415 件 ・ 関連度順
概要と使いどころ
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch 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。
Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda window scheduling, soft-core potentials, REST2 enhanced sampling, MBAR/BAR analysis, and cycle closure validation. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
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 this skill when the user asks about CMV maintenance program requirements under 49 CFR 396.3 — what records must be kept, retention periods (12-18 months on vehicle file + 1 year after disposal), what counts as systematic inspection/repair/maintenance, preventive maintenance scheduling, brake adjustment records, and what auditors look for in vehicle files. Cite 49 CFR 396.3 + 396.21.
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 this skill when the user asks how to design or improve a Preventive Maintenance (PM) program for a fleet — mileage vs time intervals, A/B/C/D inspection levels, ATA standards, common PM tasks, scheduling software, cost tracking, and how PM affects roadside inspections + CSA scores. Reference ATA Equipment Maintenance Council standards.
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。
ディスクリートおよびバッチ製造における生産スケジューリング、ジョブシーケンシング、ライン バランシング、段取り替え最適化、ボトルネック解消に関する体系化された専門知識。15年以上の経験を 持つ生産スケジューラーの知見に基づく。TOC/ドラムバッファーロープ、SMED、OEE分析、混乱対応 フレームワーク、ERP/MES連携パターンを含む。生産スケジューリング、ボトルネック解消、段取り替え 最適化、混乱対応、または製造ラインバランシング時に使用します。
Design and build reactive, type-safe, production-grade backends on Convex. Covers schema, queries/mutations/actions, indexes, auth, file storage, scheduling, real-time multiplayer, mobile backends, and LLM/agent workflows on Convex's one-platform stack.
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。
Convex is the backend agents get right on the first try: an all-TypeScript reactive platform where the database, server functions, scheduling, file storage, auth, and realtime sync are one type-safe system, every function is a transaction, and `tsc` catches most mistakes before deploy. Ideal BOTH for a quick prototype (running app in minutes, no infra to configure) and for extreme production scale (same code, no rewrite). Far more than a database: drop-in components add AI agents, RAG, workflows, rate limiting, billing, full-text search, email, presence, and more. Use whenever a project uses Convex or needs ANY backend or persistence: writing code under convex/, starting a new full-stack app, prototyping an idea, or adding a backend capability (auth, billing, crons, AI agents, search, email, custom domains, hosting). Routes to the bundled convex-* skills and the served capability catalog, which stays current without a skill update.
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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 production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch 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。
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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。
Spaced-repetition flashcard system. Create cards from facts or text, chat with flashcards using free-text answers graded by the agent, generate quizzes from YouTube transcripts, review due cards with adaptive scheduling, and export/import decks as CSV.
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。
Plan days, prioritize tasks, and protect focus time with time blocking, weekly reviews, and energy-aware scheduling.
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。
Calendar management and scheduling. Create events, manage meetings, and sync across calendar providers.
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。
Gate scheduled work on Claude usage limits — /mxm-tasks schedule, run-cycle, scheduled task fire time, background batch dispatch. Checks 5-hour and 7-day usage before firing and sleeps until reset when over threshold.
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。
.NET timezone handling guidance for C# applications. Use when working with TimeZoneInfo, DateTimeOffset, NodaTime, UTC conversion, daylight saving time, scheduling across timezones, cross-platform Windows/IANA timezone IDs, or when a .NET user needs the timezone for a city, address, region, or country and copy-paste-ready C# code.
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。
Calendly scheduling integration. List events, check availability, manage meetings via Calendly API.
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。
Implements background job processing with BullMQ/Redis including job queues, workers, scheduling, retries, and monitoring. Use when users request "background jobs", "queue processing", "async tasks", "BullMQ", or "job scheduler".
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。
Calendar management and scheduling. Create events, manage meetings, and sync across calendar providers.
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。
Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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 calendar events, deadlines, action items, and follow-ups from emails. Works with any calendar provider (Google, Outlook, Apple, Notion, etc.). No external dependencies — pure agent intelligence. Use when the user forwards an email, asks to check inbox for events, or wants to extract structured scheduling data from any text.
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。
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
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。
Generates a weekly social media content calendar across platforms. Chains blog-researcher + linkedin-researcher + social-content-writer to produce 5-7 posts/week with posting schedule.
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。
Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.
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。
dbt Core/Cloud data transformations, testing, documentation, and CI/CD. Activate on: dbt, data transformation, analytics engineering, ref, source, staging model, mart, dbt test. NOT for: orchestration/scheduling (use airflow-dag-orchestrator), data warehouse tuning (use data-warehouse-optimizer).
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。