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-artifactsgit 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-artifacts)<a href="https://agentmods.dev/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-artifacts"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-artifacts/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-artifacts"><img src="https://agentmods.dev/badge/skills/sfc-gh-dflippo/snowflake-dbt-demo/dbt-artifacts.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 | $0.00060 | $0.03306 |
| Opus 5 | $0.00030 | $0.01653 |
| Sonnet 5 | $0.00012 | $0.00661 |
| Haiku 4.5 | $0.00006 | $0.00331 |
Grade A, and why
dbt-artifacts 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 — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt Artifacts Package - AI Instructions
Purpose
This skill enables AI agents to help users monitor dbt execution using the brooklyn-data/dbt_artifacts package. The package captures detailed execution metadata during dbt runs and stores it in queryable tables for analysis and monitoring.
When to Use This Skill
Activate this skill when users ask about:
- Tracking test and model execution history
- Analyzing dbt run patterns over time
- Monitoring data quality metrics from dbt tests
- Investigating dbt performance issues or slow models
- Setting up execution logging and observability
- Querying dbt execution metadata programmatically
- Comparing dbt monitoring approaches (Artifacts vs Event Tables)
What dbt Artifacts Does
The package captures one row per dbt artifact execution (models, tests, seeds, snapshots) with:
- Execution status: success, error, skipped, fail, warn
- Runtime metrics: Duration, rows affected, compile time
- Test results: Failure counts, error messages
- Run context: Command, environment, dbt version, threading
- Dependencies: Model lineage and relationships
Core Tables Reference
| Table | Purpose | Key Columns |
|---|---|---|
invocations |
One row per dbt run | command_invocation_id, dbt_command, target_name, run_started_at |
model_executions |
Model runtime performance | status, total_node_runtime, rows_affected, materialization |
test_executions |
Data quality tracking | status, failures, total_node_runtime, message |
seeds |
Seed file execution | Similar to model_executions |
snapshots |
Snapshot execution | Similar to model_executions with SCD tracking |
sources |
Source freshness | Freshness check results |
exposures |
Exposure execution | Exposure dependencies |
What ships with it
7 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 · 437 lines · 60 tokens per session scan A d6606a679701
dbt-artifacts 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 60 tokens to every session and 3,306 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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