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概要と使いどころ
Expert database administrator specializing in modern cloud databases, automation, and reliability engineering. Masters AWS/Azure/GCP database services, Infrastructure as Code, high availability, disaster recovery, performance optimization, and compliance. Handles multi-cloud strategies, container databases, and cost optimization. Use PROACTIVELY for database architecture, operations, or reliability engineering.
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。
Keep Figma and code in sync — map the 3-tier DTCG tokens to Figma Variables (collections + modes), sync in either direction, use the Figma MCP when connected, and verify component parity (variants/states). Use when the user wants to push tokens/components to Figma, pull a design into code, set up token↔Variable sync, or check design-code drift.
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 skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".
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 the skill to control and verify the code interface configuration of your model — how Simulink® model elements are represented in the generated C or C++ code. This includes — (1) specifying names, types (storage classes), and placement of variables that represent model elements (for example, making a model parameter tunable as a global extern variable); (2) specifying names, types, and placement of functions that represent model algorithms; (3) selecting the deployment type (Component, Subcomponent, or Automatic); (4) selecting the interface configuration type (data or service interface); (5) linking a shared Embedded Coder dictionary to a model; (6) creating Embedded Coder dictionary entries and setting their properties; (7) for service interface configuration — specifying service interface definitions, including sender, receiver, client, and server services. Items 1 and 2 can be set per element or as a category-wide default. Item 1 applies to GRT and ERT models; the rest to ERT models only.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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 worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an anti-pattern). Triggers: worker setup, pool constant, per-worker state, non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel 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。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI 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。
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
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 SPEC, INNOVATE, or VALIDATE (Layer 2) approach hinges on an unverified runtime/library/external mechanism: run a probe from the 8-family taxonomy and produce a VIABLE/NOT-VIABLE/INCONCLUSIVE VERDICT artifact.
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。
Product research for dropshipping businesses. Identify profitable products with reliable suppliers, healthy margins, and manageable competition. Evaluates shipping times, return risk, and marketing viability.
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 the user asks to "build a computer-use agent", "automate a GUI with an AI agent", "when to use computer use vs an API", "make browser automation reliable", or "design screenshot-driven agent actions".
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 Terraform provider configuration and authentication with the Plugin Framework: provider schema for credentials (Optional + Sensitive attributes), environment variable fallbacks, credential provider chains (static config, then environment variables, shared credentials file, and platform identity), unknown-value guards in Configure(), secret redaction, configure-time credential validation, and diagnostics that name every source tried. Use when implementing or reviewing a provider's Configure method or provider schema, adding authentication options (API keys, tokens, profiles, credentials files, assume-role), deciding how a provider should resolve credentials, debugging "no valid credential sources" or missing-credentials errors, or unit testing credential resolution.
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。
Systematic contract negotiation strategies for technology services agreements with German/EU law specificity. Provides three-position framework (provider-favorable, balanced, client-favorable), deal-size calibration (€100K to €10M+), five-tier objection handling with regulatory leverage (GDPR, DORA, NIS2, BGB), concession packaging, and position matrices for liability, IP, payment, SLA, data protection, and termination. Use when: (1) Developing negotiation strategies for SaaS, cloud, or managed services agreements, (2) Preparing position papers and fallback positions, (3) Responding to counterparty objections, (4) Structuring concession packages that protect core economics, (5) Calibrating tactics to deal size and leverage, or (6) Applying German contract law constraints (BGB §§ 305-310 AGB, § 309 Nr. 7 liability limits, § 288 statutory interest).
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 a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.
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。
Predicts access barriers in a learning task before delivery, given a learner variability profile. Distinguishes between barriers addressable through design and those requiring specialist support.
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 a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.
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 a proposed assessment for construct validity, reliability, and alignment to learning objectives. Use when reviewing or quality-assuring assessments before 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。
Generates a variable reward system with randomized rewards, daily bonuses, mystery box mechanics, and ethical engagement caps. Use when user wants daily spins, mystery boxes, random rewards, or gamification reward 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。
Expert knowledge for Azure Resiliency development including security, configuration, and deployment. Use when configuring resiliency drills, backup/replication policies, Recovery Plans RBAC, or Infra Resiliency Manager coverage, and other Azure Resiliency related development tasks. Not for Azure Reliability (use azure-reliability), Azure Site Recovery (use azure-site-recovery), Azure Backup (use azure-backup), Azure Monitor (use azure-monitor).
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 Sre Agent development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when wiring SRE Agent to DevOps/GitHub, KQL telemetry, AKS Java apps, IaC deployments, or DR architectures, and other Azure Sre Agent related development tasks. Not for Azure Monitor (use azure-monitor), Azure Reliability (use azure-reliability), Azure Resiliency (use azure-resiliency), Azure Service Health (use azure-service-health).
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 Service Health development including troubleshooting, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when handling Service Health APIs, Resource Graph queries, webhooks, VM Resource Health, or retirement alerts, and other Azure Service Health related development tasks. Not for Azure Monitor (use azure-monitor), Azure Reliability (use azure-reliability), Azure Resiliency (use azure-resiliency).
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 Chaos Studio development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when automating Chaos Studio via CLI/ARM, configuring faults/targets, securing networks/identity, or choosing Workspaces vs classic, and other Chaos Studio related development tasks. Not for Azure Resiliency (use azure-resiliency), Azure Reliability (use azure-reliability), Azure Monitor (use azure-monitor), Azure Site Recovery (use azure-site-recovery).
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。
Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).
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。
Environment variables validation and ConfigModule setup. Use when validating environment variables with Joi/Zod or configuring ConfigModule in NestJS.
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。