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 G1Joshi/Agent-Skills --skill dbtgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/dbt)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/dbt"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/dbt/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/g1joshi/agent-skills/dbt"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/dbt.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 | $0.00045 | $0.01254 |
| Opus 5.5 | $0.00018 | $0.00502 |
| Sonnet 5.5 | $0.00009 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
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 22d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt (Data Build Tool)
dbt manages data transformation in the warehouse using SQL. v2.0 introduces the Fusion Engine (Rust) for performance.
When to Use
- Analytics Engineering & Data Transformations: Transforming raw data inside modern cloud warehouses (Snowflake, BigQuery, Databricks, Redshift).
- Modular SQL with Jinja & Version Control: Building reusable SQL models with DAG dependencies and macros.
- Data Quality & Schema Testing: Enforcing
unique,not_null, referential integrity, and custom singular tests. - Automated Data Documentation & Lineage: Generating interactive dependency lineage graphs directly from codebase schemas.
Quick Start
-- models/marts/fct_orders.sql
{{ config(materialized='table') }}
with orders as (
select * from {{ ref('stg_orders') }}
),
payments as (
select * from {{ ref('stg_payments') }}
)
select
orders.order_id,
orders.customer_id,
orders.order_date,
coalesce(payments.amount, 0) as total_amount
from orders
left join payments using (order_id)
Core Concepts
Declarative SQL Modeling with ref() & source()
Building transformation models with dependency resolution:
-- models/marts/core/fct_customer_orders.sql
{{
config(
materialized = 'incremental',
unique_key = 'order_id',
on_schema_change = 'fail'
)
}}
with orders as (
select * from {{ ref('stg_orders') }}
{% if is_incremental() %}
where order_date >= (select coalesce(max(order_date), '1970-01-01') from {{ this }})
{% endif %}
),
customers as (
select * from {{ ref('stg_customers') }}
)
select
o.order_id,
o.customer_id,
c.full_name as customer_name,
o.order_date,
o.total_amount_usd
from orders o
inner join customers c on o.customer_id = c.customer_id
Schema Testing & Documentation in YAML
Enforcing column constraints and documentation:
# models/marts/core/schema.yml
version: 2
models:
- name: fct_customer_orders
description: "Daily customer order fact table combining staged orders and customer dimensions."
columns:
- name: order_id
description: "Primary key for orders."
tests:
- unique
- not_null
- name: customer_id
description: "Foreign key referencing staging customers."
tests:
- not_null
- relationships:
to: ref('stg_customers')
field: customer_id
- name: total_amount_usd
description: "Total transaction value in USD."
tests:
- not_null
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.
- 22d ago Changed · +5 lines b87236214d52
- 26d ago Changed · +124 lines · +30 tokens per session fee7c34ea24c
- 1mo ago First seen · 45 lines · 15 tokens per session scan A 1ebf543fdb12
dbt is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 23d ago), licensed MIT. It adds 45 tokens to every session and 1,254 once invoked, about $0.0002 per session on Opus 5.5. 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
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
awesome-database-audit
Read-only audit of a database layer: schema anti-patterns, query and index fit, integrity, concurrency, migrations, tenancy, with a verdict. Use when asked to review a schema, migrations or data model.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
observability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.