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-warehousegit 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-warehouse)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-warehouse"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-warehouse/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-warehouse"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-warehouse.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 | $0.00096 | $0.05887 |
| Opus 5 | $0.00048 | $0.02943 |
| Sonnet 5 | $0.00019 | $0.01177 |
| Haiku 4.5 | $0.00010 | $0.00589 |
Grade A, and why
data-data-warehouse 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 — 562 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Data Warehouse
Purpose
Design data warehouse schemas with dimensional modeling, platform-specific optimization, materialized views, and cost controls.
Agent Protocol
Trigger
Exact user phrases: "data warehouse", "Snowflake", "BigQuery", "Redshift", "star schema", "snowflake schema", "OLAP", "dimensional modeling", "fact table", "dimension table", "data warehouse design", "warehouse schema", "partition", "cluster", "materialized view", "warehouse optimization", "slowly changing dimension".
Input Context
Before activating, verify:
- Warehouse platform (Snowflake, BigQuery, Redshift, DuckDB)
- Data size and growth rate (TB scale, daily increment)
- Query patterns (dashboard, ad-hoc, ML feature extraction)
- Business domains (sales, marketing, finance, product)
- Compliance requirements (data retention, PII masking)
Output Artifact
Data warehouse design with schema, partition strategy, optimization plan as SQL and YAML.
Response Format
-- Fact table DDL
-- Dimension table DDL
-- Materialized view DDL
# Partition/cluster config
# Cost optimization rules
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Dimensional model with fact and dimension tables designed
- Slowly changing dimension strategy selected (SCD Type 2 default)
- Partitioning and clustering configured per table
- Materialized views defined for common query patterns
- Cost optimization rules configured
- Data retention and lifecycle policies set
Max Response Length
300 lines of SQL and configuration.
Warehouse Platforms
Snowflake
Snowflake is a fully-managed cloud data warehouse with separated compute and storage. Key features: auto-scaling warehouses (XS to 6XL), automatic clustering, zero-copy cloning, time travel (up to 90 days), data sharing, and Snowpark for Python/Java/Scala processing. Snowflake uses a columnar storage format with automatic micro-partitioning. Compute is billed per second while active; storage is billed per TB per month. Best for: organizations that want minimal operational overhead, need data sharing capabilities, or require multi-cloud support.
What ships with it
11 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/api_reference.md 604 B
- references/architecture-patterns.md 1.3 KB
- references/clickhouse-analytics.md 3.6 KB
- references/modeling-optimization.md 1.4 KB
- references/performance-optimization.md 1.2 KB
- references/timescaledb-druid.md 4.4 KB
- references/warehouse-cost-optimization.md 4.8 KB
- references/warehouse-data-sharing.md 2.9 KB
- references/warehouse-observability.md 3.4 KB
- references/warehouse-platforms.md 7.7 KB
- references/warehouse-security.md 1.3 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.
- 8d ago First seen · 562 lines · 96 tokens per session scan A 22d63ba7b644
data-data-warehouse is a skill published in the GitHub repository j4flmao/agent-skills (23 stars, last pushed 6d ago), licensed MIT. It adds 96 tokens to every session and 5,887 once invoked, about $0.0005 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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