awesome-bio-agent-skills

Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics, multi-omics, pathway, metagenomics, database queries, visualization, workflows): search the index, locate the best-matching skill, fetch its SKILL.md, and follow it.

Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.

Fetch a region of cis-eQTL summary statistics from EBI eQTL Catalogue v7+ via tabix-on-FTP. Use when an agent needs eQTL beta / SE / p-value for every variant in a window around a gene's TSS for one specific dataset (study × tissue × quantification method). Input: dataset_id, chromosome, start, end, optional molecular_trait_id. Output: harmonised TSV slice.

Phylogenetic distance matrices and trees from VCF or FASTA data using the fastreeR hybrid Java/Python toolkit (VCF2TREE, VCF2DIST, DIST2TREE, FASTA2DIST).

Find, read, download, and locally cache academic papers. Disambiguate ambiguous queries, discover via keyword search / citation traversal / recommendations / arXiv monitoring / trending / GitHub search, evaluate (TLDR, citations, code, SOTA), read using a 3-level strategy, and save PDFs to a local library for offline reuse. Use when finding a specific paper, listing papers on a topic, tracking recent advances, finding a baseline with code, reading or downloading a paper by URL, searching the local PDF library, or collecting a corpus for survey/ideation. Trigger phrases include: find/search papers, related work, citation analysis, latest research, download paper, save paper, my local library. Do NOT use for generating survey reports (use research-survey), generating research ideas (use research-ideation), writing a Related Work section (use paper-writing), comparing/ranking ideas (use research-ideation), or planning paper structure (use paper-planning).

Use this skill when users need to build, populate, or extend a domain-specific knowledge graph from literature and structured databases. Triggers include: 'build knowledge graph', 'extract claims from papers', 'ingest data into graph', 'batch extract claims', 'knowledge graph construction', 'populate graph from PubMed', 'extract structured claims', 'ingest atlas data', or any request involving knowledge graph population from scientific literature or biomedical databases. Covers both structured data ingestion (Phase 1) and LLM-based claim extraction from papers (Phase 2).

Use this skill whenever the user wants to formalize a network architecture and derive theoretical components from a research idea. Triggers include: 'method design', 'design method', 'network architecture', 'formula derivation', 'method-design', 'theoretical framework', 'derive equations', or any request to transform IDEA.md into a detailed METHOD.md. This skill is the **mandatory interface-layer method formalizer** in NeuroClaw: it reads IDEA.md, designs concrete network structures (layers, modules, connections), performs mathematical derivations (equations, loss functions, proofs), and always outputs a structured METHOD.md.

2021年诺贝尔生理学或医学奖得主，PIEZO1/PIEZO2机械力感受器发现者。 以功能性筛选策略鉴定全新的离子通道家族，揭示了触觉、本体感觉等机械力转导的分子基础。 触发词：「Patapoutian」「PIEZO」「mechanosensation」「mechanotransduction」「压力感受器」「触觉分子机制」。 信息源：诺奖官网、Nature/Science/Cell论文、PNAS/Quanta Magazine/Kavli Prize、Scripps/HHMI官方资料。 调研时间：2026-04-06。

2009年诺贝尔生理学或医学奖得主Carol W. Greider的智慧蒸馏——端粒酶发现者、分子生物学家、女性科学倡导者

Craig C. Mello (2006年诺贝尔生理学或医学奖) 的思维框架与决策视角。 核心镜片：简单模型的力量、RNA作为信息货币、跨学科对话。 调研来源：诺奖官网、学术论文、STAT News、NBC News等一手素材。 触发词：「Mello视角」「RNAi思维」「简单模型思维」「基因沉默」「Mello怎么想」。

David Julius认知框架蒸馏 — 2021年诺贝尔生理学或医学奖得主，温度与触觉受体发现者。 以"自然界的分子工具"为核心方法论，从辣椒素出发开创了整个疼痛感知研究领域。 适用于：科学探索方法论、逆向工程思维、从日常现象发现深层机制的决策框架。 触发词：「Julius视角」「分子工具思维」「从现象到机制」「辣椒素范式」

2025年诺贝尔生理学或医学奖得主Fred Ramsdell的思维框架。 聚焦：免疫耐受机制发现、从单基因突变到疾病治疗的全链条思维、工业界科研的价值。 调研来源：7篇一手论文 + Nobel Prize官方资料。信息量有限（极低调的科学家），心智模型基于有限推断。 触发词：FOXP3、Treg、免疫耐受、自身免疫、IPEX、Fred Ramsdell、诺贝尔医学奖2025。

Jack W. Szostak（2009年诺贝尔生理学或医学奖得主）思维框架。 端粒与端粒酶的共同发现者，RNA世界假说/生命起源领域的领军人物。 核心镜片：化学还原论+跨学科碰撞+问题选择艺术。 适用场景：科研方向选择、跨学科创新、从化学第一性原理思考生命问题、科研诚信决策。

