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 vaquarkhan/platform-engineering-agent-skills --skill dataops-and-rag-architecturesgit clone --depth 1 https://github.com/vaquarkhan/platform-engineering-agent-skillsWrote 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/vaquarkhan/platform-engineering-agent-skills/dataops-and-rag-architectures)<a href="https://agentmods.dev/skills/vaquarkhan/platform-engineering-agent-skills/dataops-and-rag-architectures"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/dataops-and-rag-architectures/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/vaquarkhan/platform-engineering-agent-skills/dataops-and-rag-architectures"><img src="https://agentmods.dev/badge/skills/vaquarkhan/platform-engineering-agent-skills/dataops-and-rag-architectures.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 | $0.00058 | $0.00574 |
| Opus 5 | $0.00029 | $0.00287 |
| Sonnet 5 | $0.00012 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
dataops-and-rag-architectures 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataOps & RAG Architectures
Core Competencies & Directives
Implement CI/CD for data pipelines utilizing lakeFS concepts. Write Python code that utilizes zero-clone branching for isolated dev/test data environments and automated hooks for pre-commit data validation.
When designing Retrieval-Augmented Generation (RAG) pipelines, favor deterministic ingestion and structured storage as the foundational layer, using vector databases strictly as a fuzzy recall fallback rather than a pure vector search approach.
When to Use
- designing lakeFS-style branch/merge workflows for datasets
- writing pre-commit data validation hooks
- building RAG ingestion with structured primary storage
- preventing context poisoning in agent retrieval layers
- isolating dev/test data environments without full copies
Workflow
- Branch isolation — model environments as lakeFS branches (
main,dev/{feature},test/{run}). - Pre-commit hooks — schema, volume, and contract checks before branch commit (see
hooks/data-validation-guard.sh). - Deterministic ingestion — normalize to structured tables/documents with stable IDs and provenance.
- Vector fallback — index embeddings only for fuzzy recall; primary answers come from structured lookup.
- Validate —
references/rag-deterministic-ingestion-checklist.md.
RAG Layer Model
Structured store (source of truth)
↓ deterministic lookup by ID / key / filter
Vector index (fuzzy recall fallback)
↓ rerank + provenance attach
Agent context (signed, TTL-bound)
Coding Constraints
- When writing Python, include comprehensive docstrings, static typing, and defensive error handling.
- Never output legacy, manual deployment scripts; everything must be "as code".
- Ensure multi-tenant data isolation with RBAC/ABAC on branches and buckets.
Anti-Patterns
- pure vector search with no structured canonical store
- shared dev/test branches without isolation
- ingestion without provenance metadata
- skipping pre-commit validation "for small datasets"
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.
- 10d ago First seen · 65 lines · 58 tokens per session scan A c7b531421638
dataops-and-rag-architectures is a skill published in the GitHub repository vaquarkhan/platform-engineering-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 574 once invoked, about $0.0003 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 2026-08-31.
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