Build, run, and debug Minecraft Console Client (MCC) against a real local Minecraft Java server on Linux, macOS, or WSL. Use this whenever the user wants to compile MCC, start or inspect a local test server, connect MCC to a server, debug protocol or login issues, validate a code change end-to-end, or run MCC commands on a real server instead of guessing from static code.
Use when proving MCC behavior on a real local Minecraft server, validating runtime or protocol changes end-to-end, exercising movement, physics, inventory, entity, chat, or terrain behavior, or running a single-version or cross-version regression sweep.
create, repair, compress, and optimize prompts, system messages, tool instructions, schemas, and eval rubrics for general tasks across writing, research, coding, analysis, planning, tutoring, automation, and agent workflows. use when the user wants a new prompt, wants an existing prompt improved, wants prompt failures debugged, or needs better structure for grounding, tool use, output format, or reliability.
Adapt MCC palettes and protocol handling for a new Minecraft version. Use when the user wants to add support for a new MC version, compare version registries, update item/entity/block/metadata palettes, or fix protocol mismatches between MC versions.
C# 14 / .NET 10 coding conventions, idiomatic patterns, and performance best practices for the Minecraft Console Client codebase. Use when writing, reviewing, or modifying C# code.
Use when diagnosing or optimizing generic C#/.NET performance, GC pressure, allocations, heap or stack usage, LINQ overhead, boxing, Span/Memory, stackalloc, pooling, or hot-path code with CLI-first tools such as dotnet-counters, dotnet-trace, dotnet-stack, dotnet-gcdump, dotnet-dump, or BenchmarkDotNet.
Use when optimizing C# code in MCC, reducing GC pressure, profiling hot paths, fixing latency spikes, or reviewing code for allocation or throughput issues.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Use this skill when writing documentation for MCC.