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 khalilbenaz/claude-skills-collection --skill data-pipeline-buildergit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote 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/khalilbenaz/claude-skills-collection/data-pipeline-builder)<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/data-pipeline-builder"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/data-pipeline-builder/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/khalilbenaz/claude-skills-collection/data-pipeline-builder"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/data-pipeline-builder.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 | $0.00074 | $0.02336 |
| Opus 5 | $0.00037 | $0.01168 |
| Sonnet 5 | $0.00015 | $0.00467 |
| Haiku 4.5 | $0.00007 | $0.00234 |
Grade B, and why
data-pipeline-builder scanned grade B with 2 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 9d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl -X POST http://localhost:8083/connectors -H 'Content-Type: application/json' -d '{ Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:8083/connectors -H 'Content-Type: application/json' -d '{ How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Builder
1. Analyser les sources et les besoins
Avant d'écrire une ligne de code, poser ces questions :
| Critère | Options | Impact |
|---|---|---|
| Volume | Ko–Mo / Go / To+ | Pandas vs Spark vs DuckDB |
| Fréquence | Batch horaire/journalier / Near-real-time / Streaming | Airflow vs Flink/Kafka |
| Fraîcheur acceptable | > 1h / 15 min / < 1 min | Batch / micro-batch / streaming pur |
| Qualité source | Propre / semi-structurée / dirty | Niveau de validation nécessaire |
| Format | CSV, JSON, Parquet, Avro, CDC | Connecteur et schéma evolution |
Règle : ne pas sur-architecturer. Un fichier CSV quotidien de 10 Mo → DuckDB + script Python, pas Spark.
2. Choisir l'architecture
Données fraîcheur > 1h → Batch (Airflow + dbt + Warehouse)
Fraîcheur 1–15 min → Micro-batch (Spark Structured Streaming / Flink)
Fraîcheur < 1 min → Streaming pur (Kafka + Flink / Kafka Streams)
Transformations lourdes → ELT (charger raw, transformer dans le warehouse)
Données sensibles/EDW → ETL (transformer avant chargement)
Architecture lambda = batch + streaming en parallèle → complexité élevée, préférer kappa (streaming seul avec replay) quand le streaming couvre tous les cas.
3. Ingestion
CDC avec Debezium (PostgreSQL → Kafka)
# docker-compose snippet
debezium:
image: debezium/connect:2.5
environment:
BOOTSTRAP_SERVERS: kafka:9092
GROUP_ID: debezium-group
CONFIG_STORAGE_TOPIC: debezium.configs
OFFSET_STORAGE_TOPIC: debezium.offsets
# Enregistrer un connecteur PostgreSQL
curl -X POST http://localhost:8083/connectors -H 'Content-Type: application/json' -d '{
"name": "pg-connector",
"config": {
"connector.class": "io.debezium.connector.postgresql.PostgresConnector",
"database.hostname": "postgres",
"database.port": "5432",
"database.user": "replicator",
"database.password": "secret",
"database.dbname": "mydb",
"table.include.list": "public.orders",
"plugin.name": "pgoutput"
}
}'
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.
- 9d ago First seen · 277 lines · 74 tokens per session scan B 1040b110e892
data-pipeline-builder is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 19d ago), licensed MIT. It adds 74 tokens to every session and 2,336 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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