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/ai-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/ai-pipeline)<a href="https://agentmods.dev/commands/luanmorenommaciel/agentspec/ai-pipeline"><img src="https://agentmods.dev/badge/commands/luanmorenommaciel/agentspec/ai-pipeline/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/commands/luanmorenommaciel/agentspec/ai-pipeline"><img src="https://agentmods.dev/badge/commands/luanmorenommaciel/agentspec/ai-pipeline.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 | $0.00019 | $0.00366 |
| Opus 5 | $0.00010 | $0.00183 |
| Sonnet 5 | $0.00004 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
ai-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 8d 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
AI Pipeline Command
Scaffold RAG pipelines, embedding workflows, feature stores, and text-to-SQL
Usage
/ai-pipeline <description-or-file>
Examples
/ai-pipeline "RAG pipeline for internal docs with pgvector"
/ai-pipeline "Embedding pipeline from S3 PDFs to Pinecone"
/ai-pipeline "Feature store setup with Feast for ML models"
/ai-pipeline "Text-to-SQL agent for analytics queries"
What This Command Does
- Invokes the ai-data-engineer agent
- Analyzes your AI/ML data requirements
- Loads KB patterns from
ai-data-engineeringandstreamingdomains - Generates:
- RAG pipeline architecture and code
- Embedding pipeline with chunking strategies
- Vector database setup and indexing
- Feature store definitions
- Text-to-SQL prompt templates
Agent Delegation
| Agent | Role |
|---|---|
ai-data-engineer |
Primary — RAG, embeddings, vector DBs, features |
streaming-engineer |
Escalation — real-time embedding pipelines |
data-quality-analyst |
Escalation — embedding quality metrics |
KB Domains Used
ai-data-engineering— RAG pipelines, vector databases, feature stores, LLMOpsstreaming— real-time embedding ingestiondata-quality— embedding quality, drift detection
Output
The agent generates pipeline code, configuration, and architecture documentation for your AI data workflow.
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.
- 8d ago First seen · 56 lines · 19 tokens per session scan A 250257840df8
ai-pipeline is a command published in the GitHub repository luanmorenommaciel/agentspec (246 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 366 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.
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ingest
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inference.embed
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vector.batch_exists
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vector.batch_upsert
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