Skill ディレクトリ

AI Agent のための再利用可能な Skill を見つける。

タスクで実際の GitHub Skill を検索し、利用前に Stars、Trust、監査、カテゴリ、インストール経路を確認できます。

すべての推奨は、リポジトリ、監査、インストール経路に明確につながっています。

検索結果: cell-fate-determination

英語版ディレクトリ

A comprehensive collection of ready-to-use scientific and research skills for AI agents.

31K
Stars
78/100
信頼
カテゴリ: utility監査

Single-cell analysis in Python. Scales to >100M cells.

2.5K
Stars
80/100
信頼
カテゴリ: geo-science監査

A python library for multi omics included bulk, single cell and spatial RNA-seq analysis.

1.0K
Stars
84/100
信頼
カテゴリ: geo-science監査

A two-spread digital e-guide preview — page 1 is a cover (display title, author, "What's inside" stats, table of contents teaser); page 2 is a spread (lesson body with pull-quote and a step list). Lifestyle / creator brand tone. Use when the brief asks for an "e-guide", "digital guide", "lookbook", "lead magnet", "creator guide", "playbook", "PDF guide", or "电子指南".

91K
Stars
79/100
信頼
カテゴリ: design-creative監査

A consumer-feeling dating / matchmaking dashboard — left rail navigation, ticker bar of community signals, headline KPIs, a 30-day mutual-matches bar chart, and a match-rate trend block. Editorial typography, restrained accent. Use when the brief asks for a "dating site", "matchmaking", "community dashboard", "social network dashboard", or any consumer product where the data is the story.

91K
Stars
72/100
信頼
カテゴリ: design-creative監査

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.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

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.

34K
Stars
77/100
信頼
カテゴリ: data-analysis監査

Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.

34K
Stars
67/100
信頼
カテゴリ: research監査

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.

25K
Stars
77/100
信頼
カテゴリ: design-creative監査

An Industrial Grade Federated Learning Framework

6.1K
Stars
73/100
信頼
カテゴリ: ml-automation監査

An overview of algorithms for estimating pseudotime in single-cell RNA-seq data

444
Stars
70/100
信頼
カテゴリ: geo-science監査

Cell type annotation for single-cell RNA-seq using multi-LLM consensus

646
Stars
71/100
信頼
カテゴリ: geo-science監査