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Review test code for quality, design, and completeness after implementing a feature or fixing a bug. Use when the user asks to "review my tests", "check my test quality", "are these tests good enough", "review testing", or after completing a feature implementation that includes tests. Also use when tests feel brittle, flaky, or superficial. Cross-references production code to find coverage gaps.
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。
Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation skills.
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。
Agents should invoke this skill when choosing patterns, designing traits/interfaces/components, deciding abstraction boundaries, evaluating dependency injection/callbacks, or comparing implementation approaches in Rust, TypeScript/React, or Django/Python.
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。
One-prompt autonomous product design and implementation. Use automatically for broad greenfield UI requests such as 'from scratch', '새 제품/화면을 알아서 만들어줘', or requests that delegate DESIGN.md creation. It decides whether to reuse, establish, refresh, or skip a project design system; asks at most one consequential question batch; then builds and verifies the real surface. Use omd:harness instead only when the user explicitly asks for guided checkpoints.
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。
One-prompt autonomous product design and implementation. Use automatically for broad greenfield UI requests such as 'from scratch', '새 제품/화면을 알아서 만들어줘', or requests that delegate DESIGN.md creation. It decides whether to reuse, establish, refresh, or skip a project design system; asks at most one consequential question batch; then builds and verifies the real surface. Use omd:harness instead only when the user explicitly asks for guided checkpoints.
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。
Execute the implementation planning workflow using the plan template to generate design artifacts.
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。
Run the speckit implementation phase chunk-by-chunk in clean-context subagents, then review, then produce a final report. Picks up from an existing 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。
Perform a dual-lens critical review of the specification and plan from both product strategy and engineering risk perspectives 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。
Screenshot comparison QA for frontend development. Takes a screenshot of the current implementation, scores it across multiple visual dimensions, and returns a structured PASS/REVISE/FAIL verdict with concrete fixes. Use when implementing UI from a design reference or verifying visual correctness.
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。
Check reference SDK implementations using btca ask
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。
Planning agent that creates implementation plans and handoffs from conversation context
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。
OIDC flows, PKCE implementation, token refresh strategies, social login integration, and secure session 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。
Implementation agent that executes a single task and creates handoff on completion
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。
Post-implementation cleanup that removes AI-generated bloat while preserving functionality. Runs pass-by-pass with test verification after each pass. Activate after kraken/spark complete a feature, or when a codebase needs hygiene 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。
Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.
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。
Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
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。
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention 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。
Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation 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。
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
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。
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
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。
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
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。
Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
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。