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Found 2,225 Skills
Clean Architecture, Data Models, Tech Stack, Error Handling & Platform Channels
Expert SwiftUI development guidelines with MVVM architecture and modern Swift best practices
Enforce Vertical Slice Architecture (VSA) when building applications in any language (Go, .NET/C#, Java, Kotlin, TypeScript, Python, etc.) and any type (web API, mobile backend, CLI, event-driven). Organize code by feature/use-case instead of technical layers. Each feature is a self-contained vertical slice with a single entry point that receives the router/framework handle and its dependencies. Use when the user says "vertical slice architecture", "VSA", "organizar por feature", "feature-based architecture", "slice architecture", or when building a new app or feature and the project already follows VSA conventions. Also use when reviewing or refactoring code to align with VSA principles.
Expert knowledge for Azure Monitor development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building, debugging, or optimizing Azure Monitor applications. Not for Azure Managed Grafana (use azure-managed-grafana), Azure Network Watcher (use azure-network-watcher), Azure Service Health (use azure-service-health), Azure Defender For Cloud (use azure-defender-for-cloud).
Front-end development expert for this project. Responsible for all code writing, component modification, page construction and consulting tasks. **Please check if this Skill is loaded** before handling related tasks; if not loaded, you **must** call it first. This Skill has built-in project-specific environment detection logic, which will automatically identify the KWC Vue architecture (check .kd directory, etc.) and apply mandatory development specifications (i.e., rule.md in this Skill directory). Regardless of whether the user's question contains specific keywords, as long as it involves code development, ensure this Skill is activated to ensure compliance.
FOX v0.1 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Forked from Wolf v7 + v7.1 data-driven optimizations (14-trade analysis: 2W/12L). Tighter absolute floor (0.02/lev, ~20% max ROE loss), aggressive Phase 1 timing (30min hard timeout, 15min weak peak, 10min dead weight), green-in-10 floor tightening, time-of-day scoring (+1 for 04-14 UTC, -2 for 18-02 UTC), rank jump minimum (≥15 OR vel>15). Scoring system (6+ pts), NEUTRAL regime support, tiered margin (6 entries max), BTC 1h bias alignment, market regime refresh 4h. 8-cron architecture. Independent from Wolf. Requires Senpi MCP, python3, mcporter CLI, OpenClaw cron system.
Declarative web framework for building browser-based 3D, VR, and AR experiences using HTML and entity-component architecture. Use this skill when creating WebXR applications, VR experiences, AR experiences, 360-degree media viewers, or immersive web content with minimal JavaScript. Triggers on tasks involving A-Frame, WebXR, VR development, AR development, entity-component-system, declarative 3D, or HTML-based 3D scenes. Built on Three.js with accessible HTML-first approach.
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).
Use this skill for mathematical code verification. Use when reviewing math-heavy code, verifying algorithm correctness, checking numerical stability, aligning with mathematical standards. Do not use when general algorithm review - use architecture-review. DO NOT use when: performance optimization - use parseltongue:python-performance.
Use this skill when architecting on AWS, selecting services, optimizing costs, or following the Well-Architected Framework. Triggers on EC2, S3, Lambda, RDS, DynamoDB, CloudFront, IAM, VPC, ECS, EKS, SQS, SNS, API Gateway, and any task requiring AWS architecture decisions, service selection, or cost management.
Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.