1,262 件 ・ 関連度順
概要と使いどころ
Programmatic page generation at scale using template-based SEO and data pipelines: keyword pattern mining, template architecture, and indexation for 100-100K+ pages. Use when building SEO pages at scale or scoping a programmatic SEO build.
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 calibrated interview loops, competency-based question banks, and hiring calibration. Use when designing interview processes, creating hiring pipelines, generating scoring rubrics, analyzing interviewer bias, or building question banks.
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。
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
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 RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
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。
DevOps for CI/CD, containers, Kubernetes, and Terraform. Use when building pipelines, containerizing apps, managing clusters, provisioning cloud infra, deploying with blue- green/canary, or handling infrastructure incidents.
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。
Orchestrate end-to-end release pipelines. Use when running pre-release validation, generating changelogs, bumping semantic versions, scoring deployment readiness, or gating releases with secret scanning and GO/NO-GO checks.
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 multi-agent orchestration with workflow DAGs, routing, handoff protocols, and state management. Use when building pipelines of specialized agents, designing fan-out/fan-in patterns, or implementing fault-tolerant 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。
Audit data quality across pipelines, warehouses, and stores. Use when designing a DQ program, defining DQ dimensions, building rule-based checks, detecting schema drift, monitoring freshness SLAs, or responding to a DQ incident.
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。
Context management engine for AI coding agents. Use when building agent memory systems, optimizing context windows, allocating token budgets, designing RAG pipelines for code, or managing persistent multi-session agent state.
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。
Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.
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 custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
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。
Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill portfolio.
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 the user wants to build an evaluation system for an LLM/agent application but doesn't know where to start — they have traces, prompts, RAG pipelines, or nothing at all. Also use when the user mentions evaluation, eval, benchmarking, testing LLM quality, measuring agent performance, assessing RAG accuracy, or wants to compare prompts/models. This skill is the entry router: it asks diagnostic questions then recommends which sub-skill (local workflow) to use next.
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 TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity compliance.
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 patterns for multi-platform exports including export templates (Windows/Linux/macOS/Android/iOS/Web), command-line exports (headless mode), platform-specific settings (codesign, notarization, Android SDK), feature flags (OS.has_feature), CI/CD pipelines (GitHub Actions), and build optimization (size reduction, debug stripping). Use for release preparation or automated deployment. Trigger keywords: export_preset, export_template, headless_export, platform_specific, feature_flag, CI_CD, build_optimization, codesign, Android_SDK.
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。
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential 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。
Generates infrastructure for exporting app content to social platforms (Instagram Stories, TikTok, Twitter/X) with platform-specific formatting, aspect ratios, and metadata. Use when user wants social media export, share to stories, or platform-specific sharing pipelines.
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。
Code generator skills that produce production-ready Swift code for common app components. Use when user wants to add logging, analytics, onboarding, review prompts, networking, authentication, paywalls, settings, persistence, error monitoring, CI/CD pipelines, localization, push notifications, deep linking, testing, accessibility, widgets, feature flags, app icons, image caching, pagination, HTTP caching, share cards, social export, subscription lifecycle, referral systems, watermarks, streak tracking, milestone celebrations, what's new screens, lapsed user re-engagement, usage insights, variable rewards, consent flows, account deletion, permission priming, force updates, state restoration, debug menus, offline queues, feedback forms, announcement banners, quick win sessions, Spotlight indexing, App Clips, screenshot automation, background processing, app extensions, data export, or SwiftUI preview sample data and variant matrices.
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 knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines, or Azure-integrated storage/BI, and other Azure HDInsight related development tasks. Not for Azure Databricks (use azure-databricks), Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics).
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 knowledge for Azure Business Process Tracking development including deployment. Use when creating CI/CD pipelines, automating builds, running tests, and deploying tracking solutions via DevOps tools, and other Azure Business Process Tracking related development tasks. Not for Azure Monitor (use azure-monitor), Azure Logic Apps (use azure-logic-apps), Azure Data Factory (use azure-data-factory), Azure Machine Learning (use azure-machine-learning).
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。
Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.
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。
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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。