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 justanesta/claude-code-resources --skill python-data-pipelinesgit clone --depth 1 https://github.com/justanesta/claude-code-resourcesWrote 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/justanesta/claude-code-resources/python-data-pipelines)<a href="https://agentmods.dev/skills/justanesta/claude-code-resources/python-data-pipelines"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/python-data-pipelines/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/justanesta/claude-code-resources/python-data-pipelines"><img src="https://agentmods.dev/badge/skills/justanesta/claude-code-resources/python-data-pipelines.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 | $0.00063 | $0.00864 |
| Opus 5.5 | $0.00025 | $0.00346 |
| Sonnet 5.5 | $0.00013 | $0.00173 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
python-data-pipelines scanned grade A with 1 finding 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 2mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(endpoint) How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Data Pipelines
Modern data pipeline orchestration patterns with Prefect and Airflow.
Decision Matrix: Prefect vs Airflow
| Factor | Prefect | Airflow | Winner |
|---|---|---|---|
| Learning curve | Gentler (Pythonic) | Steeper (DAG syntax) | Prefect |
| Dynamic workflows | Native | Requires workarounds | Prefect |
| Local development | Excellent | Harder | Prefect |
| Ecosystem maturity | Newer (2018) | Mature (2014) | Airflow |
General guidance:
- Use Prefect when: New projects, want Pythonic API, dynamic workflows
- Use Airflow when: Existing Airflow org, need battle-tested tool
Prefect Patterns
Basic Task and Flow
from prefect import task, flow
@task
def extract_data(source: str) -> list:
return fetch_from_api(source)
@task
def transform_data(data: list) -> list:
return [process_record(r) for r in data]
@flow(name="ETL Pipeline")
def etl_pipeline(source: str, destination: str):
raw = extract_data(source)
transformed = transform_data(raw)
load_data(transformed, destination)
Retries and Caching
from datetime import timedelta
from prefect.tasks import task_input_hash
@task(
retries=3,
retry_delay_seconds=60,
cache_key_fn=task_input_hash,
cache_expiration=timedelta(hours=1)
)
def unreliable_api_call(endpoint: str):
response = requests.get(endpoint)
response.raise_for_status()
return response.json()
See prefect-patterns.md for:
- Subflows
- Task results and artifacts
- Scheduling and deployment
Airflow Patterns
Basic DAG
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta
with DAG(
'etl_pipeline',
schedule_interval='@daily',
start_date=datetime(2024, 1, 1),
catchup=False
) as dag:
extract >> transform >> load
See airflow-patterns.md for:
- TaskFlow API (modern Airflow)
- Sensors for waiting
- Branch operators
- Dynamic task generation
What ships with it
4 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.
- 2mo ago First seen · 148 lines · 63 tokens per session scan A 77a7db5789f0
python-data-pipelines is a skill published in the GitHub repository justanesta/claude-code-resources (2 stars, last pushed 6mo ago), licensed MIT. It adds 63 tokens to every session and 864 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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