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-bootstrapnpx skills add entropy-data/dataproduct-builder-dbt --skill dataproduct-bootstrapgit clone --depth 1 https://github.com/entropy-data/dataproduct-builder-dbtWhat 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 | $0.00121 | $0.04059 |
| Opus 5 | $0.00060 | $0.02030 |
| Sonnet 5 | $0.00024 | $0.00812 |
| Haiku 4.5 | $0.00012 | $0.00406 |
Grade A, and why
dataproduct-bootstrap 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 3d 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bootstrap a new dbt data product
Create a new dbt data product project that follows the Entropy Data conventions. This skill handles the greenfield case — empty directory, no dbt project yet. For an existing dbt project that just needs the Entropy Data layer, use the entropy-data-sync skill instead.
What this skill produces
After running, the directory contains:
.
├── dbt_project.yml
├── pyproject.toml
├── .gitignore
├── README.md
├── profiles.yml.example
├── models/
│ ├── input_ports/_models.yml
│ ├── staging/_models.yml
│ ├── intermediate/_models.yml
│ └── output_ports/v1/_models.yml
├── analyses/ # empty
├── macros/ # empty
├── seeds/ # empty
├── snapshots/ # empty
└── tests/ # empty
It then invokes entropy-data-sync to add <id>.odps.yaml, the output-port contract under models/output_ports/v1/<contract>.odcs.yaml, openlineage.yml, and .github/workflows/data-product.yml.
How to run this skill
${PLUGIN_ROOT}below refers to the root of this plugin — the directory that containsskills/. On Claude Code it is set automatically as${CLAUDE_PLUGIN_ROOT}— use that. On any other agent (Codex, Copilot CLI, etc.) it is unset; resolve it as../..relative to thisSKILL.mdfile's directory (i.e. the grandparent ofskills/<this-skill>/).
Plan announcement (before Step 1)
Before running Step 1, print this plan to the user verbatim:
Running dataproduct-bootstrap. I'll:
- Pre-checks: confirm the working directory is empty (greenfield only), then ask whether this is a brand-new data product or one that already has an ODPS draft in Entropy Data.
- Gather parameters. If you point me at an existing draft, I pull them from the fetched ODPS; otherwise I'll ask you in one batched question (data product id, team, platform, catalog/schema, table).
- Pick the dbt adapter and profile block for the chosen platform.
- Scaffold the dbt project (
dbt_project.yml,profiles.yml.example, model layout, README,.gitignore), and check whether the user's existing~/.dbt/profiles.ymlwould collide with the new profile.- Hand off to
entropy-data-syncfor the publishing layer (ODPS, ODCS, OpenLineage, GitHub Actions).- Summarize what was scaffolded and the next manual steps.
What ships with it
9 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.
- templates/.gitignore 68 B
- templates/dbt_project.yml 420 B
- templates/models/input_ports/_models.yml 770 B
- templates/models/intermediate/_models.yml 159 B
- templates/models/output_ports/v1/_models.yml 720 B
- templates/models/staging/_models.yml 259 B
- templates/profiles.yml.example 80 B
- templates/pyproject.toml 355 B
- templates/README.md 1.4 KB
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.
- 3d ago First seen · 196 lines · 121 tokens per session scan A aeb9e8699b8b
dataproduct-bootstrap is a skill published in the GitHub repository entropy-data/dataproduct-builder-dbt (12 stars, last pushed 3mo ago), licensed MIT. It adds 121 tokens to every session and 4,059 once invoked, about $0.0006 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.
Other skills, from other repositories
cognee-community
Use when the user needs something that ships outside cognee core — community database adapters (Qdrant, Milvus, Weaviate, Redis, Pinecone, FalkorDB, Memgraph, DuckDB, NetworkX, …), data-source connectors (Slack, Gmail, Notion, Confluence, Google Drive), custom tasks/pipelines/retrievers (Exa, ScrapeGraph, codify)…
bigquery-ai-ml
Skill for BigQuery AI and Machine Learning queries using standard SQL and AI. functions (preferred over dedicated tools).
data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
fabriciq-ontology-cli
Manages Fabric IQ Ontology items, including entity and relationship types, data bindings, and definition updates, plus schema, lineage, grounding, and graph-walk exploration. Use for ontology modelling and traversal. For natural-language questions over a Power BI report use fabriciq.
lineage-diff
Compare column-level lineage between two versions of a SQL query to show added, removed, and changed data flow edges.
dbt-bigquery
Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots dbt models or pipelines - Needs to optimize SQL within a dbt project - Is…