documenting-dbt-models

documenting-dbt-models is a skill for Claude Code from AltimateAI/data-engineering-skills. It costs 105 tokens per session (1,046 once invoked), scanned A, original, MIT.

A guide for documenting dbt models and columns in schema.yml files. dbt is a tool for building data models, while schema.yml stores descriptions and related metadata about them.

In plain words
What is it for?
Use it to add or improve model and column descriptions, document business context, explain data grain, or prepare dbt documentation.
Why use it?
It helps documentation explain what data means, why it exists, its level of detail, business rules, and important limitations.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the dbt-skills plugin — 7 skills shipped together

Good fit Use it to add or improve model and column descriptions, document business context, explain data grain, or prepare dbt documentation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/altimateai/data-engineering-skills/documenting-dbt-models
Install

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.

Any agent
npx skills add AltimateAI/data-engineering-skills --skill documenting-dbt-models
Clone the repo
git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills

Made for: Claude Code.

Or install dbt-skills, the plugin that ships this one along with the rest of its 7 skills.

Wrote 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.

agentmods badge for documenting-dbt-models

README.md
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Your own site
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Your own site · 80×15
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Per session 105 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,046 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 160
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00105 $0.01046
Opus 5.5 $0.00042 $0.00418
Sonnet 5.5 $0.00021 $0.00209
Haiku 4.5 $0.00011 $0.00105

Measured 1mo ago against content hash 128a222b1c03, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

documenting-dbt-models 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 1mo 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.

skills/dbt/documenting-dbt-models/SKILL.md · 168 lines

How it starts

The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.

dbt Documentation

Document the WHY, not just the WHAT. Include grain, business rules, and caveats.

Workflow

1. Study Existing Documentation Patterns

CRITICAL: Match the project's documentation style before adding new docs.

# Find all schema.yml files with documentation
find . -name "schema.yml" | head -5

# Read well-documented models to learn patterns
cat models/marts/schema.yml | head -150
cat models/staging/schema.yml | head -150

Extract from existing documentation:

  • Description length (brief vs detailed)
  • Formatting style (plain text vs markdown with headers)
  • Information included (grain? business rules? caveats?)
  • Column description depth (all columns vs key columns)
  • Use of meta tags or custom properties

2. Read Model SQL

cat models/<path>/<model_name>.sql

Understand: transformations, business logic, joins, filters.

3. Check Existing Documentation for This Model

# Find existing schema.yml
find . -name "schema.yml" -exec grep -l "<model_name>" {} \;

# Read existing docs
cat models/<path>/schema.yml | grep -A 100 "<model_name>"

4. Identify Documentation Needs

For each model, document:

  • Model description: Purpose, grain, key business rules
  • Column descriptions: Business meaning, not just data type

For each column, consider:

  • What business concept does this represent?
  • Are there any caveats or special values?
  • What is the source of this data?

5. Write Documentation

Match the style discovered in step 1. Example format (adapt to project):

version: 2

models:
  - name: orders
    description: |
      Order transactions at the order line item grain.
      Each row represents one product in one order.

      **Business Rules:**
      - Revenue recognized on ship_date, not order_date
      - Cancelled orders excluded (status != 'cancelled')
      - Returns processed as negative line items

      **Grain:** One row per order_id + product_id combination

    columns:
      - name: order_id
        description: |
          Unique identifier for the order.
          Source: orders.id from Stripe webhook

      - name: customer_id
        description: |
          Foreign key to customers table.
          NULL for guest checkouts (pre-2023 only)

      - name: revenue
        description: |
          Net revenue for this line item in USD.
          Calculation: unit_price * quantity - discount_amount
          Excludes tax and shipping

      - name: order_status
        description: |
          Current status of the order.
          Values: pending, processing, shipped, delivered, cancelled, returned

Read the full file on GitHub · 168 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. 1mo ago First seen · 168 lines · 105 tokens per session scan A 128a222b1c03

Subscribe to this mod's changes

documenting-dbt-models is a skill published in the GitHub repository AltimateAI/data-engineering-skills (129 stars, last pushed 5d ago), licensed MIT. It adds 105 tokens to every session and 1,046 once invoked, about $0.0004 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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