data-engineering
ML data engineering covering data pipelines, data quality, collection strategies, storage, and versioning for machine learning systems.
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ML data engineering covering data pipelines, data quality, collection strategies, storage, and versioning for machine learning systems.
Applies 7 systematic thinking modes for breakthrough problem-solving and 10x improvements. Use when tackling complex problems, seeking transformative insights, or escaping conventional thinking traps.
Multi-layer caching with Redis, CDN, and browser caching for optimal application performance
Efficient AI techniques including model compression, quantization, pruning, knowledge distillation, and hardware-aware optimization for production systems.
Mutation testing with Stryker to verify test quality by introducing code mutations and measuring detection rates
Machine Learning Systems - comprehensive knowledge for building production ML systems from data engineering through deployment and operations. Based on Harvard ML Systems course and Designing ML Systems by Chip...
Python development with type hints, async patterns, testing, and modern best practices
AI agent creates structured implementation plans with task breakdown, dependency mapping, risk assessment, and file-level detail. Use when planning features, projects, or complex implementations.
Processes audio and video with ffmpeg including transcoding, streaming, and batch operations. Use when building video pipelines, converting formats, generating thumbnails, or implementing HLS/DASH streaming.
Comprehensive 4D testing methodology covering Accuracy, Performance, Security, and Accessibility for production-ready quality assurance
Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.
Agency-grade interface design with systematic aesthetic framework (BEAUTIFUL → RIGHT → SATISFYING → PEAK)
Claude writes comprehensive Python tests using pytest. Use when writing unit tests, creating fixtures, parametrizing tests, mocking dependencies, testing async code, or setting up CI test pipelines.
Deep learning foundations including neural network basics, backpropagation, optimization, regularization, and training best practices.
Claude builds immersive 3D web experiences with Three.js and React Three Fiber. Use when creating WebGL scenes, 3D animations, shaders, or physics simulations.
The agent enforces mandatory test completion before any task or feature can be marked as done, ensuring code quality through strict validation gates and evidence-based completion criteria.
Building robust AI systems including model monitoring, drift detection, reliability engineering, and failure handling for production ML.
Enterprise Ruby on Rails development with Active Record, API mode, testing, and production patterns
Create beautiful infographics based on the given text content. Use this when users request creating infographics.
Create, update, and translate Dify X Reveal.js slide decks in this repo. Use when asked to draft slides, extract outlines, or maintain bilingual CN/EN parity for the ctrip/paypal/pupu/milvus/aispeech/dentsply/o...
Fetch secrets from Doppler for API keys and credentials.
Execute automated release workflow for Rust projects using Conventional Emoji Commits. Analyze commits since last release, determine semantic version bump (MAJOR/MINOR/PATCH), update Cargo.toml, generate change...
Run Rust cargo tests properly in git worktrees with environment variable sourcing. Use when cargo test fails, when seeing authentication errors, when test results differ between runs, or before running pre-comm...
Manage git worktrees for parallel feature development following Langstar's issue-driven workflow. Create worktrees with correct target branches based on issue hierarchy, handle cleanup after PR merge, and maint...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。