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Found 470 Skills
Expert-level Rust performance optimization guidelines for build profiles, allocation, synchronization, async/await, and I/O. This skill should be used when writing, reviewing, or optimizing Rust code for performance. Triggers on tasks involving slow Rust code, large binary size, long compile times, LTO configuration, release profile tuning, allocation reduction, clone avoidance, lock contention, BufReader/BufWriter, flamegraph analysis, async runtime issues, Tokio performance, spawn_blocking, parking_lot vs std sync, or any Rust performance investigation.
Best practices for Claude Code performance optimization, context management, storage cleanup, and troubleshooting slowdowns
Expert performance decisions for iOS/tvOS: when to optimize vs premature optimization, profiling tool selection, SwiftUI view identity trade-offs, and memory management strategies. Use when debugging performance issues, optimizing slow screens, or reducing memory usage. Trigger keywords: performance, Instruments, Time Profiler, Allocations, memory leak, view identity, lazy loading, @StateObject, retain cycle, image caching, faulting, batch operations
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.
Run parallel quality reviews (React, SOLID, Security, Simplification, Slop) on branch changes and auto-fix issues
Debugs and resolves common uv issues. Learn to diagnose dependency resolution failures, handle version conflicts, fix cache problems, troubleshoot Python environment issues, optimize performance, and solve platform-specific problems. Use when uv commands fail, dependencies won't resolve, cache is corrupted, Python installation issues occur, or performance is slow.
Use when starting a new Xiaohongshu account from zero, launching first content on fresh account, accelerating initial growth phase, reaching first 1000 followers, or overcoming slow start on new account
Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
Investigate incidents, debug performance issues, analyze logs, and manage observability resources in Dynatrace using the dtctl CLI. Use this skill whenever the user asks about error rates, latency spikes, service health, crash-looping pods, web vitals, SLO status, open problems, root cause analysis, log patterns, trace analysis, or building dashboards — even if they don't mention Dynatrace by name. Also covers DQL queries, workflow management, notebook and dashboard creation, settings configuration, and any operations against a Dynatrace environment.
Use when app feels slow, memory grows, battery drains, or diagnosing ANY performance issue. Covers memory leaks, profiling, Instruments workflows, retain cycles, performance optimization.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.