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Agent skill

Data Throughput Accelerator

by affaan-m in affaan-m/ECC

Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking…

MITAuto-check passedBusiness, Finance & HR

Install Data Throughput Accelerator

skills CLI
$ npx skills add affaan-m/ECC --skill data-throughput-accelerator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC data-throughput-accelerator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-throughput-accelerator .claude/skills/data-throughput-accelerator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
data-throughput-accelerator
GitHub stars
275k
Used in
1 other repo
Token cost
~707 tokens
SKILL.md length
289 words
Files
1
Skills in repo
645
Repo updated
First seen
Licence
MIT

At a glance

Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking…

  • Works in 7 steps: Read the current source, target, and… → Measure backlog: external files,… → Run a safe catch-up or sample benchmark. → …
  • Backfill is too slow and must get faster without losing data correctness
  • SKILL.md covers First Distinction, Fast Path Heuristics, Workflow and Accounting Output, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Data Throughput Accelerator is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 710 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Accounting and bookkeeping and Data pipelines and ETL. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Backfill is too slow and must get faster without losing data correctness
  • Tasks that involve Accounting and bookkeeping
  • Tasks that involve Data pipelines and ETL

Example prompts

  • “/data-throughput-accelerator”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Read the current source, target, and manifest contracts.
  2. Measure backlog: external files, manifest rows, raw rows, derived rows,
  3. Run a safe catch-up or sample benchmark.
  4. Compare variants: batch size, worker count, warehouse SQL, file grouping,
  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.

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Data Throughput Accelerator loads about 707 tokens when it runs. Until then it costs about 106 tokens; SKILL.md has 289 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~707

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 289 words, ~707 tokens.

Download SKILL.mdSave it as .claude/skills/data-throughput-accelerator/SKILL.md (or your agent's skills folder).
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:

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 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.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/data-throughput-accelerator of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Data Throughput Accelerator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Data Throughput Accelerator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Throughput Accelerator this skillaffaan-m/ECC275k1 repos~707Automated safety check: PassMIT
Navan Performance Tuningjeremylongshore/tons-of-skills-marketplace2.8k—~924Automated safety check: PassMIT
SQL Server Table Reconciliationgithub/awesome-copilot40k1 repos~1.4kAutomated safety check: PassMIT
Car Methodsfranklee16/academic-research-skills2231 repos~1.2kAutomated safety check: PassNone
Jae Tables Figuresfranklee16/academic-research-skills2231 repos~1kAutomated safety check: PassNone
Jar Data Analysisfranklee16/academic-research-skills2231 repos~1.3kAutomated safety check: PassNone

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Categories

Questions about Data Throughput Accelerator

What does Data Throughput Accelerator do?

Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking…. Data Throughput Accelerator is an agent skill from affaan-m/ECC. 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.

When should I use Data Throughput Accelerator?

Data Throughput Accelerator fits situations like: backfill is too slow and must get faster without losing data correctness; tasks that involve Accounting and bookkeeping; tasks that involve Data pipelines and ETL.

How do I install Data Throughput Accelerator in Claude Code?

Run `npx skills add affaan-m/ECC --skill data-throughput-accelerator -a claude-code`. Or copy the skill folder (skills/data-throughput-accelerator in affaan-m/ECC) into .claude/skills/data-throughput-accelerator in your project. Claude Code loads it when a task matches its description.

How do I install Data Throughput Accelerator in Codex?

Run `npx skills add affaan-m/ECC --skill data-throughput-accelerator -a codex`. Or copy the skill folder (skills/data-throughput-accelerator in affaan-m/ECC) into .agents/skills/data-throughput-accelerator in your project. Codex loads it when a task matches its description.

Can I use Data Throughput Accelerator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill data-throughput-accelerator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-throughput-accelerator, .gemini/skills/data-throughput-accelerator, .github/skills/data-throughput-accelerator and .opencode/skills/data-throughput-accelerator in your project.

What does Data Throughput Accelerator need to run?

SKILL.md names no scripts, command-line tools or credentials: Data Throughput Accelerator is instructions for the agent only.

Does Data Throughput Accelerator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Data Throughput Accelerator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Data Throughput Accelerator use?

Data Throughput Accelerator is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Data Throughput Accelerator use?

About 707 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Data Throughput Accelerator?

Skills that share tags, products or a category with Data Throughput Accelerator: Navan Performance Tuning (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), SQL Server Table Reconciliation (github/awesome-copilot, 40k stars), Car Methods (franklee16/academic-research-skills, 223 stars) and Jae Tables Figures (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Throughput Accelerator?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,023 GitHub stars. The repository holds 645 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.