A comprehensive collection of ready-to-use scientific and research skills for AI agents.
Skill-Verzeichnis
Wiederverwendbare Skills für AI Agents entdecken.
Jede Empfehlung bleibt mit ihrem Repository, Audit und Installationspfad nachvollziehbar.
Suchergebnisse: comparative-genomics
Englisches VerzeichnisADAM is a genomics analysis platform with specialized file formats built using Apache Avro, Apache Spark, and Apache Parquet. Apache 2 licensed.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more .
Unix, R and python tools for genomics and data science
Pysam is a Python package for reading, manipulating, and writing genomics data such as SAM/BAM/CRAM and VCF/BCF files. It's a lightweight wrapper of the HTSlib API, the same one that powers samtools, bcftools, and tabix.
A genome visualization python package for comparative genomics
Python library to facilitate genome assembly, annotation, and comparative genomics
Curated collection of 39 agent skills for healthcare and life sciences workflows, installable via the Agent Skills standard.
LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包.