346 件 ・ 関連度順
概要と使いどころ
Tailor resumes, cover letters and interview preparation to supplied experience and a specific job description. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.
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。
Run the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.
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。
Release preparation workflow - security audit → E2E tests → review → changelog → 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。
Implement and maintain compliance with SOC 2, HIPAA, PCI-DSS, and GDPR using unified control mapping, policy-as-code enforcement, and automated evidence collection. Use when building systems requiring regulatory compliance, implementing security controls across multiple frameworks, or automating audit preparation.
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 ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.
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。
Governance, risk, and compliance — risk assessment and scoring, control mapping across NIST CSF 2.0 / ISO 27001:2022 / SOC 2 / CIS Controls v8, gap analysis, audit evidence preparation, and security policy generation
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 a complex or risky code change should be split into Kent Beck's two-step method by first making the change easy through behavior-preserving preparatory refactoring, then making the intended behavior change once the design supports it. This should trigger for requests such as Apply two-step change; Make this risky change safer; Refactor before changing behavior; Separate preparation from behavior change. Part of Plinth Toolkit
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 pull requests with generated descriptions. Triggered when: PR creation, pull request, merge request, code review preparation.
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。
Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling 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。
Interview preparation and recording-consent law. Use for research, question frameworks, one-party or all-party consent rules.
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。
Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging
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。
This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation videos (Paper2Video), and conference posters (Paper2Poster). Use this skill for tasks involving paper dissemination, conference preparation, creating explorable academic homepages, generating video abstracts, or producing print-ready posters from LaTeX or PDF sources.
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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment 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。
eLife figure preparation: file formats (TIFF/EPS/PDF), striking image requirements (1800x900 px), figure supplement naming, and image screening policy treating selective enhancement as misconduct.
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。
The Lancet figure preparation: resolution (300+ DPI at 120%), preferred editable formats (PowerPoint/Word/SVG), column widths (75/154 mm), Times New Roman, in-house redraw policy.
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。
Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.
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。
NEJM figure preparation: resolution (300-1200 DPI), editable vector formats (AI/EPS/SVG), in-house medical illustration policy, and strict image integrity requirements.
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。
PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.
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。
Cell (Cell Press) figure preparation: resolution (300-1000 DPI), formats (TIFF/PDF), RGB color, Avenir/Arial fonts, uppercase panel labels, strict image manipulation policies.
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。
Science (AAAS) figure preparation: resolution (150-300+ DPI), formats (PDF/EPS/TIFF), RGB color, Myriad/Helvetica fonts, strict image manipulation policies including gamma adjustment disclosure.
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。
Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.
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。
Analyzes changesets with risk scoring, categorization by type and impact, and release note preparation. Use when extracting insights from raw change data.
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 face.evoLVe for high-performance face recognition workflows across alignment, data preparation, PyTorch training, feature verification, and Paddle 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。
Use fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation.
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。