271 skills · sorted by relevance
Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, SDK usage, and vector search. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations.
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Use when building high-performance APIs with django-bolt - routing, JWT auth, msgspec serializers, WebSockets, SSE streaming, file uploads, ORM patterns, runbolt deployment, or migrating from DRF
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Profiling strategy for audio CPU issues — xruns, spikes, and buffer underruns. Use when the user reports CPU overload, audio glitches under load, or performance regressions. Trigger on phrases like "I'm getting xruns", "my plugin causes CPU spikes", "dropouts under high voice count", "audio thread is too slow", "how do I profile my DSP". References audio-numerics-review for denormal slowdown and audio-dsp-review for lock contention.
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Create high-quality, secure, and performance-optimized Containerfiles (Dockerfiles) following best practices for multi-architecture builds, OpenShift/Kubernetes compatibility, and BuildKit cache optimization. Use when the user wants to: (1) create a new Containerfile or Dockerfile for any project (Python, Rust, Go, Node.js, .NET, or any language), (2) containerize an application with multi-stage builds, (3) optimize an existing Containerfile for security, performance, or image size, (4) review or improve container image build practices, (5) set up BuildKit cache mounts for package managers, (6) create OpenShift-compatible container images with non-root users and arbitrary UID support, (7) write a .dockerignore file, or (8) apply OCI LABEL standards.
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MemoryPack high-performance binary serialization for Unity including custom formatters, versioning, and performance patterns.
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Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).
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Implements concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.
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Use when performance budgets or SLAs exist, users or monitoring report slowness, Core Web Vitals fall below thresholds, a change may have caused a regression, or a feature handles large datasets or high traffic. Measure-first workflow: profile, fix the proven bottleneck, re-measure. Not for speculative optimization.
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Turn vague "what did I do?" into evidence-backed impact statements for performance reviews, self-reviews, promotion packets, and weekly updates. Uniquely mines Copilot CLI session logs to reconstruct forgotten work, plus git commits and GitHub PRs. Enforces a 3-part impact contract (action → result → evidence). Works standalone with zero dependencies. Trigger for: "brag", "log work", "what did I do", "backfill my work history", "performance review", "self-review", "self assessment", "write impact statement", "review prep", "promo packet", "promotion case", "weekly update", "status report", "accomplishments", "what did I ship", "I forgot to log my work", "summarize my work", "track my wins", "what should I highlight", "end of half", "career growth", "work journal", or any request to document, summarize, or organize work accomplishments.
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Premium frontend UI direction for immersive, high-performance web experiences with strong motion, typography, and visual systems. Use when building high-end landing pages, polished interactive components, or portfolio-style sites.
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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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Memory management, profiling, optimization techniques, and Instruments usage for high-performance iOS apps
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
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Master Julia 1.10+ with modern features, performance optimization, multiple dispatch, and production-ready practices. Expert in the Julia ecosystem including package management, scientific computing, and high-performance numerical code. Use PROACTIVELY for Julia development, optimization, or advanced Julia patterns.
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Build and harden high-performance APIs with strong security controls, endpoint budgets, rate limiting, and resilience patterns. Use when tasks involve API latency/throughput tuning, abuse resistance, authz hardening, or API release gates.
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Set up vLLM to serve large language models through an OpenAI-compatible API or process prompts in batches. The skill covers deployment, performance monitoring, and quantization, which reduces model memory requirements.
Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.
Review posts, saved content, notifications, and performance figures from a connected Threads account. Search accessible content and trends, inspect audience responses, adjust feed preferences, and publish text or media posts after explicit confirmation.
Plan HyperFrames videos so motion flows continuously between scenes. Match movement direction and speed across cuts, connect actions through visible causes, and verify scene boundaries while keeping each scene active through meaningful reveals or interactions.
Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.
Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.