61 件 ・ 関連度順
概要と使いどころ
Use when a task needs the judgment of an atmospheric scientist/operational meteorologist — interpreting ensemble forecast guidance, deciding whether conditions cross a severe-weather warning threshold, calibrating a raw model probability against verification climatology, or writing a forecast discussion or warning product under time pressure.
日本語の概要は準備中です。原文の説明を表示しています。
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
日本語の概要は準備中です。原文の説明を表示しています。
Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance (use prompt-engineer).
日本語の概要は準備中です。原文の説明を表示しています。
Build GenAI and agentic applications with the genai-tk toolkit (https://github.com/tclatos/genai-tk) — a YAML-driven wrapper over LangChain, LangGraph, and 100+ LLM providers. Use this skill whenever the user mentions genai-tk, genai_tk, the GenAI Toolkit, `cli init`, `LangchainAgent`, `get_llm`/`get_embeddings`, `RetrieverFactory`/`ManagedRetriever`, the four bundled agent frameworks (ReAct, Deep, Deer-flow, SmolAgents), the OpenSandbox Docker integration, the `model_id@provider` identifier format, the `global_config()`/`OmegaConfig` system with `app_conf.yaml` and `:merge`, BAML structured extraction, SkillsMiddleware, writing or editing the toolkit's YAML profiles (langchain.yaml, deerflow.yaml, llm.yaml, retrievers.yaml), composing retrievers (vector/bm25/ensemble/reranked/pg_hybrid/zero_entropy), or extending the CLI with `CliTopCommand`. Trigger even when the user only says "the toolkit" in context.
日本語の概要は準備中です。原文の説明を表示しています。
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
日本語の概要は準備中です。原文の説明を表示しています。
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
日本語の概要は準備中です。原文の説明を表示しています。
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
日本語の概要は準備中です。原文の説明を表示しています。
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
日本語の概要は準備中です。原文の説明を表示しています。
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
日本語の概要は準備中です。原文の説明を表示しています。
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
日本語の概要は準備中です。原文の説明を表示しています。
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
日本語の概要は準備中です。原文の説明を表示しています。
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
日本語の概要は準備中です。原文の説明を表示しています。
Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance.
日本語の概要は準備中です。原文の説明を表示しています。