3,094 件 ・ 関連度順
概要と使いどころ
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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。
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
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。
Selects biomarker features from high-dimensional omics data using Boruta all-relevant selection, mRMR, LASSO/elastic-net, and stability selection, while controlling the leakage, irreproducibility, and correlated-feature traps that make most published signatures fail to replicate. Use when identifying candidate biomarkers, deciding between an all-relevant and a minimal-optimal selector, or judging whether a selected gene set is reproducible. For unbiased performance estimation of the resulting model see machine-learning/model-validation; for interpreting a trained model see machine-learning/prediction-explanation.
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。
Validates predictive models on omics and biomedical data with nested cross-validation, group/batch/temporal-aware splits, the full data-leakage taxonomy, probability calibration, decision-curve net benefit, optimism correction, sample-size planning, and TRIPOD+AI reporting. Use when estimating model performance honestly, choosing a CV scheme, detecting leakage, or judging whether reported discrimination means the model is actually useful. For feature selection itself see machine-learning/biomarker-discovery; for confirmatory-trial inference see clinical-biostatistics/trial-reporting.
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。
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
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。
Review hot paths, allocations, iteration, caching, event frequency, and correctness-preserving optimization.
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。
Review changes for correctness, security, accessibility, and performance. Use for scoped quality reviews or verification plans; bug fixes use oma-debug.
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。
Full QA review pipeline — security audit (OWASP Top 10), performance analysis, accessibility check (WCAG 2.1 AA), and code quality review
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。
Evaluate AI model performance with metrics, benchmarks, and comparison. TRIGGERS - Use when user needs help with ai-model-evaluator related tasks.
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。
Design analytics for AI chatbot performance. TRIGGERS - Use when user needs help with ai-chatbot-analytics related tasks.
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。
Evaluate AI copilot performance. TRIGGERS - Use when user needs help with ai-copilot-evaluation related tasks.
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。
Evaluate AI agent performance and reliability. TRIGGERS - Use when user needs help with ai-agent-evaluation related tasks.
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。
Extract key performance indicators from client documents, then generate a branded PDF report summarizing the results.
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。
Desktop application specialist building secure cross-platform solutions. Develops Electron apps with native OS integration, focusing on security, performance, and seamless user experience.
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 network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.
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。
Deep, excruciating code review. Use anytime to analyze code for correctness, edge cases, security, performance, and design issues. Not tied to baseline—this is pure code analysis.
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 Kubernetes specialist mastering container orchestration, cluster management, and cloud-native architectures. Specializes in production-grade deployments, security hardening, and performance optimization with focus on scalability and reliability.
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 database administrator specializing in high-availability systems, performance optimization, and disaster recovery. Masters PostgreSQL, MySQL, MongoDB, and Redis with focus on reliability, scalability, and operational excellence.
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 cloud architect specializing in multi-cloud strategies, scalable architectures, and cost-effective solutions. Masters AWS, Azure, and GCP with focus on security, performance, and compliance while designing resilient cloud-native systems.
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。
Senior backend engineer specializing in scalable API development and microservices architecture. Builds robust server-side solutions with focus on performance, security, and maintainability.
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。
Smoke tests contra URL Vercel deployada (preview ou production). Verifica saude minima do deploy — homepage, assets, auth, console errors, performance. Usa Playwright CLI ou suite de smoke tests. Use apos cada deploy para garantir que nada quebrou.
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。
Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.
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。
5-level verification pyramid: static→unit→Playwright E2E (homepage-first, 6bp)→AI visual→post-deploy. 8-check quality gate. Multi-agent testing (functional/security/a11y/performance). Playwright v1.59+ AI agents (Planner/Generator/Healer). WCAG 2.2 AA via axe-core v4.11. Percy+Chromatic visual regression. ADA Title II 2027/2028 deadlines.
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。