Analytics Engineer
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile.
Install dbt Model Builder
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-models --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dbt/creating-dbt-models .claude/skills/creating-dbt-models && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .claude/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-modelsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-models --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dbt/creating-dbt-models .agents/skills/creating-dbt-models && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .agents/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-models --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dbt/creating-dbt-models .cursor/skills/creating-dbt-models && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .cursor/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/AltimateAI/data-engineering-skills.git --path skills/dbt/creating-dbt-models--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-models --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dbt/creating-dbt-models .gemini/skills/creating-dbt-models && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .gemini/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-modelsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dbt/creating-dbt-models .github/skills/creating-dbt-models && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .github/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install AltimateAI/data-engineering-skills creating-dbt-models --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/AltimateAI/data-engineering-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dbt/creating-dbt-models .opencode/skills/creating-dbt-models && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "creating-dbt-models" agent skill from https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/dbt/creating-dbt-models into .opencode/skills/creating-dbt-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "creating-dbt-models", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
creating-dbt-models- GitHub stars
- 127
- Token cost
- ~890 tokens
- SKILL.md length
- 319 words
- Files
- 1
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- MIT
At a glance
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile.
- Works in 10 steps: Understand the Task Requirements → Discover Project Conventions → Find Similar Models → …
- Creating a new dbt model in any layer of a project
- SKILL.md covers Critical Rules, Workflow and Anti-Patterns
- Calls dbt
What it does
This skill walks an agent through writing a dbt model. It first pins down the required columns, the grain of the table and any calculations, then reads dbt_project.yml and a few existing models to learn naming, config and SQL patterns, and looks for similar models to copy join types, aggregation patterns and NULL handling. It can preview upstream data with dbt show, writes the model to match the discovered conventions and the required columns exactly, and uses dbt compile as a syntax check.
Running dbt build on the model is mandatory, because compile alone is not enough. If the build fails three or more times, the agent stops and reassesses its whole approach. After a successful build it checks the result with dbt show for column names, row counts, plausible values and unexpected NULLs, and for calculated columns it verifies a sample row by hand against the source values. Finally it re-reads the original request to confirm it built what was asked, with exact column names and correct logic.
When your agent uses it
- Creating a new dbt model in any layer of a project
- Changing the logic, columns or joins of an existing model
- Implementing a model from schema.yml specs or expected output
Example prompts
- “Create a fct_orders model with one row per order and total_revenue as quantity times price.”
- “Add a customer_lifetime_value column to the dim_customers model and verify it.”
- “Build the model described in schema.yml for monthly_active_users and check its output.”
Requirements
- A dbt project where dbt build and dbt show can run
Workflow steps
10 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 705c68b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
dbt
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
dbt Model Builder loads about 890 tokens when it runs. Until then it costs about 133 tokens; SKILL.md has 319 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from AltimateAI/data-engineering-skills at commit 705c68b, republished under its MIT licence (© AltimateAI). 319 words, ~890 tokens.
Download SKILL.mdSave it as
.claude/skills/creating-dbt-models/SKILL.md (or your agent's skills folder).- name
- creating-dbt-models
- description
- Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
dbt Model Development
Read before you write. Build after you write. Verify your output.
Critical Rules
- ALWAYS run
dbt buildafter creating/modifying models - compile is NOT enough - ALWAYS verify output after build using
dbt show- don't assume success - If build fails 3+ times, stop and reassess your entire approach
Workflow
1. Understand the Task Requirements
- What columns are needed? List them explicitly.
- What is the grain of the table (one row per what)?
- What calculations or aggregations are required?
2. Discover Project Conventions
cat dbt_project.yml
find models/ -name "*.sql" | head -20Read 2-3 existing models to learn naming, config, and SQL patterns.
3. Find Similar Models
# Find models with similar purpose
find models/ -name "*agg*.sql" -o -name "*fct_*.sql" | head -5Learn from existing models: join types, aggregation patterns, NULL handling.
4. Check Upstream Data
# Preview upstream data if needed
dbt show --select <upstream_model> --limit 105. Write the Model
Follow discovered conventions. Match the required columns exactly.
6. Compile (Syntax Check)
dbt compile --select <model_name>7. BUILD - MANDATORY
This step is REQUIRED. Do NOT skip it.
dbt build --select <model_name>If build fails:
- Read the error carefully
- Fix the specific issue
- Run build again
- If fails 3+ times, step back and reassess approach
8. Verify Output (CRITICAL)
Build success does NOT mean correct output.
