浏览 colingwuyu 维护的已发布 AI 技能，比较用途与说明，并通过来源仓库了解安装方式。
已收录技能
13收录技能的仓库 Stars 合计
0仓库 Forks
0平均每个技能的仓库 Stars
0同一仓库中的多个技能可能共享 Stars，合计包含重复仓库数据。
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...
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。