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Found 196 Skills
Design data architecture at enterprise and solution levels. Cover data mesh, lakehouse, governance, domain-driven design, conceptual/logical/physical data modeling, platform selection, and compliance frameworks. Produce ADRs, data model diagrams, platform comparison matrices, and governance policy templates. Triggers on "design data platform", "choose data warehouse", "data mesh", "lakehouse architecture", "data governance", "data modeling", "platform selection", "data architecture decision", "compliance framework", or "data strategy". For applied AI solution architecture (RAG data plane, embeddings, vector stores in commercial or enterprise products), use applied-ai-architect-commercial-enterprise. For dbt analytics layers and mart delivery, use analytics-data-engineer—not data-architect.
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics
Context layer for AI data agents - query warehouses accurately with semantic layers, metrics, and wiki knowledge through MCP
A structured root-cause investigation protocol for complex, ambiguous, or multi-layer technical problems. Activate this skill whenever: a problem has resisted two or more fix attempts; the root cause is unknown or assumed; you are tempted to try a variation of something that already failed; a system has multiple interacting layers (hardware, OS, runtime, middleware, config, network); the user says "ultrathink", "think deeper", "figure out why", "stop guessing", "find the root cause", or "it's still broken after your fix". Also activate proactively when you catch yourself about to write a fix before you have verified the cause — that instinct is the signal the protocol is needed. The protocol enforces three disciplines that distinguish root-cause investigation from trial-and-error: (1) explicit THOUGHT/ACTION/OBSERVATION cycles, (2) a hard gate that blocks implementation until the cause is verified by direct evidence, and (3) structured escalation when in-process diagnostic tools are exhausted.
Guide for implementing James Shore's Nullables pattern and A-Frame architecture for testing without mocks. Use when implementing or refactoring code to follow patterns of: (1) Separating logic, infrastructure, and application layers, (2) Creating testable infrastructure with create/createNull factory methods, (3) Writing narrow, sociable, state-based tests without mocks, (4) Implementing value objects, (5) Building infrastructure wrappers that use embedded stubs, or (6) Designing dependency injection through static factory methods.
AdvantageKit logging framework best practices for FRC Java robots (2026 / AKit 4.x). Use when implementing or reviewing AdvantageKit IO layers, Logger usage, replay-compatible subsystem design, signal logging, output logging, or deterministic simulation. Triggers on: AdvantageKit, Logger.recordOutput, Logger.processInputs, LoggedRobot, IO interfaces, IOInputs, AutoLog annotation, replay, log-replay, non-deterministic, or any AdvantageKit-related robot code task.
Automate Adobe After Effects via ExtendScript. Use when the user asks to create, modify, or query anything in an After Effects project — layers, keyframes, expressions, effects, compositions, assets, rendering, batch operations. Generates and executes JSX ExtendScript via osascript on macOS.
Provides TypeScript patterns for DynamoDB-Toolbox v2 including schema/table/entity modeling, .build() command workflow, query/scan access patterns, batch and transaction operations, and single-table design with computed keys. Use when implementing type-safe DynamoDB access layers with DynamoDB-Toolbox v2 in TypeScript services or serverless applications.
Provides practical Zod v4 validation utilities and schema patterns for TypeScript applications. Use when designing validation layers for API payloads, forms, configuration, and domain input parsing with strong type inference.
Expert in Drizzle ORM for TypeScript — schema design, relational queries, migrations, and serverless database integration. Use when building type-safe database layers with Drizzle.
Use when you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. Part of the skills-for-java project
Refactor Nuxt.js/Vue code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, oversized components, deep nesting, SRP violations, data fetching anti-patterns with useFetch/useAsyncData/$fetch, poor composable organization, and mixed business/presentation logic. Applies Nuxt 3 patterns including auto-imports, proper data fetching, single-responsibility composables, TypeScript integration, runtime config, Nitro server routes, Nuxt Layers, middleware patterns, Pinia state management, and performance optimizations.