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/data-engineering-agent-skills --skill operational-datastore-selection-relational-and-nosqlgit clone --depth 1 https://github.com/vaquarkhan/data-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/data-engineering-agent-skills/operational-datastore-selection-relational-and-nosql)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/operational-datastore-selection-relational-and-nosql"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/operational-datastore-selection-relational-and-nosql/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/data-engineering-agent-skills/operational-datastore-selection-relational-and-nosql"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/operational-datastore-selection-relational-and-nosql.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 | $0.00061 | $0.00743 |
| Opus 5 | $0.00030 | $0.00371 |
| Sonnet 5 | $0.00012 | $0.00149 |
| Haiku 4.5 | $0.00006 | $0.00074 |
Grade A, and why
operational-datastore-selection-relational-and-nosql 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Operational Datastore Selection Relational And NoSQL
Overview
Use this skill when the real decision is not warehouse modeling, but what operational datastore should hold state, serve requests, or back data-driven application flows. It helps agents choose intentionally between relational systems such as MySQL and PostgreSQL versus NoSQL families such as document, key-value, wide-column, or cache-oriented stores.
When to Use
- choosing
MySQLversusNoSQLfor application-facing or service-facing data - selecting stores for metadata, idempotency keys, session state, event state, or operational APIs
- deciding whether flexible schema or strict relational constraints matter more
- evaluating consistency, scale, and access-pattern trade-offs
- preventing analytics or pipeline systems from being misused as OLTP stores
Do not treat datastore selection as a brand preference or trend decision.
Workflow
-
Define the access pattern first. Clarify:
- read versus write ratio
- request latency expectations
- query shapes
- update frequency
- retention and archival behavior
-
Define integrity and consistency needs. Include:
- transactions
- joins and relational constraints
- uniqueness guarantees
- conflict handling
- tolerance for eventual consistency
-
Match the workload to the right store family. Common guidance:
- relational stores such as
MySQLfit well when transactions, constraints, and predictable relational queries matter - document stores fit when entity shapes vary and document-oriented retrieval dominates
- key-value stores fit when lookup by key is primary and access patterns are narrow
- wide-column stores fit when scale and partition-oriented access dominate
- caches should accelerate another source of truth, not silently replace one
- relational stores such as
-
Check downstream and operational impact. Consider:
- CDC and replication support
- backup and restore behavior
- schema evolution pain
- observability and operational maturity
- how analytics or warehouse ingestion will work
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 · 82 lines · 61 tokens per session scan A 81a24fcddec7
operational-datastore-selection-relational-and-nosql is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 743 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 2026-09-03.
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