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Found 101 Skills
Expert patterns for Godot 3D lighting including DirectionalLight3D shadow cascades, OmniLight3D attenuation, SpotLight3D projectors, VoxelGI vs SDFGI, and LightmapGI baking. Use when implementing realistic 3D lighting, shadow optimization, global illumination, or light probes. Trigger keywords: DirectionalLight3D, OmniLight3D, SpotLight3D, shadow_enabled, directional_shadow_mode, directional_shadow_split, omni_range, omni_attenuation, spot_range, spot_angle, VoxelGI, SDFGI, LightmapGI, ReflectionProbe, Environment, WorldEnvironment.
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
Multi-cycle performance optimization with profiling and bottleneck analysis. Use when optimizing application performance.
Performance rules for query shape, aggregation strategy, and payload minimization.
Expert blueprint for low-level server access (RenderingServer, PhysicsServer2D/3D, NavigationServer) using RIDs for maximum performance. Bypasses scene tree overhead for procedural generation, particle systems, and voxel engines. Use when nodes are too slow OR managing thousands of objects. Keywords RenderingServer, PhysicsServer, NavigationServer, RID, canvas_item, body_create, low-level, performance.
Expert knowledge for Azure Database for MySQL development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying MySQL Flexible Server, tuning performance, configuring HA/networking, securing access, or integrating apps, and other Azure Database for MySQL related development tasks. Not for Azure Database for MariaDB (use azure-database-mariadb), Azure Database for PostgreSQL (use azure-database-postgresql), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance).
Expert knowledge for Azure Managed Lustre development including troubleshooting, best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when mounting AML, integrating with Blob auto-import/export, AKS CSI, quotas, or performance tuning, and other Azure Managed Lustre related development tasks. Not for Azure HPC Cache (use azure-hpc-cache), Azure NetApp Files (use azure-netapp-files), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Network (use azure-virtual-network).
Diagnose, compare, and optimize Apache Spark applications and SQL queries using Spark History Server data. Use this skill whenever the user wants to understand why a Spark app is slow, compare two benchmark runs or TPC-DS results, find performance bottlenecks (skew, GC pressure, shuffle spill, straggler tasks), get tuning recommendations, or optimize Spark/Gluten configurations. Also trigger when the user mentions 'diagnose', 'compare runs', 'why is this query slow', 'tune my Spark job', 'benchmark comparison', 'performance regression', or asks about executor skew, shuffle overhead, AQE effectiveness, or Gluten offloading issues.
Use when app feels slow, memory grows, battery drains, or diagnosing ANY performance issue. Covers memory leaks, profiling, Instruments workflows, retain cycles, performance optimization.
End-to-end SGLang SOTA performance workflow. Use when a user names an LLM model and wants SGLang to match or beat the best observed vLLM and TensorRT-LLM serving performance by searching each framework's best deployment command, benchmarking them fairly, profiling SGLang if it is slower, identifying kernel/overlap/fusion bottlenecks, patching SGLang code, and revalidating with real model runs.
Oracle Database skills for administration, SQL and PL/SQL development, performance tuning, security, ORDS, SQLcl, migrations, frameworks, Oracle Container Registry guidance, and agent-safe database workflows.
Reviews Forge apps for security vulnerabilities, architecture issues, cost inefficiencies, performance problems, and trigger/scheduling waste before deployment. Use when the user says "review my Forge app", "check my app", "pre-deploy check", "is my app ready to deploy", "audit my Forge app", "check for security issues", "check performance", "review manifest", "check my Forge app for problems", "app review", "optimize my Forge app costs", "reduce invocations", "why is my app expensive", "check my triggers", or any request to evaluate a Forge app's quality, safety, cost efficiency, or readiness. Also triggers when users ask about Forge best practices, permission scopes, resolver optimization, storage efficiency, cold start reduction, frontend offloading, trigger filtering, scheduled trigger frequency, N+1 API calls, bulk API usage, verbose logging, or Forge platform pricing.