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: molecular-biology

英語版ディレクトリ

Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 160,000+ scientists worldwide. 140 ready-to-use skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.

33K
Stars
88/100
信頼
カテゴリ: data-analysis監査

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

Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.

2.4K
Stars
83/100
信頼
カテゴリ: rag-knowledge監査

A comprehensive library for computational molecular biology

958
Stars
71/100
信頼
カテゴリ: geo-science監査

Working with molecular structures in pandas DataFrames

752
Stars
69/100
信頼
カテゴリ: geo-science監査

Scikit-learn compatible library for molecular fingerprints and chemoinformatics

377
Stars
68/100
信頼
カテゴリ: geo-science監査

Open-source data lakehouse for biology. Query, trace & validate with a lineage-native lakehouse that supports bio-formats, registries & ontologies. 🍊YC S22

271
Stars
70/100
信頼
カテゴリ: devops監査

Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.

1.1K
Stars
65/100
信頼
カテゴリ: productivity監査

Python package for graph neural networks in chemistry and biology

805
Stars
63/100
信頼
カテゴリ: geo-science監査

Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)

284
Stars
62/100
信頼
カテゴリ: ml-automation監査

Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming, API/SDK surface, and fuzzy debugging where the obvious answer is expensive to get wrong. Triggers "/adhd", "adhd mode", "brainstorm", "ideate", "widen the option space", "divergent ideas", "we keep landing on the same idea". Skip for lookups, syntax, bugs with a known root cause, or closed phrasing ("quick", "standard", "canonical", "textbook"). Use /oracle compare to evaluate options you already have — adhd generates the option space; use /plan-ceo-review to challenge whether to build at all.

42
Stars
66/100
信頼
カテゴリ: design-creative監査

Deep learning meets molecular dynamics.

190
Stars
60/100
信頼
カテゴリ: data-analysis監査