performance
Performance & Optimization - Atoll Tourisme. Use when optimizing performance or profiling code.
Performance & Optimization - Atoll Tourisme. Use when optimizing performance or profiling code.
Rendimiento & Optimización - Atoll Tourisme. Use when optimizing performance or profiling code.
Performance & Optimierung - Atoll Tourisme. Use when optimizing performance or profiling code.
Python API v2 for programming Opentrons OT-2 and Flex liquid handling robots. Write protocols as Python files with metadata and a run() function; control pipettes, labware, and hardware modules (thermocycler, heater-shaker, magnetic, temperature). Simulate locally with opentrons_simulate, then upload to the robot app. Use PyLabRobot instead for hardware-agnostic scripts that run on Hamilton, Tecan, or other vendors.
Opentrons Protocol API v2 for OT-2 and Flex liquid handling robots. Write Python protocols for automated pipetting, serial dilutions, PCR setup, plate replication. Control hardware modules (thermocycler, heater-shaker, magnetic, temperature). For multi-vendor lab automation use pylabrobot.
PyTorch Geometric (PyG) for graph neural networks. Node classification, graph classification, link prediction with GCN, GAT, GraphSAGE, GIN layers. Message passing framework, mini-batch processing, heterogeneous graphs, neighbor sampling for large-scale learning, model explainability. Supports molecular property prediction (QM9, MoleculeNet), social networks, knowledge graphs, 3D point clouds. For non-graph deep learning use PyTorch directly; for traditional graph algorithms use NetworkX.
pymoo is a Python framework for single- and multi-objective optimization using evolutionary algorithms. Define problems as vectorized objective functions and constraints, then solve with NSGA-II, NSGA-III, MOEA/D, genetic algorithms, or differential evolution. Analyze Pareto fronts, visualize trade-off surfaces, and customize operators and callbacks. Ideal for engineering design, hyperparameter search, process optimization, and any problem with multiple conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient-based), platypus (fewer algorithms), jMetalPy (Java-based, more algorithms).
Upgrade shared Modal runtime dependencies in kernelbot and verify them end to end. Use when changing torch/CUDA or other shared Modal image dependencies, deploying the Modal app, and validating with both Modal integration tests and real popcorn leaderboard submissions.
Guide for implementing metacircular evaluators—interpreters that can interpret themselves. This skill should be used when building self-interpreting Scheme-like evaluators, debugging multi-level interpretation issues, or implementing language features like environments, closures, and special forms. Focuses on incremental development, continuous metacircular testing, and systematic debugging of nested interpretation failures.
This skill provides guidance for implementing custom compression encoders that must be compatible with existing decoders (especially arithmetic coding). It should be used when the task requires writing a compressor/encoder that produces output compatible with a given decompressor/decoder, or when implementing arithmetic coding or similar bit-level compression schemes.
Expert blueprint for performance profiling and optimization (frame drops, memory leaks, draw calls) using Godot Profiler, object pooling, visibility culling, and bottleneck identification. Use when diagnosing lag, optimizing for target FPS, or reducing memory usage. Keywords profiling, Godot Profiler, bottleneck, object pooling, VisibleOnScreenNotifier, draw calls, MultiMesh.
Provides Zig patterns for type-first development with tagged unions, explicit error sets, comptime validation, and memory management. Must use when reading or writing Zig files.
Implement memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, managing resources, or preventing memory bugs.
Add new core business logic functions to Catalyst-Relay. Use when creating pure functions, ADT operations, or library-consumable code.
Advanced TypeScript type system patterns for production codebases. [What: branded types for nominal typing, discriminated unions, template literal types, conditional types, the infer keyword, satisfies operator, const assertions, Zod schema inference, type-safe event emitters, exhaustive switch checking] [When: designing domain models, building type-safe APIs, creating reusable generic utilities, eliminating runtime bugs with compile-time guarantees, refactoring any-typed codebases] [Keywords: branded types, discriminated union, template literal types, conditional types, infer, satisfies, const assertion, Zod inference, exhaustive, mapped types, utility types, nominal typing, type narrowing, generic constraints] NOT for basic TypeScript syntax or React component typing (use a React-specific skill).
VGV-specific reference for bumping Dart and Flutter SDK constraints across packages. Use when upgrading the Flutter or Dart SDK version in any VGV repository — bumping pubspec.yaml environment constraints, updating CI workflow Flutter versions, or preparing an SDK upgrade PR. CI uses ^MAJOR.MINOR.x to resolve to the latest patch; pubspec pins the exact patch version (e.g., ^3.50.1). Trigger on phrases like "bump Flutter to 3.x", "update SDK constraints", "upgrade Dart SDK", "update CI Flutter version", "bump SDK version", or "prep the SDK upgrade PR".
Adding a new asset to snowboardkids2.yaml requires following specific steps to ensure assets are in the correct order and properly declared. Use this skill when you need to add a new binary asset.
Build or maintain a Codex-native self-improving memory loop using global `AGENTS.md`, a persistent memories directory, and optional nightly refinement automation. Use when Codex needs to adapt OpenClaw-style self-improvement ideas to Codex, set up long-term user/profile memory, create `PROFILE.md` / `ACTIVE.md` / `LEARNINGS.md` / `ERRORS.md` / `FEATURE_REQUESTS.md`, add promotion rules from raw learnings into active guidance, or create a recurring memory-refinement automation.
Apply mainframe dependency transformations to COBOL code using a pre-generated transformation guide. Converts CICS/VSAM constructs to standard COBOL.
This skill should be used when optimizing silu kernel on AMD GPUs.
Create a transformation guide for replacing mainframe-specific COBOL constructs with standard COBOL equivalents. Use when preparing COBOL code for local execution or migration.