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 dbt-labs/dbt-agent-skills --skill building-dbt-semantic-layergit clone --depth 1 https://github.com/dbt-labs/dbt-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/dbt-labs/dbt-agent-skills/building-dbt-semantic-layer)<a href="https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/building-dbt-semantic-layer"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/building-dbt-semantic-layer/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/dbt-labs/dbt-agent-skills/building-dbt-semantic-layer"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/building-dbt-semantic-layer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 45 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
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 | $0.00062 | $0.02467 |
| Opus 5 | $0.00031 | $0.01234 |
| Sonnet 5 | $0.00012 | $0.00493 |
| Haiku 4.5 | $0.00006 | $0.00247 |
Grade A, and why
building-dbt-semantic-layer 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- building-dbt-semantic-layer — 97% identical, 64 lines differ
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building the dbt Semantic Layer
This skill guides the creation and modification of dbt Semantic Layer components: semantic models, entities, dimensions, and metrics.
- Semantic models - Metadata configurations that define how dbt models map to business concepts
- Entities - Keys that identify the grain of your data and enable joins between semantic models
- Dimensions - Attributes used to filter or group metrics (categorical or time-based)
- Metrics - Business calculations defined on top of semantic models (e.g., revenue, order count)
Additional Resources
- Time Spine Setup - Required for time-based metrics and aggregations
- Best Practices - Design patterns and recommendations for semantic models and metrics
- Latest Spec Authoring Guide - Full YAML reference for dbt Core 1.12+ and Fusion
- Legacy Spec Authoring Guide - Full YAML reference for dbt Core 1.6-1.11
Determine Which Spec to Use
There are two versions of the Semantic Layer YAML spec:
- Latest spec - Semantic models are configured as metadata on dbt models. Simpler authoring. Supported by dbt Core 1.12+ and Fusion.
- Legacy spec - Semantic models are defined as separate top-level resources. Uses measures as building blocks for metrics. Supported by dbt Core 1.6 through 1.11. Also supported by Core 1.12+ for backwards compatibility.
Step 1: Check for Existing Semantic Layer Config
Look for existing semantic layer configuration in the project:
- Top-level
semantic_models:key in YAML files → legacy spec semantic_model:block nested under a model → latest spec
Step 2: Route Based on What You Found
If semantic layer already exists:
- Determine which spec is currently in use (legacy or latest)
- Check dbt version for compatibility:
- Legacy spec + Core 1.6-1.11 → Compatible. Use legacy spec guide.
- Legacy spec + Core 1.12+ or Fusion → Compatible, but offer to upgrade first using
uvx dbt-autofix deprecations --semantic-layeror the migration guide. They don't have to upgrade; continuing with legacy is fine. - Latest spec + Core 1.12+ or Fusion → Compatible. Use latest spec guide.
- Latest spec + Core <1.12 → Incompatible. Help them upgrade to dbt Core 1.12+.
What ships with it
4 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.
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 · 248 lines · 62 tokens per session scan A d45338208eb4
building-dbt-semantic-layer is a skill published in the GitHub repository dbt-labs/dbt-agent-skills (709 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 2,467 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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