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Found 39 Skills
Process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performance
Use when "training LLM", "finetuning", "RLHF", "distributed training", "DeepSpeed", "Accelerate", "PyTorch Lightning", "Ray Train", "TRL", "Unsloth", "LoRA training", "flash attention", "gradient checkpointing"
Analyze code performance, detect bottlenecks, suggest optimizations for algorithms, queries, and resource usage. Use when improving application performance or investigating slow code.
Design memory hierarchy with progressive loading for optimal context management. Use when organizing CLAUDE.md imports, implementing just-in-time context loading, or designing priming hierarchies for agents.
Validate and use CPU offloading in Megatron Bridge, including layer-level activation offloading and fractional optimizer state offloading with HybridDeviceOptimizer.
Reviews Unity code targeting Meta Quest and Horizon OS for performance issues, rendering best practices, and common VR pitfalls. Use during code review or when diagnosing Quest performance problems in Unity projects.
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。
Apple Silicon skill for M-series development and profiling. Use when leveraging unified memory, Metal Performance Shaders, Instruments profiling, sysctl hardware queries, Rosetta 2 behavior, or 16KB page size considerations. Activates on queries about Apple Silicon, unified memory, AMX, MPS, Instruments, Rosetta, or M-series page size.
Use when a Unity 6.3 game is slow or stutters: low fps, frame spikes, hitches when entering areas or on first effects, GC spikes, too many draw calls or SetPass calls, CPU vs GPU bound questions, Profiler, Profile Analyzer, Memory Profiler, Frame Debugger or Project Auditor work, GPU Resident Drawer or SRP Batcher setup, LOD and Mesh LOD, shader warm-up and variants, memory or VRAM budgets, IL2CPP and build size, or proving a fix with numbers from a player.
Measure, profile, optimize, and regression-test Rust performance across latency, throughput, CPU, allocations, memory, binary size, and compile time using representative workloads, Criterion, iai-callgrind, cargo-flamegraph, samply, DHAT, heap or pprof tools, cargo-bloat, cargo-llvm-lines, and build timings. Use when users report slow Rust code, high memory or CPU, large binaries, noisy benchmarks, tail-latency regressions, or need performance budgets and evidence-backed optimization.
Make code faster or find why it is slow, from measurements and not from guesses. Read this skill before you change code to make it faster or use less memory. Use it when the user reports slow code, high latency, or high memory use. Use it also to check a speed claim, or to compare the speed of two options. Use it to diagnose a slow or stuck process, a profile, or a trace file.
Performance diagnosis and optimization - MicroProfiler, Script Profiler, Developer Console, Scene Analysis, Luau heap and memory categories, memory leaks, network/replication cost, rendering and physics cost, streaming, native codegen, Parallel Luau with Actors. Use when a game lags, stutters, drops FPS, crashes on mobile, has high ping, or before optimizing hot code.