066/academic-figures-mcp/pubmed-gene-drug-research"><img src="https://agentmods.dev/badge/skills/u9401066/academic-figures-mcp/pubmed-gene-drug-research/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/academic-figures-mcp/pubmed-gene-drug-research"><img src="https://agentmods.dev/badge/skills/u9401066/academic-figures-mcp/pubmed-gene-drug-research.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 | $0.00064 | $0.01293 |
| Opus 5 | $0.00032 | $0.00647 |
| Sonnet 5 | $0.00013 | $0.00259 |
| Haiku 4.5 | $0.00006 | $0.00129 |
Grade A, and why
pubmed-gene-drug-research 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-gene-drug-research — 14 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.
基因與藥物研究
描述
這個 workflow 用在基因、化合物、藥物與臨床變異查詢。公開 MCP 工具已經收斂成一組新的 NCBI extended 介面：基因用 search_gene / get_gene_details / get_gene_literature，化合物用 search_compound / get_compound_details / get_compound_literature，變異則用 search_clinvar。
觸發條件
- 「這個基因的功能是什麼？」
- 「這個藥物的結構或機轉？」
- 「這個變異的臨床意義？」
- 提到 BRCA1、TP53、CYP2C9、warfarin、PubChem、ClinVar
工具對照
| 主題 | 工具 |
|---|---|
| Gene 搜尋 | search_gene, get_gene_details, get_gene_literature |
| Compound 搜尋 | search_compound, get_compound_details, get_compound_literature |
| Variant / ClinVar | search_clinvar |
| 後續文獻追蹤 | fetch_article_details, unified_search |
Gene 工作流
1. 搜尋基因
search_gene(query="BRCA1", organism="human")
2. 取得基因詳情
get_gene_details(gene_id="672")
3. 取得基因相關文獻
get_gene_literature(gene_id="672", limit=20)
4. 展開 PubMed 細節
fetch_article_details(pmids="12345678,87654321")
get_gene_literature走的是 NCBI curated gene-to-publication links，通常比單純 keyword 搜尋更準。
Compound / Drug 工作流
1. 搜尋化合物
search_compound(query="remimazolam")
2. 取得化合物詳情
get_compound_details(cid="11526795")
3. 取得化合物相關文獻
get_compound_literature(cid="11526795", limit=20)
4. 做更廣泛的機轉或比較搜尋
unified_search(
query="remimazolam mechanism action",
limit=20,
ranking="quality"
)
ClinVar / Variant 工作流
搜尋變異
search_clinvar(query="BRCA1 pathogenic")
search_clinvar(query="NM_007294.4:c.5266dupC")
search_clinvar(query="CYP2C9 drug response")
使用方式重點
- 查 gene-level 臨床意義：
search_clinvar(query="BRCA1 pathogenic") - 查精確變異：用 HGVS，例如
NM_007294.4:c.5266dupC - 查藥物基因體學：用基因 + phenotype，例如
CYP2C9 drug response
目前公開介面沒有獨立的 ClinVar detail 工具。要更精準，就把
search_clinvar的查詢寫得更具體。
常見整合流程
情境 1：基因到文獻
search_gene(query="TP53", organism="human")
get_gene_details(gene_id="7157")
get_gene_literature(gene_id="7157", limit=20)
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 · 187 lines · 64 tokens per session scan A dd3ea3440078
pubmed-gene-drug-research is a skill published in the GitHub repository u9401066/academic-figures-mcp (0 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,293 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pubmed-gene-drug-research, differing in 14 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…
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.
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…
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…