How long does the integration take?

Change one base URL in an OpenAI-compatible client, or install the Python or TypeScript SDK; the quickstart routes a first request in a few minutes.

Will this lock us into another vendor?

No. The router is Apache-2.0 open source, your provider keys stay yours, and the API is OpenAI-compatible, so leaving is a change of base URL.

What happens when a provider fails?

The router falls back to the next model your policy allows and records the fallback in the trace, so an outage costs a retry, not an incident.

data-throughput-accelerator - Skill

Name: data-throughput-accelerator
Author: affaan-m

Imported from affaan-m/ecc (skills/data-throughput-accelerator/SKILL.md) via skills.sh. Install upstream with npx skills add affaan-m/ecc --skill data-throughput-accelerator. Copyright stays with the author (MIT).

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:

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.

Install data-throughput-accelerator and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

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data-throughput-accelerator

About

Data Throughput Accelerator

First Distinction

Fast Path Heuristics

Workflow

Accounting Output

Guardrails

Install data-throughput-accelerator and it becomes one more candidate the router can pick - when it fits.

What it costs to adopt - in engineering time.

One base URL

No vendor lock-in

Failover built in

Use it

Manifest

Reviews