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概要と使いどころ
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
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。
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
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Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.
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End-to-end pooled and single-cell CRISPR screen analysis from FASTQ to hit genes. Orchestrates library design QC, guide counting, six-stage screen QC (plasmid Gini, replicate Pearson, CEGv2 PR-AUC, copy-number artifact), method-appropriate hit calling across MAGeCK RRA/MLE, BAGEL2, drugZ, JACKS, and Chronos, cancer-cell-line copy-number correction (CRISPRcleanR / Chronos), batch correction for multi-batch screens, and the specialized branches for combinatorial paralog screens, single-cell Perturb-seq, base-editor variant-function screens, prime-editor screens, and in vivo bottleneck-aware screens. Use when analyzing any pooled CRISPR screen end-to-end, matching the hit-calling method to the experimental design, integrating copy-number correction into the pipeline, or branching the workflow for single-cell, combinatorial, base-editor, prime-editor, or in vivo variants.
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。
End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.
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Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target before commit-to-medchem, target prioritization across a list, and target-deselection rationale.
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Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.
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Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports with metabolite-pathway mapping.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al 2022/2023 J Chem Inf Model 63:147-158), Pharmer / Pharmit (search), and PharmacoForge (diffusion-based generation, Flynn et al 2025 Front Bioinform), covering ligand-based pharmacophore (from active set alignment) and receptor-based pharmacophore (from binding pocket geometry). Explicit handling of feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
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。
Keep inherited changes narrow, correctly marked, traceable, and easy to rebase across repository layers.
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。
Implement complete SS14 features across ownership, ECS, networking, resources, localization, UI, and tests.
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。
Place contracts and behavior across authority, prediction, presentation, localization, and assembly boundaries.
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。
When the user wants to create or update their product marketing context document. Also use when the user mentions 'product context,' 'marketing context,' 'set up context,' 'positioning,' or wants to avoid repeating foundational information across marketing tasks. Creates `.claude/product-marketing-context.md` that other marketing skills reference.
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。
Generate a multi-sheet XLSX report tracking API usage, credit consumption, and billing across multiple agency clients.
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Reads and changes a running CARLA server's WorldSettings via apply_settings — synchronous vs asynchronous mode, fixed_delta_seconds (fps), physics substepping, and no_rendering_mode — and keeps the Traffic Manager's sync state matched automatically. Use when the user asks to "enable/disable sync mode", "set synchronous mode", "run at a fixed fps/step", "turn off rendering", "make the sim deterministic", or "restore async mode", and as the prerequisite for keeping settings across a map load.
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Spawns vehicles on a running CARLA server and destroys them — scattered across the map's spawn points on Traffic Manager autopilot, a row queued in a single lane at a fixed spacing, or a single hero/ego vehicle (optionally named for native ROS 2 publishing with --ros-name). Covers blueprint filtering, the atomic autopilot hand-off, and cleanup. Use when the user asks to "spawn vehicles/cars/traffic", "add N cars driving around", "put cars in a lane 15 m apart", "spawn the ego/hero vehicle", or "remove the vehicles".
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。
Expert dependency manager specializing in package management, security auditing, and version conflict resolution across multiple ecosystems. Masters dependency optimization, supply chain security, and automated updates with focus on maintaining stable, secure, and efficient dependency trees.
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。
Use when writing or auditing the conversational copy layer of a Salesforce bot or Agentforce agent: utterance authoring strategy, fallback message copy, escalation-criteria phrasing, and persona-consistent dialog scripting across channels. NOT for platform configuration (bot builder setup, topic metadata wiring, action mapping, or Agentforce deployment) — see architect/einstein-bot-architecture and agentforce/agentforce-persona-design for those concerns.
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。
Expert code reviewer specializing in code quality, security vulnerabilities, and best practices across multiple languages. Masters static analysis, design patterns, and performance optimization with focus on maintainability and technical debt reduction.
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。
Configure Adobe OAuth credentials and API access across development, staging, and production environments with separate Developer Console projects, secret managers, and environment-specific scoping. Trigger with phrases like "adobe environments", "adobe staging", "adobe dev prod", "adobe environment setup", "adobe config by env".
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。