Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add daffy0208/ai-dev-standards --skill data-engineergit clone --depth 1 https://github.com/daffy0208/ai-dev-standardsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/daffy0208/ai-dev-standards/data-engineer)<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/data-engineer"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/data-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/daffy0208/ai-dev-standards/data-engineer"><img src="https://agentmods.dev/badge/skills/daffy0208/ai-dev-standards/data-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.04563 |
| Opus 5.5 | $0.00006 | $0.01825 |
| Sonnet 5.5 | $0.00003 | $0.00913 |
| Haiku 4.5 | $0.00002 | $0.00456 |
Grade A, and why
data-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 870 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer Skill
I help you build robust data pipelines, ETL processes, and data infrastructure.
What I Do
Data Pipelines:
- Extract, Transform, Load (ETL) processes
- Data ingestion from multiple sources
- Batch and real-time processing
- Data quality validation
Data Infrastructure:
- Database schema design
- Data warehousing
- Caching strategies
- Data replication
Analytics:
- Data aggregation
- Metrics calculation
- Report generation
- Data export
ETL Patterns
Pattern 1: Simple ETL Pipeline
Use case: Daily sync from external API to database
// lib/etl/daily-sync.ts
interface RawCustomer {
id: string
full_name: string
email_address: string
signup_date: string
}
interface Customer {
id: string
name: string
email: string
signupDate: Date
}
export async function syncCustomers() {
console.log('Starting customer sync...')
// EXTRACT: Fetch data from external API
const response = await fetch('https://api.example.com/customers', {
headers: {
Authorization: `Bearer ${process.env.API_KEY}`
}
})
const rawCustomers: RawCustomer[] = await response.json()
console.log(`Extracted ${rawCustomers.length} customers`)
// TRANSFORM: Clean and normalize data
const transformedCustomers: Customer[] = rawCustomers.map(raw => ({
id: raw.id,
name: raw.full_name.trim(),
email: raw.email_address.toLowerCase(),
signupDate: new Date(raw.signup_date)
}))
// LOAD: Insert into database
let inserted = 0
let updated = 0
for (const customer of transformedCustomers) {
const existing = await db.customers.findUnique({
where: { id: customer.id }
})
if (existing) {
await db.customers.update({
where: { id: customer.id },
data: customer
})
updated++
} else {
await db.customers.create({
data: customer
})
inserted++
}
}
console.log(`Sync complete: ${inserted} inserted, ${updated} updated`)
return { inserted, updated, total: transformedCustomers.length }
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 870 lines · 16 tokens per session scan A 873316a2549d
data-engineer is a skill published in the GitHub repository daffy0208/ai-dev-standards (38 stars, last pushed 10mo ago), licensed MIT. It adds 16 tokens to every session and 4,563 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
Data Pipeline Engineering
Guide for designing, building, and maintaining reliable data pipelines.
polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
data-engineer
Data engineering agent for ETL pipelines, data warehousing, and analytics.
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
okf-maintain
Create, update, and validate the OKF v0.1 knowledge bundle in operator-etl. Use when adding concepts, fixing frontmatter, or preparing a documentation release.
operator-extend
Extend Operator ETL — new sources, domains, gold SQL marts, gov contracts. Use when adding pipelines beyond the FOIA or orders demos.