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 agentmods add skills/entropy-data/dataproduct-builder-dbt/dataproduct-dbtnpx skills add entropy-data/dataproduct-builder-dbt --skill dataproduct-dbtgit clone --depth 1 https://github.com/entropy-data/dataproduct-builder-dbtWrote 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/entropy-data/dataproduct-builder-dbt/dataproduct-dbt)<a href="https://agentmods.dev/skills/entropy-data/dataproduct-builder-dbt/dataproduct-dbt"><img src="https://agentmods.dev/badge/skills/entropy-data/dataproduct-builder-dbt/dataproduct-dbt.svg" alt="Measured on agentmods" 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 | $0.00101 | $0.04317 |
| Opus 5 | $0.00051 | $0.02159 |
| Sonnet 5 | $0.00020 | $0.00863 |
| Haiku 4.5 | $0.00010 | $0.00432 |
Grade A, and why
dataproduct-dbt 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 5d 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 — 301 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build and run dbt transformations for a data product
dataproduct-implement generates the ends of the pipeline: input-port sources (from access agreements) and output-port models (from the data contract). This skill fills in the middle, the staging/ and intermediate/ layers, and runs dbt against the result. The conventions below are adapted from dbt's structure and materialization best practices, and are meant to be edited by the organization adopting this plugin.
When to use this vs. other skills
- No dbt project yet → run
dataproduct-bootstrapfirst. - No output-port models or input-port sources yet → run
dataproduct-implementfirst. - Auditing or reviewing an existing dbt project → use
/reviewor/simplify, not this skill. - Authoring or refactoring the middle of the DAG, or running dbt → this skill.
Conventions (the editable part)
These rules are the contract between this skill and the rest of the plugin. Organizations forking the plugin should treat this section as the place to encode their own style.
Layer responsibilities
| Layer | Purpose | Materialization | References | Naming |
|---|---|---|---|---|
models/input_ports/<op-id>.source.yaml |
External raw data, one file per active access agreement | n/a (source) | n/a | source name = <provider-dp-id>_<provider-op-id> |
models/staging/stg_<provider-dp-id>__<table>.sql |
One staging model per source table: rename, cast, light cleanup, dedup | view |
{{ source(...) }} only |
double underscore separates source from entity |
models/intermediate/int_<purpose>.sql |
Joins, aggregations, pivots, surrogate keys, single-purpose | view (or ephemeral if used once) |
{{ ref(stg_*) }} and {{ ref(int_*) }} only |
verb-based filename describing what it does |
models/output_ports/v1/<table>.sql |
Published, contract-governed tables | table (or incremental past the threshold below) |
{{ ref(int_*) }} or {{ ref(stg_*) }} |
table name from the ODCS contract's models: key |
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.
- 5d ago First seen · 301 lines · 101 tokens per session scan A 10ba4c42a83b
dataproduct-dbt is a skill published in the GitHub repository entropy-data/dataproduct-builder-dbt (12 stars, last pushed 3mo ago), licensed MIT. It adds 101 tokens to every session and 4,317 once invoked, about $0.0005 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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