1,541 件 ・ 関連度順
概要と使いどころ
Draft a professional, audience-aware email or reply with calibrated tone and a clear call to action. Use when the user needs one message, response, follow-up, support note, or meeting request; use sales-email-sequences for a coordinated multi-touch outbound campaign.
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。
Classify, prioritize, and route incoming support tickets by extracting intent and entities, assigning severity, and generating initial responses. Use when the user requests ticket triage or provides relevant inputs for this workflow.
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。
Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.
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。
Create polished, audience-tailored presentations from outlines or documents, with support for Marp, reveal.js, Google Slides, and PPTX output formats. Use when the user requests presentation creation or provides relevant inputs for this workflow.
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。
Transcribe meeting audio with speaker diarization, generate structured summaries with action items, decisions, and follow-ups, and support multiple audio formats and languages. Use when the user requests meeting transcription or provides relevant inputs for this workflow.
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 machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
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。
Set up and manage data labeling workflows using manual annotation tools, semi-automated pipelines, active learning, and programmatic weak supervision. Use when the user requests data labeling or provides relevant inputs for this workflow.
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。
Generate professional reports — sprint retrospectives, financial summaries, analytics dashboards, and incident postmortems — from structured data with templates, charts, and multi-format output. Use when the user requests report generation or provides relevant inputs for this workflow.
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 clear, searchable help center articles and FAQ entries based on support data, product documentation, and common customer questions. Use when the user requests knowledge base article writing or provides relevant inputs for this workflow.
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。
Optimize machine learning model hyperparameters using grid search, random search, Bayesian optimization, and Hyperband to maximize model performance within a compute budget. Use when the user requests hyperparameter tuning or provides relevant inputs for this workflow.
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。
Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.
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。
Compress selected context to a target token budget while preserving decisions, evidence, constraints, and unresolved questions. Use when relevant material is already selected but too long; use context-optimization when selection, deduplication, and ordering are also required.
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。
Improve code quality and maintainability through systematic identification of code smells and application of proven refactoring patterns. Use when the user requests refactoring or provides relevant inputs for this workflow.
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。
Populate databases with realistic, reproducible test data for development, testing, and staging environments. Use when the user requests database seeding or provides relevant inputs for this workflow.
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。
Optimize a complete candidate context package by deduplicating, filtering, ordering, and allocating its token budget. Use when retrieved or assembled material is noisy or exceeds the useful context budget; use context-ranking for scoring chunks and context-compression for shrinking selected content.
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 reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.
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, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
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。
Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.
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。
Deploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.
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 RESTful APIs with proper resource modeling, HTTP method semantics, status codes, pagination, versioning, and documentation. Use when the user requests api design or provides relevant inputs for this workflow.
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。
Integrate with external APIs using REST clients, webhook consumers, SDK wrappers, and polling patterns with proper authentication, error handling, and retry logic. Use when the user requests api integration or provides relevant inputs for this workflow.
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 GraphQL APIs with well-structured schemas, efficient resolvers, pagination, and performance patterns like DataLoader and federation. Use when the user requests graphql api design or provides relevant inputs for this workflow.
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 OAuth 2.0 authentication flows including authorization code with PKCE, client credentials, and device code for secure API integration. Use when the user requests oauth 2 0 setup or provides relevant inputs for this workflow.
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。
Set up webhook receivers with signature verification, idempotent event processing, retry handling, and dead letter queues for reliable event-driven integrations. Use when the user requests webhook setup or provides relevant inputs for this workflow.
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。