Accelerates large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table sync while maintaining correctness. Focuses on bottleneck isolation, safe benchmarking, and auditable throughput gains.
How this skill is triggered — by the user, by Claude, or both
Slash command
/everything-claude-code:data-throughput-acceleratorThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
当瓶颈在于移动、转换或保存大量数据时使用此技能。目标不仅仅是速度。目标是更快地将正确的数据落在正确的位置并有证明。
当瓶颈在于移动、转换或保存大量数据时使用此技能。目标不仅仅是速度。目标是更快地将正确的数据落在正确的位置并有证明。
在优化之前先区分这些:
一个管道可能"很快",但如果新数据到达速度快于最终追赶窗口,它仍然可能显得落后。
使用硬核算块:
数据吞吐结果:
- 发现的源文件:294
- 本次运行处理的文件:294
- 新增原始行数:9,683,598
- 新增派生行数:8,917,585
- 剩余尾部:回读时 24 个文件
- 运行时间:38.7s
- 正确性关卡:清单计数和表最大时间戳匹配
npx claudepluginhub aaione/everything-claude-code-zhAccelerates large-scale data ingestion, backfill, ETL, and warehouse loading while preserving correctness. Use when data movement is the bottleneck.
Designs data pipelines and ETL processes covering extraction, transformation, loading, data quality checks, orchestration, and patterns for batch, streaming, CDC, ELT. Useful for building pipelines, data flows, syncing, or moving data between systems.
Orchestrates production ETL patterns by routing to reliability features like idempotency, checkpointing, retries and incremental strategies like timestamp loads, CDC, backfills.