Performance
Performance profiling and optimization
ortools
by tondevrel
Google Optimization Tools. An open-source software suite for optimization, specialized in vehicle routing, flows, integer and linear programming, and constraint programming. Features the world-class CP-SAT solver. Use for vehicle routing problems (VRP), scheduling, bin packing, knapsack problems, linear programming (LP), integer programming (MIP), network flows, constraint programming, combinatorial optimization, resource allocation, shift scheduling, job-shop scheduling, and discrete optimization problems.
front-end-checklist
by fabricioctelles
This skill should be used when a developer or team needs to review, validate, or audit front-end code before launching a website or HTML page to production. Triggers on requests such as "run the front-end checklist", "validate my front-end", "check my site before launch", or when the user asks for a structured review of HTML, CSS, JavaScript, accessibility, performance, security, or design quality.
performance
by 89jobrien
Comprehensive performance specialist covering analysis, optimization,
database-optimization
by 89jobrien
SQL query optimization and database performance specialist. Use when
golang-performance
by 89jobrien
Go performance optimization techniques including profiling with pprof,
/remember — 本地长期记忆
by atxinsky
保存本次会话的关键记忆到本地记忆库，下次开启会话时自动回忆
mcp-integration
by 89jobrien
Model Context Protocol (MCP) integration specialist. Use when creating
nextjs-architecture
by 89jobrien
Next.js architecture specialist. Use when designing Next.js applications,
prompt-optimization
by 89jobrien
Expert prompt optimization for LLMs and AI systems. Use when building
review-perf
by mgiovani
Perform comprehensive performance review analyzing database queries,
memory-protocol
by jwilger
File-based knowledge persistence patterns: when to store discoveries, when to recall past solutions, and how to organize project memory. Activate when starting tasks, encountering errors, making decisions, or when context may be lost between sessions.
resume-updater
by Aznatkoiny
Conversational skill that interviews the user to capture professional experience and build/update their career-profile.json. Use when the user says "update my resume", "add my recent experience", "refresh my profile", "capture my work history", "build my resume", "add a new job to my resume", or wants to create or modify their professional profile data. When to Use: - User wants to add new experience, skills, projects, or achievements - User wants to create their career profile from scratch - User wants to update existing profile entries - User mentions they changed jobs, got promoted, or completed a project When NOT to Use: - User wants to generate/format a resume (use /resume-generator command) - User wants career advice (use career-director agent) - User wants to search for jobs (use job-search agent)
cpp-reinforcement-learning
by Aznatkoiny
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
vite-react-best-practices
by claudiocebpaz
Comprehensive React and Vite SPA performance, architecture, and deployment guidelines. Use this skill when building, reviewing, or refactoring React applications built with Vite (SPA). Covers Vite-specific build configurations, static hosting requirements, and core React performance patterns.
rust-expert-best-practices-code-review
by wispbit-ai
Rust best practices and code quality guidelines for writing idiomatic, safe, and performant Rust code. This skill should be used when writing, reviewing, or refactoring Rust code. Triggers on tasks involving Rust programming, code review, error handling, type safety, or performance optimization.
postgresql-expert-best-practices-code-review
by wispbit-ai
PostgreSQL database design, migration, and performance optimization best practices. This skill should be used when writing, reviewing, or refactoring database schemas, migrations, or query patterns. Triggers on tasks involving PostgreSQL databases, schema design, migration optimization, or data modeling.
memory-integration
by troykelly
Use to maintain context across sessions - integrates episodic-memory for conversation recall and mcp__memory knowledge graph for persistent facts
engineering-review-framework
by derKlinke
Structured pre-implementation engineering review for code writing, implementation planning, and architecture decisions. Use when drafting or reviewing plans before code changes, or when the user asks for rigorous analysis of architecture, code quality, testing, and performance with explicit tradeoffs, opinionated recommendations, and user checkpoints.
ollama
by G1Joshi
Ollama local LLM deployment and management. Use for running LLMs locally.
ios-auto-layout-debugging
by derKlinke
Use when encountering "Unable to simultaneously satisfy constraints" errors, constraint conflicts, ambiguous layout warnings, or views positioned incorrectly - systematic debugging workflow for Auto Layout issues in iOS
pytorch
by G1Joshi
PyTorch deep learning framework with dynamic graphs. Use for neural networks.
debug:tensorflow
by SnakeO
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
spacy
by G1Joshi
spaCy NLP library with pipelines. Use for text processing.
debug:pytorch
by SnakeO
Debug PyTorch issues systematically. Use when encountering tensor errors, CUDA out of memory errors, gradient problems like NaN loss or exploding gradients, shape mismatches between layers, device conflicts between CPU and GPU, autograd graph issues, DataLoader problems, dtype mismatches, or training instabilities in deep learning workflows.