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 christopherlouet/claude-base --skill data-pipelinegit clone --depth 1 https://github.com/christopherlouet/claude-baseWrote 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/christopherlouet/claude-base/data-pipeline)<a href="https://agentmods.dev/skills/christopherlouet/claude-base/data-pipeline"><img src="https://agentmods.dev/badge/skills/christopherlouet/claude-base/data-pipeline.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00445 |
| Opus 5 | $0.00014 | $0.00222 |
| Sonnet 5 | $0.00006 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
data-pipeline 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 3d 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 Pipeline
ETL vs ELT
| Pattern | When to use |
|---|---|
| ETL | Complex transformation, sensitive data |
| ELT | Big data, cloud DW (BigQuery, Snowflake) |
Airflow DAG
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta
default_args = {
'owner': 'data-team',
'retries': 3,
'retry_delay': timedelta(minutes=5),
}
with DAG(
'daily_etl',
default_args=default_args,
schedule_interval='0 2 * * *',
start_date=datetime(2024, 1, 1),
catchup=False,
) as dag:
extract = PythonOperator(
task_id='extract',
python_callable=extract_from_source,
)
transform = PythonOperator(
task_id='transform',
python_callable=transform_data,
)
load = PythonOperator(
task_id='load',
python_callable=load_to_warehouse,
)
extract >> transform >> load
dbt Transformation
-- models/staging/stg_orders.sql
{{ config(materialized='view') }}
SELECT
id AS order_id,
customer_id,
order_date,
CAST(total AS DECIMAL(10,2)) AS total_amount
FROM {{ source('raw', 'orders') }}
WHERE order_date >= '2023-01-01'
Data Quality
def validate_data(df):
assert df['order_id'].is_unique, "Duplicate IDs"
assert df['amount'].ge(0).all(), "Negative amounts"
assert df['customer_id'].notna().all(), "Null customers"
What ships with it
1 file 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.
- 3d ago First seen · 86 lines · 28 tokens per session scan A d4e27c26f60c
data-pipeline is a skill published in the GitHub repository christopherlouet/claude-base (5 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 445 once invoked, about $0.0001 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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