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Skills affaan-m ecc

data-throughput-accelerator

Use it when:
An ingestion, backfill, export, ETL job, warehouse load or table sync is too slow and must get faster without losing data.
It does:
Separates extraction, transfer, load, transform and freshness, measures the backlog, then benchmarks batch sizes, worker counts, warehouse SQL and file grouping.
You get:
The fastest correct path codified as a CLI, scheduled job, workflow or runbook, and an accounting block of files, rows, remaining tail and runtime.
Won't:
Delete raw data to improve a metric, skip failed files silently, or call a pipeline done before target tables and manifest agree.

TL;DR

ECC's skill for data that moves too slowly. Its goal is faster correct data in the right place, with proof. The agent first separates source extraction, network transfer, warehouse load, transform, serving-table freshness and the live tail still arriving while the job runs, since a fast pipeline can still look behind. It reads the source, target and manifest contracts, measures the backlog in files, manifest rows, raw and derived rows, timestamps and unprocessed counts, then runs a safe catch-up or sample benchmark. Variants compare batch size, worker count, warehouse SQL, file grouping, staging shape and manifest updates, guided by heuristics: move compute to the data, prefer warehouse-native scans and appends, skip completed files or partitions through manifests, batch small writes and make writes idempotent. Only a path that keeps counts and timestamps coherent is promoted, and the accounting is rerun after the codified path executes. For finance, healthcare, regulated or customer-impacting data it keeps replay evidence and approval gates.

Summarizes the skill at d2ef268.

npx skills add https://github.com/affaan-m/ecc --skill data-throughput-accelerator

skills.sh pageSource on GitHubAll skills from affaan-m/ecc

Total Downloads
5,200 on skills.sh, Nov 9, 2026
Repo stars
299,776 affaan-m/ecc
First seen
May 25, 2026 on skills.sh
Weekly Downloads
439
+69% over the 8 weeks to Nov 9
Weekly values
Week toInstalls
Sep 21259
Sep 28159
Oct 5142
Oct 12231
Oct 19411
Oct 26402
Nov 2465
Nov 9439
Category
category:debuggingcategory:software-engineering

Find root causes, review code and test what you built.

Everything for building software: planning, coding, debugging, design, frameworks, cloud, agents and skills.
Type
type:workflow

A multi-step process the agent follows: interview, debug, run a TDD loop, review.
Effect
effect:remote

Its own steps change something outside your machine: post, send, deploy, provision, file issues, drive a website, or upload data to a service.
Needs
needs:none

Instructions only. Works in any agent that supports skills.
Publisher
by:community

Everyone else. Unverified vendor claims default to community.
Topics
performancedatabasesverification

SKILL.md

From affaan-m/ecc at d2ef268. Checked daily, last changed Nov 6, 2026.

Frontmatter
name: data-throughput-accelerator
description: Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere. Use when a pipeline or backfill is too slow and must get faster without losing data correctness.
license: MIT
metadata:
  origin: ECC
tools: Read, Write, Edit, Bash, Grep, Glob

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

  1. Read the current source, target, and manifest contracts.
  2. Measure backlog: external files, manifest rows, raw rows, derived rows, min/max timestamps, and unprocessed counts.
  3. Run a safe catch-up or sample benchmark.
  4. Compare variants: batch size, worker count, warehouse SQL, file grouping, staging shape, and manifest update method.
  5. Promote only the fastest path that keeps counts and timestamps coherent.
  6. Codify the path as a CLI, scheduled job, workflow, or runbook.
  7. Rerun final accounting after the codified path executes.

Accounting Output

Use a hard accounting block:

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 match

Guardrails

  • 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.