1,308 件 ・ 関連度順
概要と使いどころ
Auto-invoked 7-phase workflow for complex-feature intent. Includes fasttrack mode for pre-approved specs. NOT for explicit user-invoked single tasks (see mk:cook); NOT for green-field autonomous builds (see mk:autobuild); NOT for simple bug fixes (see mk:fix).
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。
FastAPI patterns for high-performance async APIs
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 landscape designer transforming yards through photo mapping, 3D visualization, seasonal planning, and deep plant knowledge. Specializes in fast-growing privacy screens (knows arborvitae pitfalls), architecture-appropriate design, outdoor living spaces, and realistic maintenance expectations. Activate on "landscape design", "yard design", "garden planning", "plant selection", "privacy screen", "outdoor living", "backyard makeover", "arborvitae", "hedge", "fast growing tree", "landscaping ideas". NOT for interior design (use interior-design-expert), hardscape construction (consult contractors), or lawn care chemicals (consult local experts).
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。
Designing HTTP cache headers that work correctly across browsers, CDNs, and shared proxies — `Cache-Control` directives per RFC 9111, `stale-while-revalidate` and `stale-if-error` per RFC 5861, the Vary header for varying responses, and surrogate keys for tag-based purging. Grounded in IETF RFCs and Cloudflare/Fastly docs.
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。
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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。
Read, write, and index compressed sequence files (gzip, bzip2, xz, BGZF) with Biopython and bgzip/samtools. Use when working with .gz, .bz2, or .bgz sequence files, when random access into a compressed FASTA/FASTQ is needed, or when SeqIO.index/faidx/tabix rejects a plain .gz. Covers the BGZF-vs-gzip seekability asymmetry, the 'rt'-not-'rb' handle trap, virtual offsets, and gzip-to-BGZF conversion.
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。
Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
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。
Provides guidance on acceptable blood sampling conditions for primary aldosteronism screening when ideal early‑morning supine fasting conditions cannot be met. Triggers include when a clinician says “The patient wasn’t fasting/supine” or questions whether non‑ideal sampling affects screening validity.
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。
Search and preview Irasutoya illustrations with the installed `irasutoya` command-line tool. Use this skill whenever the user wants to find, recommend, preview, randomly pull up, or get the page/image URL of an Irasutoya (いらすとや) image — from a topic, scene, Korean/Japanese/English phrase, visual description, emotion, character, object, or action — even if they don't say "Irasutoya" by name but clearly want a free, cute illustration for a slide, 발표자료, blog, doc, or chat. Also use it to optimize vague requests into fast Japanese keyword candidates and to return page/image URLs. Do NOT use it to generate brand-new AI art (e.g. DALL·E, Midjourney), draw charts or diagrams, design logos or icons, cut out/edit existing images, or recommend other stock-image sites — it only searches Irasutoya's existing catalog.
Expert-level machine design covering shafts, bearings, gears, fasteners, springs, clutches, brakes, and mechanical power transmission 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。
FastAPI service setup: app/main.py, API routers, pydantic-settings, lifespan/app.state services, middleware, health checks, pytest/TestClient, Uvicorn, Gunicorn/uvicorn-worker, Docker/Compose. Use for scaffolding or modernizing Python API services; skip for Flask/Django-only, frontend-only, non-Python APIs, or Supabase-specific work.
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。
明示されたディレクトリ内の独立した git clone の upstream 更新を確認し、clean な fast-forward だけを適用する。マーケットプレース・プラグイン・スキルの更新確認、更新チェック、スキル更新、プラグイン最新？、update skills、check for updates に対応する。ユーザーが /check-updates と入力したら必ずこのスキルを使う。
指定ディレクトリ内の独立したgit cloneのupstream更新を確認し、cleanなfast-forwardだけを適用する。スキルやプラグインの更新確認に使う。
コミット/PR の「全部置換した」「-N%削減」等の完了主張を grep で反証できる残骸や best/typical/worst 試算で検証し、抽出・集約リファクタでは並列度(Promise.all)/fast-path/遅延評価の性能特性退行と、Map/Set 集約キーの cross-kind 衝突による検出漏れも監査する
Configures Codemagic CI/CD pipelines for Flutter using `codemagic.yaml`. Use when setting up automated Flutter builds on Codemagic, configuring iOS code signing without Fastlane Match, deploying to App Store Connect or Google Play from Codemagic, caching pub dependencies in Codemagic, setting up encrypted environment variable groups, running tests with Codemagic's test dashboard, or choosing between Codemagic and GitHub Actions + Fastlane for a Flutter CI/CD pipeline.
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。
Hive CE (Community Edition v2.19.x) NoSQL object database for Flutter providing blazing-fast key-value and object storage with TypeAdapters. Use this skill when implementing offline-first architecture, high-performance local data caching, NoSQL document-style object stores, custom TypeAdapter serialization for complex objects, lazy loading boxes for memory efficiency, encrypted boxes (HiveAES encryption), database compaction for size optimization, storing large datasets without SQL schema overhead, implementing local-first sync patterns, or migrating from SQLite to NoSQL. Supports primitive types, custom objects, lists, and maps. Ideal for apps requiring ultra-fast read/write operations (microsecond latency), object persistence without ORM complexity, or local-first data architecture.
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。
Express/Fastifyを使用した本番環境対応のNode.jsバックエンドサービスを構築し、ミドルウェアパターン、エラーハンドリング、認証、データベース統合、APIデザインのベストプラクティスを実装します。Node.jsサーバー、REST API、GraphQLバックエンド、マイクロサービスアーキテクチャの作成時に使用してください。
Backend Python Specialist IA — Expert en développement backend Python (FastAPI, Django, SQLAlchemy, Celery, async, type safety)
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。
Backend Python v2 — FastAPI, SQLAlchemy, async, Pydantic, deployment
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。
Backend Node.js Specialist IA — Expert en développement backend Node.js (Express, Fastify, NestJS, Prisma, real-time, WebSockets)
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。
Help the user understand the current topic visually with concise diagrams, code-shape sketches, and focused HTML artifacts. Use when the user asks to show / diagram / visualize / sketch / illustrate what is going on, asks "what does this look like", "how does this flow", "show me the structure", or in Japanese 図で見せて / 図解して / 図にして / 構造を見せて / フローを見せて / 全体像を見せて / どうつながってるか見せて. Also use it proactively when an explanation is running long and a call tree, file tree, component tree, diff, Mermaid diagram, or a small HTML artifact would land faster than prose.
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
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。
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
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。
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
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。