사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/affaan-m/ecc --skill data-throughput-accelerator시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Data Throughput Accelerator
Use this skill when the bottleneck is moving, transforming, or saving lots of data. The goal is not just speed. The goal is faster correct data landing in the right place with proof.
First Distinction
Separate these before optimizing:
- source extraction speed;
- network transfer speed;
- warehouse/load speed;
- transform speed;
- serving-table freshness;
- live tail growth while the job runs.
A pipeline can be "fast" and still appear behind if new data arrives faster than the final catch-up window.
Fast Path Heuristics
- Move compute to where the data already is.
- Prefer warehouse-native scans, joins, and appends for large landed files.
- Use manifests or checkpoints so completed files/partitions are skipped.
- Use partitioning and clustering that match the read and append pattern.
- Batch small files, requests, and writes.
- Make writes idempotent through unique keys, manifests, or replaceable staging.
- Keep raw, derived, and serving tables separately accountable.
Workflow
- Read the current source, target, and manifest contracts.
- Measure backlog: external files, manifest rows, raw rows, derived rows,
min/max timestamps, and unprocessed counts.
- Run a safe catch-up or sample benchmark.
- Compare variants: batch size, worker count, warehouse SQL, file grouping,
staging shape, and manifest update method.
- Promote only the fastest path that keeps counts and timestamps coherent.
- Codify the path as a CLI, scheduled job, workflow, or runbook.
- Rerun final accounting after the codified path executes.
Accounting Output
Use a hard accounting block:
text
Data throughput result:
- Source files discovered: 294
- Files processed this run: 294
- Raw rows added: 9,683,598
- Derived rows added: 8,917,585
- Remaining tail: 24 files at readback time
- Runtime: 38.7s
- Correctness gate: manifest counts and table max timestamps matchGuardrails
- Do not delete raw data to make a metric look better.
- Do not skip failed files silently.
- Do not mix historical backfill status with live-tail freshness.
- Do not call a pipeline complete until the target tables and manifest agree.
- For finance, healthcare, regulated, or customer-impacting data, preserve
replay evidence and approval gates.
