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 vaquarkhan/data-engineering-agent-skills --skill dbt-and-analytics-engineeringgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-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/vaquarkhan/data-engineering-agent-skills/dbt-and-analytics-engineering)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/dbt-and-analytics-engineering"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/dbt-and-analytics-engineering/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/vaquarkhan/data-engineering-agent-skills/dbt-and-analytics-engineering"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/dbt-and-analytics-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00043 | $0.00510 |
| Opus 5 | $0.00022 | $0.00255 |
| Sonnet 5 | $0.00009 | $0.00102 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
dbt-and-analytics-engineering 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 9d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt And Analytics Engineering
Overview
Use this skill when the job is analytics engineering rather than raw ingestion. It helps agents build trustworthy dbt projects with clear layering, reusable models, tests, documentation, and publish-safe business definitions.
When to Use
- creating or changing
dbtmodels - building staging, intermediate, or mart layers
- adding tests, snapshots, or exposures
- organizing business logic for analysts and BI tools
- preparing semantic-layer-friendly outputs
Do not use this to justify putting ingestion or orchestration logic inside dbt.
Workflow
-
Confirm the model's role. Decide whether it belongs in:
- staging
- intermediate
- marts
- snapshot or semantic-serving layers
-
Define the business grain and contract. Capture:
- keys
- metric intent
- filter logic
- null handling
- freshness expectations
-
Add tests and documentation with the model. Typical checks:
- unique
- not null
- relationships
- accepted values
- source freshness where relevant
-
Keep model boundaries clean. Avoid mixing raw cleanup, business logic, and publish semantics in one model.
-
Validate downstream usability. Make sure the output is understandable to analysts, dashboards, and metric consumers.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It is only SQL, we do not need model structure." | Poor layering creates brittle marts and duplicate business logic. |
| "We can add tests after the dashboard is working." | Untested metrics create trust problems that are hard to unwind. |
| "A giant model is easier to maintain." | Monolithic models hide grain changes, assumptions, and reuse opportunities. |
Red Flags
- model grain is unclear
- business logic is duplicated across marts
- no YAML tests or documentation accompany the change
- publish models depend directly on raw sources without clear staging
Verification
- The model has a clear layer and business purpose
- Grain, keys, and metric assumptions are explicit
- Tests and documentation ship with the model
- Output usability for downstream consumers has been considered
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.
- 9d ago First seen · 74 lines · 43 tokens per session scan A e468310b64da
dbt-and-analytics-engineering is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (43 stars, last pushed 2mo ago), licensed MIT. It adds 43 tokens to every session and 510 once invoked, about $0.0002 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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