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 ihatesea69/kiro-kit --skill data-engineeringgit clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/ihatesea69/kiro-kit/data-engineering)<a href="https://agentmods.dev/skills/ihatesea69/kiro-kit/data-engineering"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/data-engineering/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/ihatesea69/kiro-kit/data-engineering"><img src="https://agentmods.dev/badge/skills/ihatesea69/kiro-kit/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00030 | $0.00295 |
| Opus 5 | $0.00015 | $0.00148 |
| Sonnet 5 | $0.00006 | $0.00059 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
data-engineering 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 7d 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.
What it actually says
Data Engineering
Activate this skill when designing data pipelines or working with data infrastructure.
When to Use
- Building ETL/ELT pipelines
- Designing data warehouse schemas
- Implementing streaming data processing
- Optimizing data storage and retrieval
- Setting up data quality checks
Core Tools
- Apache Airflow: Workflow orchestration
- dbt: SQL-based transformations
- Apache Spark/PySpark: Distributed processing
- DVC: Data version control
- Great Expectations: Data validation
Patterns
# Airflow DAG pattern
from airflow import DAG
from airflow.operators.python import PythonOperator
with DAG("etl_pipeline", schedule="@daily") as dag:
extract = PythonOperator(task_id="extract", python_callable=extract_fn)
transform = PythonOperator(task_id="transform", python_callable=transform_fn)
load = PythonOperator(task_id="load", python_callable=load_fn)
extract >> transform >> load
Rules
- Idempotent operations (safe to re-run)
- Schema validation at pipeline boundaries
- Incremental processing over full reloads when possible
- Monitor data freshness and quality metrics
- Version control data schemas alongside code
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.
- 7d ago First seen · 48 lines · 30 tokens per session scan A 1dd9c1e7f648
data-engineering is a skill published in the GitHub repository ihatesea69/kiro-kit (18 stars, last pushed 22d ago), licensed MIT. It adds 30 tokens to every session and 295 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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goal-test
A local experiment for testing a goal command that keeps an AI coding session working until a stated condition is judged complete. It uses a separate language model to evaluate the conversation after each assistant turn.
schliff
Deterministic linter and scorer for instruction files — SKILL.md, AGENTS.md, CLAUDE.md. No model in the loop: the same bytes score the same everywhere. Use for linting, scoring, auditing or CI-gating an instruction file, and for checking whether the commands a file promises actually resolve in the repo. Trigger…