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Trial shortlist
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
Multi-source literature search, citation verification, strict independent other-citation audits, article-level citation metric tables, influential citer profiling with citation-context extraction, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv, Scopus, ScienceDirect). Use for coordinated literature workflows beyond one MCP call, including 文献检索、 查文献、找文献、文献综述检索、查论文、引文核对、参考文献管理、文献去重、 严格他引、他引判定、排除自引、谁引用了我的文章、引用我的文章的人有没有大牛、 院士引用、校长引用、院长引用、杰青引用、长江学者引用、Fellow引用、文章引用表、 指定文章引用数、严格他引数、整理成表格.
Add strict Nature/CNS citations to manuscript text by splitting long passages into citable segments, searching only accepted flagship and subjournal titles from Nature Portfolio, the AAAS Science family, and Cell Press, filtering by publication time range, and exporting one reference-manager-ready output by default. Use this skill whenever the user asks to input text and automatically get references, add citations to a paragraph/manuscript, find Nature-series or CNS support for statements, create text-to-reference correspondence, "分段引用", "自动给出引用", "Nature系列引用", "CNS及子刊", "支撑文献", "补引用", "找引用", or export EndNote/RIS/ENW/Zotero RDF. Also trigger on general academic-writing citation needs even without the word "Nature", such as adding references while writing a paper, finding sources/literature for a claim, building a reference list, citation/referencing for academic writing, and Chinese phrasings like 学术写作引用、写论文加引用、写paper找文献、加参考文献、配文献、引用文献、文献支撑.
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions. Also trigger on general academic-writing data needs even without the word "Nature", such as writing a data availability statement for any journal, code/data sharing sections, repository selection while writing a paper, and Chinese phrasings like 数据可用性声明、数据可用性、 数据共享、代码可用性、学术写作数据声明、写数据声明、数据存放、数据仓库选择.
Create, revise, audit, and export submission-grade scientific figures for Nature-family and other high-impact venues in Python (matplotlib/seaborn) or R (ggplot2/patchwork/ComplexHeatmap), including multi-panel plots, figures4papers-style work, and journal-ready SVG/PDF/TIFF outputs. Use for paper or scientific plots, manuscript data visualization, 论文配图、学术写作配图、科研绘图、科研作图、画图、作图、出图、论文图表、可视化. Define the conclusion, evidence logic, data integrity, template compatibility, export needs, and reviewer risks before plotting; honor or persist the Python/R backend choice. Also use the separate OpenRouter GPT Image 2 route for explicit AI-generated graphical abstracts, mechanism diagrams, concept schematics, 论文示意图、机制示意图、图形摘要; this route skips backend choice and treats outputs as drafts. Do not use for interactive dashboards, statistics-only analysis, data cleaning, literature review, code debugging, pure photo editing, or Illustrator/Figma-first infographics without manuscript-figure intent.
Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.
| name | trialgpt-matching |
| description | Trial shortlist |
| keywords | ["retrieval","ranking","ClinicalTrials","patient-profile"] |
| measurable_outcome | Produce ≥5 ranked trials (when available) with rationale + missing-data notes within 3 minutes of receiving a patient query. |
| license | MIT |
| metadata | {"author":"TrialGPT Team","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+"}] |
| allowed-tools | ["run_shell_command","read_file"] |
Run the locally checked-out TrialGPT pipeline to retrieve, rank, and explain candidate trials for a patient before deeper eligibility review.
cd repo && pip install -r requirements.txt (or reuse env).trial-eligibility-agent.README.md.Skills/Clinical/Trial_Eligibility_Agent for criterion-level review.