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 sfc-gh-dflippo/snowflake-dbt-demo --skill dbt-materializationsgit clone --depth 1 https://github.com/sfc-gh-dflippo/snowflake-dbt-demoWrote 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/sfc-gh-dflippo/snowflake-dbt-demo/dbt-materializations)<a href="https://agentmods.dev/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-materializations"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-materializations/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/sfc-gh-dflippo/snowflake-dbt-demo/dbt-materializations"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-materializations.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 | $0.00061 | $0.03841 |
| Opus 5 | $0.00030 | $0.01920 |
| Sonnet 5 | $0.00012 | $0.00768 |
| Haiku 4.5 | $0.00006 | $0.00384 |
Grade A, and why
dbt-materializations 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 11d 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 — 694 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt Materializations
Purpose
Transform AI agents into experts on dbt materializations, providing guidance on choosing the right materialization strategy based on model purpose, size, update frequency, and query patterns, plus implementation details for each type including advanced features like snapshots and Python models.
When to Use This Skill
Activate this skill when users ask about:
- Choosing the right materialization for a model
- Implementing incremental models with merge/append/delete+insert strategies
- Setting up snapshots for SCD Type 2 historical tracking
- Converting table materializations to incremental
- Creating Python models for ML or advanced analytics
- Understanding trade-offs between ephemeral, view, and table
- Optimizing materialization performance
- Implementing slowly changing dimensions
Official dbt Documentation: Materializations
Decision Matrix
| Materialization | Use Case | Build Time | Storage | Query Speed | Best For |
|---|---|---|---|---|---|
| ephemeral | Staging, reusable logic | Fast (CTE) | None | N/A | Bronze layer |
| view | Simple transforms | Fast | Minimal | Slow | Always-fresh data |
| table | Complex logic | Slow | High | Fast | Dimensions |
| incremental | Large datasets | Fast | Medium | Fast | Large facts |
Ephemeral Materialization
When to Use: Staging models, reusable intermediate logic that doesn't need to be queried directly
{{ config(materialized='ephemeral') }}
select
customer_id,
customer_name,
upper(trim(email)) as email_clean
from {{ source('crm', 'customers') }}
How it Works:
- Compiled as CTE in downstream models
- No physical table created
- Zero storage cost
- Cannot be queried directly
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.
- 11d ago First seen · 694 lines · 61 tokens per session scan A 8881519d4d47
dbt-materializations is a skill published in the GitHub repository sfc-gh-dflippo/snowflake-dbt-demo (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 3,841 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 2026-08-30.
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