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Design database schemas with normalization, relationships, and constraints. Use when creating new database schemas, designing tables, or planning data models for any database paradigm.
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 React animations with Motion (Framer Motion) - gestures (drag, hover, tap), scroll effects, spring physics, layout animations, SVG. Bundle: 2.3 KB (mini) to 34 KB (full). Use when: drag-and-drop, scroll animations, modals, carousels, parallax. Troubleshoot: AnimatePresence exit, list performance, Tailwind conflicts, Next.js "use client".
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。
Bu template'i klonladiktan sonraki tum kurulumu yurutur — bagimliliklar ve codegen, projeyi kendine uyarlama, Firebase, giris yontemleri (Google/Apple/e-posta dogrulama/misafir gezinme), zorunlu guncelleme (Remote Config), odeme ve abonelik (RevenueCat), Android release imzalama (keystore), native splash ve store URL'leri. Kullaniciya ne istedigini sorar, AppConfig bayraklarini cevirir ve Firebase Console / Apple Developer / Play Console / RevenueCat tarafinda yapmasi gerekenleri sirayla listeler. Kullanici "kurulumu yap", "kurulumu baslat", "klonladim ne yapmaliyim", "Firebase'i bagla", "odeme ekle", "keystore olustur", "release imzasi kur", "set up the project" dediginde kullan. Repoyu ilk kez calistiran icin GIRIS NOKTASIDIR.
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。
Bu template'te Firebase'i ve giris yontemlerini kurar. Kullaniciya hangi saglayicilari istedigini (Google, Apple, e-posta dogrulama, misafir gezinme) sorar, AppConfig bayraklarini cevirir ve o saglayicilarin gercekten calismasi icin Firebase Console / Apple Developer / Play Console / Xcode tarafinda yapmasi gerekenleri adim adim listeler. Kullanici "girisi kur", "Firebase'i bagla", "Google girisi ekle", "Apple ile giris ekle", "auth kurulumu", "login sistemini aktif et", "set up authentication" dediginde kullan. Repoyu klonlayip ilk kez calistiran icin giris noktasidir.
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。
Multi-source research synthesizer. Takes a question, runs 3-5 parallel web searches with varied phrasings, deduplicates, and returns a cited, concise answer. For Hebrew questions, searches in both Hebrew and English. Output is always under ~400 words.
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 working with Aws Documentdb — aWS DocumentDB cluster management and health analysis. Covers cluster inventory, instance status, parameter groups, subnet groups, snapshots, event subscriptions, and performance metrics. Use when inspecting DocumentDB clusters, debugging connectivity issues, reviewing backup configurations, or analyzing cluster performance.
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。
MANDATORY parallel execution patterns, cluster overview script, -o json with jq filtering, batch operations, and common pitfalls
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 working with Argo Workflows — argo Workflows management for Kubernetes-native workflow orchestration. Covers workflow templates, cron workflow scheduling, artifact management, workflow execution, parameter handling, and resource monitoring. Use when checking workflow status, investigating step failures, managing templates, or auditing cron workflows.
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 working with Gcp — cost anti-hallucination rules, MANDATORY parallel execution patterns (30x speedup), monitoring aligners, reusable billing/pricing scripts, VAT/tax handling, and filtering/pagination.
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。
MANDATORY parallel execution patterns (30x speedup), monitor metrics aggregation, --output tsv formatting, and common pitfalls
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 working with Aws — mANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls.
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。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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。
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
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。
Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).
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。
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
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。
Delegate routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent execution.
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。
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
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。
JUnit 5 testing patterns and parameterized-test guidance. Use when writing or reviewing Java unit 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。
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
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。
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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 en concurrence (threads, async/await, mutex, channels, race conditions)
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 en localisation d'agents IA (i18n, l10n, RTL, adaptation culturelle, arabe DZ/darija)
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。