Browse published AI skills maintained by colingwuyu. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
13Repository stars across indexed skills
0Repository forks
0Average repository stars per indexed skill
0Repository stars may repeat across skills from the same repository.
Guide for designing Instance resources in OptAIC. Use when creating DatasetInstance, SignalInstance, ExperimentInstance, ModelInstance, PortfolioOptimizerInstance, or BacktestInstance. Covers definition referen...
Follow these patterns when implementing quant domain resources like Dataset, Signal, Alpha, Portfolio, Strategy, Universe, Backtest, or MonitoringRun in OptAIC. Use for creating DB models, DTOs, services, and t...
Follow these patterns when implementing governance operations (copy, branch, transfer, promote, merge) in OptAIC. Covers artifact handling, RBAC mutations, lineage tracking, and activity emission.
Follow these patterns when implementing activity emission and audit logging in OptAIC. Use for emitting ActivityEnvelopes on mutations (create, update, delete, execute), designing payloads, and ensuring audit c...
Follow these patterns when implementing MLOps features in OptAIC. Use for ML model definitions (5-component structure), model instances, training/inference pipelines, model registry, and monitoring. Covers sign...
Guide for porting code from optaic-v0 to optaic-trading. Use when migrating DataAPI, pipelines, stores, accessors, operators, or expressions into the Resource/Activity architecture. Covers pattern mappings for...
Follow these patterns when extending the OptAIC Python SDK with new domain operations. Use for adding client methods for datasets, signals, portfolios, backtests, and other resources. Covers async/sync interfac...
Guide for designing Run resources in OptAIC. Use when creating PipelineRun, ExperimentRun, BacktestRun, PortfolioOptimizationRun, TrainingRun, InferenceRun, or MonitoringRun. Covers execution tracking, metrics,...
Guide for designing Definition resources in OptAIC (the "Law"). Use when creating PipelineDef, StoreDef, AccessorDef, OpDef, MLModuleDef, or PortfolioOptimizerDef. Covers interface_spec, input/output schemas, c...
Design patterns, rules, and procedures for developing GUI features in `apps/web`. Covers migration from `optaic-v0` legacy design and new `ApiClient` integration patterns.
Strategy and patterns for designing and implementing End-to-End scenario tests using the Python SDK. Focuses on full-stack verification and SDK usability.
Follow these patterns when implementing data pipelines, ETL, data ingestion, or data validation in OptAIC. Use for point-in-time (PIT) correctness, Arrow schemas, quality checks, and Prefect orchestration.
Follow these patterns when designing guardrails validation contracts in OptAIC. Use for signal bounds, dataset schemas, portfolio constraints, PIT validation, and other domain-specific validation rules. Covers...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.