# Check the table was created and preview data
dbt show --select <model_name> --limit 10Verify:
- Column names match requirements exactly
- Row count is reasonable
- Data values look correct
- No unexpected NULLs
9. Verify Calculations Against Sample Data
For models with calculations, verify correctness manually:
# Pick a specific row and verify calculation by hand
dbt show --inline "
select *
from {{ ref('model_name') }}
where <primary_key> = '<known_value>'
" --limit 1
# Cross-check aggregations
dbt show --inline "
select count(*), sum(<column>)
from {{ ref('model_name') }}
"For example, if calculating total_revenue = quantity * price:
- Pick one row from output
- Look up the source quantity and price
- Manually calculate: does it match?
10. Re-review Against Requirements
Before declaring done, re-read the original request:
- Did you implement what was asked, not what you assumed?
- Are column names exactly as specified?
- Is the calculation logic correct per the requirements?
- Does the grain (one row per what?) match what was requested?
Anti-Patterns
- Declaring done after compile without running build
- Not verifying output data after build
- Getting stuck in compile/build error loops
- Assuming table exists just because model file exists
- Writing SQL without checking existing model patterns first
© AltimateAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/dbt/creating-dbt-models of AltimateAI/data-engineering-skills.
Open the folder on GitHubat commit 705c68b
Compare with similar skills
dbt Model Builder next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| dbt Model Builder this skillAltimateAI/data-engineering-skills | 127 | — | ~890 | Automated safety check: Pass | MIT | |
| Analytics Engineerborghei/Claude-Skills | 874 | — | ~3.4k | Automated safety check: Pass | MIT | |
| Databricks JobsKilo-Org/kilo-marketplace | 189 | 1 repos | ~3.1k | Automated safety check: Pass | Custom licence | |
| Monte Carlo Validation Notebooksickn33/agentic-awesome-skills | 47k | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Monte Carlo Preventsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.3k | Automated safety check: Pass | MIT | |
| Data Quality Checksmohitagw15856/pm-claude-skills | 1.4k | — | ~919 | Automated safety check: Pass | MIT |
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Related
Categories
Questions about dbt Model Builder
What does dbt Model Builder do?
Creates or modifies dbt models in line with a project's own conventions, then runs dbt build and dbt show to check the output instead of stopping at compile. This skill walks an agent through writing a dbt model.yml and a few existing models to learn naming, config and SQL patterns, and looks for similar models to copy join types, aggregation patterns and NULL handling.
When should I use dbt Model Builder?
dbt Model Builder fits situations like: creating a new dbt model in any layer of a project; changing the logic, columns or joins of an existing model; implementing a model from schema.yml specs or expected output.
How do I install dbt Model Builder in Claude Code?
Run `npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a claude-code`. Or copy the skill folder (skills/dbt/creating-dbt-models in AltimateAI/data-engineering-skills) into .claude/skills/creating-dbt-models in your project. Claude Code loads it when a task matches its description.
How do I install dbt Model Builder in Codex?
Run `npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a codex`. Or copy the skill folder (skills/dbt/creating-dbt-models in AltimateAI/data-engineering-skills) into .agents/skills/creating-dbt-models in your project. Codex loads it when a task matches its description.
Can I use dbt Model Builder in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AltimateAI/data-engineering-skills --skill creating-dbt-models -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/creating-dbt-models, .gemini/skills/creating-dbt-models, .github/skills/creating-dbt-models and .opencode/skills/creating-dbt-models in your project.
What does dbt Model Builder need to run?
Going by SKILL.md and its folder, dbt Model Builder needs the command-line tools its instructions call (dbt). Our summary lists: A dbt project where dbt build and dbt show can run.
Does dbt Model Builder access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is dbt Model Builder safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does dbt Model Builder use?
dbt Model Builder is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does dbt Model Builder use?
About 890 tokens (SKILL.md is roughly 3.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to dbt Model Builder?
Skills that share tags, products or a category with dbt Model Builder: Analytics Engineer (borghei/Claude-Skills, 874 stars), Databricks Jobs (Kilo-Org/kilo-marketplace, 189 stars), Monte Carlo Validation Notebook (sickn33/agentic-awesome-skills, 47k stars) and Monte Carlo Prevent (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains dbt Model Builder?
AltimateAI (a GitHub organization) maintains it in AltimateAI/data-engineering-skills, which has 127 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 1, 2026.
Source: AltimateAI/data-engineering-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.