ReddRadar
Topic: data-science
11 skills in this topic.
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wax
Comprehensive guidance for the Wax on-device memory/RAG framework. Use when integrating MemoryOrchestrator, VideoRAGOrchestrator, Wax/WaxSession, embedding providers, hybrid search, maintenance, or when evaluating Wax constraints like offline-only, single-file .wax persistence and deterministic retrieval.
christopherkarani/Wax 700
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wax-performance-audit
Benchmarking and performance auditing for the Wax repo. Use when running or interpreting Wax benchmarks, diagnosing CPU, memory, or I/O bottlenecks, or investigating Swift 6.2 concurrency issues such as Sendable, actor isolation, `@unchecked Sendable`, task-group fan-out, and data races.
christopherkarani/Wax 700
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dbt
Use when building dbt models, adding tests, or designing data models. Covers dimensional modeling, model organization (staging/intermediate/marts), testing patterns, and warehouse-specific configurations.
bbrewington/software-data-and-ai-tools 25
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service-design
Comprehensive service design methodology for creating sustainable solutions and optimal experiences for both customers and service providers. Use when designing end-to-end service experiences, creating customer journey maps, building service blueprints, mapping service ecosystems, identifying touchpoints and pain points, designing frontstage/backstage interactions, or improving existing service delivery. Applicable to digital services, physical services, and hybrid product-service systems.
bbrewington/software-data-and-ai-tools 25
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data-analysis
Patterns for data loading, exploration, and statistical analysis
Yeachan-Heo/My-Jogyo 162
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experiment-design
Best practices for designing reproducible experiments
Yeachan-Heo/My-Jogyo 162
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ml-rigor
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Yeachan-Heo/My-Jogyo 162
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data-analysis
Patterns for data loading, exploration, and statistical analysis
Yeachan-Heo/My-Jogyo 162
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experiment-design
Best practices for designing reproducible experiments
Yeachan-Heo/My-Jogyo 162
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ml-rigor
Enforces baseline comparisons, cross-validation, interpretation, and leakage prevention for ML pipelines
Yeachan-Heo/My-Jogyo 162
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galaxy-docker
bgruening/docker-galaxy 236