重要前提
安装AI Skills的关键前提是:必须科学上网,且开启TUN模式,这一点至关重要,直接决定安装能否顺利完成,在此郑重提醒三遍:科学上网,科学上网,科学上网。查看完整安装教程 →
data-lake-platform by vasilyu1983/ai-agents-public
npx skills add https://github.com/vasilyu1983/ai-agents-public --skill data-lake-platform构建和运营生产级数据湖与湖仓一体平台:可靠地完成数据摄取、转换、以开放格式存储并提供分析服务。
references/storage-formats.md(使用 assets/cross-platform/template-schema-evolution.md 和 assets/cross-platform/template-partitioning-strategy.md)广告位招租
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触达数万 AI 开发者,精准高效
references/ingestion-patterns.md(使用 assets/cross-platform/template-ingestion-governance-checklist.md 和 assets/cross-platform/template-incremental-loading.md)references/transformation-patterns.md(使用 assets/cross-platform/template-data-pipeline.md)references/query-engine-patterns.mdreferences/governance-catalog.md(使用 assets/cross-platform/template-data-quality-governance.md 和 assets/cross-platform/template-data-quality.md)references/operational-playbook.md 和 references/cost-optimization.md(使用 assets/cross-platform/template-data-quality-backfill-runbook.md 和 assets/cross-platform/template-cost-optimization.md)references/architecture-patterns.mdreferences/architecture-patterns.mdreferences/streaming-patterns.mdpip install "dlt[clickhouse]"
dlt init rest_api clickhouse
python pipeline.py
pip install sqlmesh
sqlmesh init duckdb
sqlmesh plan && sqlmesh run
| 资源 | 用途 |
|---|---|
| references/architecture-patterns.md | 奖章架构、数据网格 |
| references/ingestion-patterns.md | dlt vs Airbyte、CDC |
| references/transformation-patterns.md | SQLMesh vs dbt |
| references/storage-formats.md | Iceberg vs Delta |
| references/query-engine-patterns.md | ClickHouse、DuckDB |
| references/streaming-patterns.md | Kafka、Flink |
| references/orchestration-patterns.md | Dagster、Airflow |
| references/bi-visualization-patterns.md | Metabase、Superset |
| references/cost-optimization.md | 成本杠杆与维护 |
| references/operational-playbook.md | 监控与事件响应 |
| references/governance-catalog.md | 目录、血缘、访问控制 |
| references/data-mesh-patterns.md | 领域所有权、数据产品、联邦治理 |
| references/data-quality-patterns.md | 质量关卡、验证框架、SLO、异常检测 |
| references/security-access-patterns.md | 行/列安全、加密、审计日志、合规性 |
| 技能 | 用途 |
|---|---|
| ai-mlops | 机器学习部署 |
| ai-ml-data-science | 特征工程 |
| data-sql-optimization | OLTP 优化 |
每周安装次数
70
代码仓库
GitHub 星标数
53
首次出现
2026年1月23日
安全审计
安装于
cursor57
codex55
gemini-cli54
opencode54
github-copilot50
claude-code48
Build and operate production data lakes and lakehouses: ingest, transform, store in open formats, and serve analytics reliably.
references/storage-formats.md (use assets/cross-platform/template-schema-evolution.md and assets/cross-platform/template-partitioning-strategy.md)references/ingestion-patterns.md (use assets/cross-platform/template-ingestion-governance-checklist.md and assets/cross-platform/template-incremental-loading.md)references/transformation-patterns.md (use assets/cross-platform/template-data-pipeline.md)references/query-engine-patterns.mdreferences/governance-catalog.md (use assets/cross-platform/template-data-quality-governance.md and assets/cross-platform/template-data-quality.md)references/operational-playbook.md and references/cost-optimization.md (use assets/cross-platform/template-data-quality-backfill-runbook.md and assets/cross-platform/template-cost-optimization.md)references/architecture-patterns.mdreferences/architecture-patterns.mdreferences/streaming-patterns.mdpip install "dlt[clickhouse]"
dlt init rest_api clickhouse
python pipeline.py
pip install sqlmesh
sqlmesh init duckdb
sqlmesh plan && sqlmesh run
| Resource | Purpose |
|---|---|
| references/architecture-patterns.md | Medallion, data mesh |
| references/ingestion-patterns.md | dlt vs Airbyte, CDC |
| references/transformation-patterns.md | SQLMesh vs dbt |
| references/storage-formats.md | Iceberg vs Delta |
| references/query-engine-patterns.md | ClickHouse, DuckDB |
| references/streaming-patterns.md | Kafka, Flink |
| Template | Purpose |
|---|---|
| assets/cross-platform/template-medallion-architecture.md | Baseline bronze/silver/gold plan |
| assets/cross-platform/template-data-pipeline.md | End-to-end pipeline skeleton |
| assets/cross-platform/template-ingestion-governance-checklist.md | Source onboarding checklist |
| assets/cross-platform/template-incremental-loading.md | Incremental + backfill plan |
| assets/cross-platform/template-schema-evolution.md | Schema change rules |
| assets/cross-platform/template-cost-optimization.md |
| Skill | Purpose |
|---|---|
| ai-mlops | ML deployment |
| ai-ml-data-science | Feature engineering |
| data-sql-optimization | OLTP optimization |
Weekly Installs
70
Repository
GitHub Stars
53
First Seen
Jan 23, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
cursor57
codex55
gemini-cli54
opencode54
github-copilot50
claude-code48
前端代码审计工具 - 自动化检测可访问性、性能、响应式设计、主题化与反模式
49,600 周安装
| Dagster, Airflow |
| references/bi-visualization-patterns.md | Metabase, Superset |
| references/cost-optimization.md | Cost levers and maintenance |
| references/operational-playbook.md | Monitoring and incident response |
| references/governance-catalog.md | Catalog, lineage, access control |
| references/data-mesh-patterns.md | Domain ownership, data products, federated governance |
| references/data-quality-patterns.md | Quality gates, validation frameworks, SLOs, anomaly detection |
| references/security-access-patterns.md | Row/column security, encryption, audit logging, compliance |
| Cost control checklist |
| assets/cross-platform/template-data-quality-governance.md | Quality contracts + SLOs |
| assets/cross-platform/template-data-quality-backfill-runbook.md | Backfill incident/runbook |