诺贝尔医学奖得主Jeffrey C. Hall的思维视角。2017年因发现昼夜节律分子机制获奖。 核心镜片：基础研究的长期主义、模型生物的非直觉力量、负反馈回路的哲学。 调研来源：14条（诺奖官网一手采访3篇、学术论文6篇、权威媒体5篇）。 心智模型：4个。触发词：「Hall视角」「果蝇哲学」「节律思维」「基础研究」。

诺贝尔奖得主Katalin Karikó的认知框架——40年逆共识坚持、从边缘到改变世界的思维操作系统。 核心镜片：内在信念驱动、问题导向超越领域、实验验证高于同行评价。 触发词：「卡里科」「Karikó」「mRNA思维」「逆共识坚持」「长期主义科学家」「被低估的天才」。

🏅 诺贝尔奖得主认知框架库。输入人名/主题→自动匹配诺奖得主思维视角，或直接激活指定得主的认知框架。 覆盖：2004-2025年诺贝尔生理学或医学奖全部55位得主。 用途：「用Karikó的视角分析」「哪个诺奖得主适合思考这个问题」「蒸馏某位新得主」。 触发词：「诺奖」「nobel」「XX的诺奖视角」「有没有诺奖级别的思维」「蒸馏诺奖得主」。

诺贝尔医学奖得主Ralph M. Steinman（2011年）思维框架。树突状细胞发现者， 在近20年学术质疑中坚守一个发现，用数据而非争辩回应怀疑，最终开创整个免疫学新领域。 用自己的发现治疗自己的胰腺癌——科学家的终极信仰实验。 6维度蒸馏，一手来源为主。触发词：「Steinman视角」「树突状细胞思维」「长期坚守」「数据回应质疑」。

Robert G. Edwards (1925-2013) 的思维框架与决策模式。2010年诺贝尔生理学或医学奖得主，体外受精(IVF)之父。 基于12个一手/二手来源的深度调研，提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Edwards的视角分析问题——特别是在科学创新、伦理争议、长期主义和跨学科协作场景中。 当用户提到「用Edwards的视角」「IVF之父怎么看」「Edwards模式」「Robert Edwards perspective」时使用。

诺贝尔奖得主山中伸弥（Shinya Yamanaka）的认知框架。iPS诱导多能干细胞发现者，2012年诺贝尔生理学或医学奖。 核心镜片：临床痛点驱动的减法科学家——从24个因子削减到4个，从外科手术室走向诺贝尔奖。 触发词：「山中伸弥」「Yamanaka」「iPS细胞」「减法思维」「临床驱动研究」「化繁为简」。 调研来源：15+一手来源（Nobel官方、Cell论文、CiRA官网、多个采访），6个研究文件。 心智模型：4个 | 决策启发式：7条 | 诚实边界：5条

诺贝尔医学奖得主屠呦呦（Tu Youyou, 2015）的思维框架蒸馏。 核心镜片：从传统智慧中提取科学灵感的"古今转化"思维；低温突破决策。 警告：信息密度低（公开演讲/访谈极少），心智模型基于有限素材推断，诚实边界篇幅大。 触发词：「屠呦呦视角」「青蒿素思维」「古今转化」「传统中药现代化」。

2024年诺贝尔生理学或医学奖得主Victor Ambros的思维框架与表达方式。基于诺奖官网访谈、诺贝尔讲座、Lasker奖演讲、学术论文等20+个一手和二手来源的深度调研， 提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Victor Ambros的视角分析问题、审视决策、提供反馈——特别是关于基础研究价值、长期主义、异常数据探索等议题。 当用户提到「用Ambros的视角」「Ambros会怎么看」「microRNA思维」「基础研究价值」「长期主义科学」时使用。

Aesthetic guidelines for scientific figure production. Each style file specifies palettes, typography, layout, and domain-specific sub-styles for a given target venue (NeurIPS, Nature, IEEE, etc.) and figure class (methodology diagram vs. statistical plot). Used by the Graph Maker Team's `illustrator` and `data_plotter` agents.

General-purpose skills for data analysis infrastructure: environment management, parallel computing, and performance optimization.

Skills for opening and driving agent-controllable visualization components in the Pantheon UI sidebar — interactive viewers the agent can open, control, and read back. Viewers: Vitessce (spatial / single- cell omics), Viv (bioimage / microscopy), plus agent-generated apps.

Skills for the Paper Write Team: report and academic templates for HTML/PDF rendering. Each template file is self-contained (HTML + CSS or LaTeX in a single markdown file).

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.

Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.

Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.

Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.

Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF-to-FDR calibration (BF >6 ≈ FDR 0.05 from Hart 2017). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.

Analyzes base-editing screens for variant function. Covers library design (Sanson 2020 GRACE, Hanna 2021 BRCA1/2 SNV scanning, Cuella-Martin 2021), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end, wider for ABE8e), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.