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 j4flmao/agent-skills --skill data-virtualizationgit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/data-virtualization)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-virtualization"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-virtualization/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/j4flmao/agent-skills/data-virtualization"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-virtualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.05482 |
| Opus 5.5 | $0.00048 | $0.02193 |
| Sonnet 5.5 | $0.00024 | $0.01096 |
| Haiku 4.5 | $0.00012 | $0.00548 |
Grade A, and why
data-data-virtualization 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 1mo 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 — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Data Virtualization
Purpose
Design and deploy data virtualization with Trino/Presto for federated queries across data sources, with connector configuration, pushdown optimization, performance tuning, and enterprise features.
Agent Protocol
Trigger
Exact user phrases: "data virtualization", "Trino", "Presto", "Starburst", "Dremio", "query federation", "federated query", "cross-source join", "pushdown", "connector", "data lake query engine", "federated analytics".
Input Context
- Data sources to federate (databases, lakes, streaming)
- Query patterns and performance requirements
- Existing data infrastructure
- Data sizes and source locations
- Security and compliance needs
- Team expertise with query engines
- User personas and access patterns
- BI tool compatibility requirements
Output Artifact
Data virtualization architecture with engine selection (Trino/Starburst/Dremio), connector configuration for each data source, pushdown optimization rules, cross-source join strategy, and performance tuning guide.
Response Format
# Engine selection matrix
# Connector configurations
# Pushdown rules per source
# Cross-source join strategy
# Performance tuning parameters
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Query engine selected with rationale
- Connectors configured for all data sources
- Pushdown rules defined per connector and query type
- Cross-source join strategy documented with cost model
- Performance tuning parameters set (memory, concurrency, threads)
- Security configured (TLS, auth, RBAC)
- Monitoring dashboard for query performance
- Resource groups and query queues configured
Max Response Length
350 lines of configuration.
Workflow
Step 1: Select Engine
Engine Comparison Matrix
| Engine | Strengths | Weaknesses | Use Case |
|---|---|---|---|
| Trino | Open-source, broad connector support, large community | No built-in auth, no caching (vanilla) | Open-source federated query |
| Starburst | Enterprise Trino, data lake caching, built-in security, RBAC | License cost, vendor dependency | Enterprise with compliance needs |
| Dremio | Reflections (acceleration), BI-friendly, data lineage | Smaller connector ecosystem | BI optimization, self-service |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/federation-deployment.md 901 B
- references/federation-optimization.md 8.0 KB
- references/trino-architecture.md 5.3 KB
- references/virtualization-connectors.md 3.1 KB
- references/virtualization-cost-analysis.md 3.3 KB
- references/virtualization-platforms.md 4.1 KB
- references/virtualization-query-optimization.md 965 B
- references/virtualization-security.md 9.2 KB
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.
- 1mo ago First seen · 585 lines · 121 tokens per session scan A 953b449b5092
data-data-virtualization is a skill published in the GitHub repository j4flmao/agent-skills (33 stars, last pushed 10d ago), licensed MIT. It adds 121 tokens to every session and 5,482 once invoked, about $0.0005 per session on Opus 5.5. 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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