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概要と使いどころ
Translate approved GDDs + architecture into epics — one epic per architectural module. Defines scope, governing ADRs, engine risk, and untraced requirements. Does NOT break into stories — run /create-stories [epic-slug] after each epic is created.
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。
Unity 6 platform targeting and build guide. Use when building for specific platforms, configuring build profiles, using platform scripting defines (#if UNITY_IOS, #if UNITY_ANDROID), optimizing for mobile, WebGL, or consoles, working with Addressables/asset bundles, or choosing between IL2CPP and Mono. Based on Unity 6.3 LTS documentation.
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 task needs the judgment of a Data Warehousing Specialist — defining the grain of a fact table before modeling it, choosing a slowly changing dimension (SCD) type for a dimension attribute, designing an incremental ETL/ELT load strategy against a full-refresh alternative, resolving a conformed-dimension conflict across source systems, or diagnosing why a BI report's numbers don't reconcile to the source 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。
Use when a task needs the judgment of a chiropractor — screening a spinal or musculoskeletal complaint for red flags before manipulation, choosing a manipulation technique given a patient's risk profile, designing a care plan with a defined reassessment checkpoint, or deciding whether to continue, modify, or discharge a course of care.
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 task needs the judgment of a Chief Data Officer — resolving conflicting metric definitions across dashboards, deciding whether a dataset should be retained or purged, assigning data ownership for a system feeding regulatory or board reporting, sizing a privacy/breach risk against a proposed data collection, or auditing data readiness before an AI/ML initiative kicks off.
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 task needs the judgment of a board-certified Clinical Neuropsychologist — building a neuropsychological battery to localize a brain-behavior deficit, adjudicating performance validity before interpreting any cognitive score, distinguishing a dementia subtype or mTBI sequela from a psychiatric or malingering cause, or writing a forensic, pre-surgical (epilepsy/tumor), or serial-retest report.
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 task needs the judgment of an Adapted Physical Education Specialist — writing a legally-defensible PE present level and annual goal from motor/fitness assessment data, choosing the right test battery and alternate items for a student's mobility profile, recommending a placement step on the PE least-restrictive-environment continuum, or distinguishing a motor/balance deficit from a behavioral avoidance pattern.
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 task needs the judgment of an allergist/immunologist — interpreting a skin-prick or specific-IgE result against pretest probability, working up a "penicillin allergy" label for delabeling before a needed antibiotic, managing anaphylaxis and biphasic-reaction risk, stepping asthma therapy up or down, or evaluating suspected primary immunodeficiency.
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。
Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell 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。
Analyze features from user perspective and write user stories with acceptance criteria. Use when defining requirements, writing user stories, validating scope against project state, prioritizing features by impact, comparing approaches, analyzing user needs, or planning next work. Triggers: "user story", "acceptance criteria", "scope", "prioritize", "compare requirements", "user needs", "what to build 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。
Spring Modulith 2.0 implementation for bounded contexts in Spring Boot 4. Use when structuring application modules, implementing @ApplicationModuleListener for event-driven communication, testing with Scenario API, enforcing module boundaries, externalizing events to Kafka/AMQP, building a modular monolith, defining module dependencies, managing inter-module events, or organizing DDD modules as packages. Covers package-based module boundaries, named interfaces, event publication, and Scenario API testing. For architecture decisions, see domain-driven-design skill.
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 guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs microservices, implementing aggregates/entities/value objects, or when users mention "DDD", "domain-driven design", "bounded context", "aggregate", "domain model", "ubiquitous language", "event storming", "context mapping", "domain events", "anemic domain model", strategic design, tactical patterns, or domain modeling. Helps make architectural decisions, identify subdomains, design aggregates, and avoid common DDD pitfalls.
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。
Guidance for Tauri v2 window menu definition, event handling, and shortcuts.
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 whenever the user asks to "implement", "add", "build", "create", or "modify" a feature, page, view, component, or module in this Vue project. Three phases: Phase 0 scope analysis (flows + edge cases + UI needs + plan validation, STOP for user), Phase 1 implementation (layered UI → Store → API, Vue 3 Composition + Vuetify 4, /ui design rules), Phase 2 Definition-of-Done checklist + /verify + /pull-request. Auto-runs /create-module if the target module doesn't exist.
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 deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster 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。
Creates Pulumi infrastructure-as-code projects in Python, defines cloud resources (AWS, Azure, GCP), configures ESC environments for secrets management, sets up OIDC authentication for secure deployments, and builds multi-language component resources. Use when creating Pulumi Python projects, writing infrastructure code, configuring cloud providers, managing secrets with Pulumi ESC, setting up OIDC for Pulumi, automating infrastructure deployments with Python, creating reusable Pulumi components in Python, or configuring Python toolchains (pip, poetry, uv) for Pulumi. Also use when the user mentions pyproject.toml with Pulumi, component_provider_host, or Python virtual environments for infrastructure 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。
Execute the implementation plan by processing and executing all tasks defined in tasks.md
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。
Produce a researched long-form article from a topic prompt via an orchestrated pipeline - research agent (first-person sources, working-definition gate), narrative-architecture outline, writer/cold-reviewer loop with an explicit ACCEPT/REVISE verdict contract, then a catalog-deslop pass with a regression gate. The orchestrator dispatches subagents only; the writer never judges its own draft. Use when the user says "article factory", "write an article about X", "run the article pipeline", or asks for a researched long-form piece produced end-to-end. For essays and micro posts in the user's own voice without a research stage, use the prose skill instead.
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。
Bulletproof agent operating protocol. 15 failure-prevention rules distilled from 120+ real sessions and 10 agent definitions. Covers fabrication, constraint tracking, verification, scoping, retry discipline, and communication. Load before any task to prevent the most common agent failure modes.
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。
Turn Chinese text, articles, URLs, or user-defined page plans into polished 1080×1440 Xiaohongshu image-card series in the Quiet Signal visual language. Use when creating, revising, rendering, or visually auditing 小红书图文; use quiet-signal-design alone for non-Xiaohongshu design work.
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。
Apply or audit the approved Quiet Signal core visual language when the user explicitly asks for Quiet Signal styling on a defined website, interface, poster, social graphic, report, presentation, or other design artifact. This skill supplies visual identity and rule checks; it does not plan content, invent platform requirements, or run a production pipeline.
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 application observability with Prometheus, Grafana, Datadog, or CloudWatch — instrument metrics endpoints, configure Golden Signal dashboards, define alert rules with burn-rate SLOs, and add structured logging
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。
Autonomous requirements analysis agent. Parses a spec file (SPEC.md, requirements doc, or any plain-text specification) and runs a structured pass to surface logical contradictions, ambiguous requirements, and underdefined gaps — before any implementation begins.
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。
Maps the entire API surface of a codebase -- route definitions, middleware chains, auth requirements, request/response types, deprecated endpoints, orphaned endpoints, and cross-endpoint inconsistencies..
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。