dbt-core-mcp: Instructions file for GitHub Copilot

.github/instructions/dbt-unit-testing.instructions.md

dbt-core-mcp dbt-unit-testing.instructions.md is an instructions file for GitHub Copilot from NiclasOlofsson/dbt-core-mcp. It costs 1,671 tokens per session, scanned A, original, MIT.

Guidelines for unit-testing dbt models. dbt is a tool that transforms data with SQL, and unit tests check a model's result using small, known input examples.

In plain words
What is it for?
Use it to inspect model dependencies, create realistic test fixtures and expected results, run individual tests, and update models until they pass.
Why use it?
It gives tests a consistent location and workflow, making model changes easier to verify before and after implementation.

Instructions file for GitHub Copilot

Written for GitHub Copilot: a Copilot instructions file.

This is NiclasOlofsson/dbt-core-mcp's own configuration. It tells GitHub Copilot how to work on dbt-core-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dbt-core-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to NiclasOlofsson/dbt-core-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/NiclasOlofsson/dbt-core-mcp/main/.github/instructions/dbt-unit-testing.instructions.md
Clone the repo
git clone --depth 1 https://github.com/NiclasOlofsson/dbt-core-mcp

Made for: GitHub Copilot.

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When invoked 1,671 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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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.

ModelPer sessionOnce invoked
Fable 5.1 $0.01671 $0.01671
Opus 5.5 $0.00668 $0.00668
Sonnet 5.5 $0.00334 $0.00334
Haiku 4.5 $0.00167 $0.00167

Measured 2mo ago against content hash 68ad99c4bb83, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

dbt-core-mcp dbt-unit-testing.instructions.md 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 2mo 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.

.github/instructions/dbt-unit-testing.instructions.md · 239 lines

How it starts

The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.

dbt Unit Testing Guidelines

File Organization

Rule: Keep unit tests in separate unit_tests/ directory, mirroring model structure

models/
  ├── staging/
  │   └── stg_customers.sql
  └── marts/
      └── customers.sql

unit_tests/
  ├── staging/
  │   └── stg_customers_unit_tests.yml
  └── marts/
      └── customers_unit_tests.yml

Configuration (dbt_project.yml):

model-paths: ["models", "unit_tests"]

TDD Workflow for Unit Tests

1. Create Test First (Red Phase)

Step 1: Inspect the model to understand dependencies

get_resource_info(name="customers", include_compiled_sql=true)

Step 2: Query sample data for realistic fixtures

query_database("SELECT * FROM {{ ref('stg_customers') }} LIMIT 3")
query_database("SELECT * FROM {{ ref('stg_orders') }} LIMIT 5")

Step 3: Create test YAML with:

  • Descriptive name: test_<behavior> (e.g., test_customer_with_single_order)
  • Clear description
  • Minimal data covering the test case
  • Expected output

Step 4: Run the specific test

test_models(select="test_name:test_customer_with_single_order")

2. Implement/Fix Model (Green Phase)

Edit or create the dbt model to make the test pass.

3. Refactor

Clean up the model while keeping tests green.

Test Design Principles

Minimal Data: Only include columns/rows needed for the specific test case

Fixture Reuse with YAML Anchors: Reduce duplication by anchoring common input data

# Define reusable fixtures at the top
_base_customers: &customer_input
  input: ref('stg_customers')
  rows:
    - {customer_id: 1, first_name: 'Alice', last_name: 'Smith'}
    - {customer_id: 2, first_name: 'Bob', last_name: 'Jones'}

unit_tests:
  - name: test_basic_aggregation
    model: customers
    given:
      - *customer_input      # Reuse the fixture as-is
      - input: ref('stg_orders')
        rows: [...]
  
  - name: test_with_different_customers
    model: customers
    given:
      - input: ref('stg_customers')  # Just write it out if data is different
        rows:
          - {customer_id: 99, first_name: 'Different', last_name: 'Customer'}
      - input: ref('stg_orders')
        rows: [...]

Read the full file on GitHub · 239 lines

Changes

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.

  1. 2mo ago First seen · 239 lines · 1,671 tokens per session scan A 68ad99c4bb83

Subscribe to this mod's changes

dbt-core-mcp dbt-unit-testing.instructions.md is an instructions file published in the GitHub repository NiclasOlofsson/dbt-core-mcp (15 stars, last pushed 6mo ago), licensed MIT. It adds 1,671 tokens to every session, about $0.0067 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.

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