Borrowing it
Nothing to install: this file belongs to luanmorenommaciel/agentspec. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/luanmorenommaciel/agentspec/main/.claude/commands/data-engineering/pipeline.mdgit clone --depth 1 https://github.com/luanmorenommaciel/agentspecWrote 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/luanmorenommaciel/agentspec/pipeline)<a href="https://agentmods.dev/commands/luanmorenommaciel/agentspec/pipeline"><img src="https://agentmods.dev/badge/commands/luanmorenommaciel/agentspec/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 | $0.00015 | $0.00316 |
| Opus 5 | $0.00008 | $0.00158 |
| Sonnet 5 | $0.00003 | $0.00063 |
| Haiku 4.5 | $0.00002 | $0.00032 |
Grade A, and why
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 5d 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
Pipeline Command
Scaffold a data pipeline (Airflow, Dagster) with best-practice patterns
Usage
/pipeline <description-or-file>
Examples
/pipeline "Daily orders ETL from Postgres to Snowflake"
/pipeline "Kafka → staging → dbt → marts with hourly refresh"
/pipeline requirements/pipeline-spec.md
What This Command Does
- Invokes the pipeline-architect agent
- Analyzes your description or requirements file
- Loads KB patterns from
airflowanddbtdomains - Generates:
- DAG structure (Airflow or Dagster)
- Task definitions with dependencies
- Error handling and retry configuration
- Sensor/trigger patterns for scheduling
Agent Delegation
| Agent | Role |
|---|---|
pipeline-architect |
Primary — DAG design, task orchestration |
spark-engineer |
Escalation — when pipeline includes Spark jobs |
dbt-specialist |
Escalation — when pipeline includes dbt models |
KB Domains Used
airflow— DAG patterns, operators, sensorsdbt— model execution, incremental strategiesdata-quality— quality gates between pipeline stages
Output
The agent generates pipeline code files and a summary of the DAG structure with task dependencies.
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.
- 5d ago First seen · 54 lines · 15 tokens per session scan A 7facd84fca47
pipeline is a command published in the GitHub repository luanmorenommaciel/agentspec (245 stars, last pushed 3d ago), licensed MIT. It adds 15 tokens to every session and 316 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 2026-09-01.
Other commands, from other repositories
metrics-analysis
You are analyzing pull request metrics to identify opportunities for improving the development workflow.
ml-pipeline
Design and implement a complete ML pipeline for: $ARGUMENTS.
check-release-health
Summarize the CI health of an OpenShift release using live data from the openshift-ci-mcp server.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
prompt-create
Create a new prompt following ground rules.
vlm-ocr-evaluation
Run the vlm-ocr skill in its evaluate phase: compare candidate OCR systems against a stratified human-transcribed ground-truth sample and pick a model on measured CER/WER before committing to a bulk run.