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 agentmods add commands/engineerwithai/engineerwith-agents/data-pipelinegit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/commands/engineerwithai/engineerwith-agents/data-pipeline)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/data-pipeline"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/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 | $0.00000 | $0.01394 |
| Opus 5 | $0.00000 | $0.00697 |
| Sonnet 5 | $0.00000 | $0.00279 |
| Haiku 4.5 | $0.00000 | $0.00139 |
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 2d 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.
This is a copy
97% identical to data-pipeline — 29 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Architecture
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Requirements
$ARGUMENTS
Core Capabilities
- Design ETL/ELT, Lambda, Kappa, and Lakehouse architectures
- Implement batch and streaming data ingestion
- Build workflow orchestration with Airflow/Prefect
- Transform data using dbt and Spark
- Manage Delta Lake/Iceberg storage with ACID transactions
- Implement data quality frameworks (Great Expectations, dbt tests)
- Monitor pipelines with CloudWatch/Prometheus/Grafana
- Optimize costs through partitioning, lifecycle policies, and compute optimization
Instructions
1. Architecture Design
- Assess: sources, volume, latency requirements, targets
- Select pattern: ETL (transform before load), ELT (load then transform), Lambda (batch + speed layers), Kappa (stream-only), Lakehouse (unified)
- Design flow: sources → ingestion → processing → storage → serving
- Add observability touchpoints
2. Ingestion Implementation
Batch
- Incremental loading with watermark columns
- Retry logic with exponential backoff
- Schema validation and dead letter queue for invalid records
- Metadata tracking (_extracted_at, _source)
Streaming
- Kafka consumers with exactly-once semantics
- Manual offset commits within transactions
- Windowing for time-based aggregations
- Error handling and replay capability
3. Orchestration
Airflow
- Task groups for logical organization
- XCom for inter-task communication
- SLA monitoring and email alerts
- Incremental execution with execution_date
- Retry with exponential backoff
Prefect
- Task caching for idempotency
- Parallel execution with .submit()
- Artifacts for visibility
- Automatic retries with configurable delays
4. Transformation with dbt
- Staging layer: incremental materialization, deduplication, late-arriving data handling
- Marts layer: dimensional models, aggregations, business logic
- Tests: unique, not_null, relationships, accepted_values, custom data quality tests
- Sources: freshness checks, loaded_at_field tracking
- Incremental strategy: merge or delete+insert
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.
- 2d ago First seen · 187 lines · 0 tokens per session scan A 143c2d343680
data-pipeline is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,394 tokens. A static security scan graded it A with 0 findings. It is 97% identical to data-pipeline, differing in 29 lines, and is treated as a copy.
Other commands, from other repositories
doctor.es
Diagnostica problemas de inferencia LLM en Mac: asiai doctor verifica el estado de los motores, conflictos de puertos, carga de modelos y estado de la GPU.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
web-add
Ingest a single URL into the knowledge base.
laravel-ai-sdk
Build AI features with the first-party Laravel AI SDK (Laravel 13+); use the laravel:ai-sdk skill exactly as written.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.