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 creating-mermaid-dbt-daggit 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/creating-mermaid-dbt-dag)<a href="https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/creating-mermaid-dbt-dag"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/creating-mermaid-dbt-dag/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/creating-mermaid-dbt-dag"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/creating-mermaid-dbt-dag.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00053 | $0.00912 |
| Opus 5 | $0.00026 | $0.00456 |
| Sonnet 5 | $0.00011 | $0.00182 |
| Haiku 4.5 | $0.00005 | $0.00091 |
Grade A, and why
creating-mermaid-dbt-dag 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 13d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Mermaid Diagram in Markdown from dbt DAG
How to use this skill
Step 1: Determine the model name
- If name is provided, use that name
- If user is focused on a file, use that name
- If you don't know the model name: ask immediately — prompt the user to specify it
- If the user needs to know what models are available, query the list of models
- Ask the user if they want to include tests in the diagram (if not specified)
Step 2: Fetch the dbt model lineage (hierarchical approach)
Follow this hierarchy. Use the first available method:
-
Primary: Use get_lineage_dev MCP tool (if available)
- See using-get-lineage-dev.md for detailed instructions
- Preferred method — provides most accurate local lineage. If the user asks specifically for production lineage, this may not be suitable.
-
Fallback 1: Use get_lineage MCP tool (if get_lineage_dev not available)
- See using-get-lineage.md for detailed instructions
- Provides production lineage from dbt Cloud. If the user asks specifically for local lineage, this may not be suitable.
-
Fallback 2: Parse manifest.json (if no MCP tools available)
- See using-manifest-json.md for detailed instructions
- Works offline but requires manifest file
- Check file size first — if too large (>10MB), skip to next method
-
Last Resort: Parse code directly (if manifest.json too large or missing)
- See parsing-code-directly.md for detailed instructions
- Labor intensive but always works
- Provides best-effort incomplete lineage
Step 3: Generate the mermaid diagram
- Use the formatting guidelines below to create the diagram
- Include all nodes from the lineage (parents and children)
- Add appropriate colors based on node types
Step 4: Return the mermaid diagram
- Return the mermaid diagram in markdown format
- Include the legend
- If using fallback methods (manifest or code parsing), note any limitations
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.
- 13d ago First seen · 77 lines · 53 tokens per session scan A e87fa271fbcc
creating-mermaid-dbt-dag is a skill published in the GitHub repository dbt-labs/dbt-agent-skills (709 stars, last pushed today), licensed Apache-2.0. It adds 53 tokens to every session and 912 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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