Dbt Databricks PR Ready
databricks/dbt-databricks
A skill your agent uses for an open dbt-databricks pull request, including your own PR or a fork PR, to assess merge readiness and optionally repair selected gaps on the PR head branch.
Build structured semantic layer documentation for metrics, dimensions, and entities.
Install Semantic Model Builder
$ npx skills add nimrodfisher/data-analytics-skills --skill semantic-model-builder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nimrodfisher/data-analytics-skills semantic-model-builder --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/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .claude/skills/semantic-model-builder && 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 "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .claude/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builderType 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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nimrodfisher/data-analytics-skills semantic-model-builder --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .agents/skills/semantic-model-builder && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .agents/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nimrodfisher/data-analytics-skills semantic-model-builder --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .cursor/skills/semantic-model-builder && 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 "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .cursor/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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/nimrodfisher/data-analytics-skills.git --path 02-documentation-knowledge/semantic-model-builder--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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nimrodfisher/data-analytics-skills semantic-model-builder --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .gemini/skills/semantic-model-builder && 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 "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .gemini/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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 nimrodfisher/data-analytics-skills semantic-model-builderInstalls 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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .github/skills/semantic-model-builder && 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 "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .github/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nimrodfisher/data-analytics-skills semantic-model-builder --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/02-documentation-knowledge/semantic-model-builder .opencode/skills/semantic-model-builder && 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 "semantic-model-builder" agent skill from https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder into .opencode/skills/semantic-model-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "semantic-model-builder", 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
semantic-model-builder- GitHub stars
- 465
- Token cost
- ~647 tokens
- SKILL.md length
- 283 words
- Files
- 9 (incl. scripts, references, assets)
- Skills in repo
- 31
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build structured semantic layer documentation for metrics, dimensions, and entities.
- Works in 6 steps: Identify the object type — decide… → Gather the definition inputs — collect:… → Generate the YAML template — run… → …
- Tasks that involve Data pipelines and ETL
- Runs Python scripts from its folder
What it does
Semantic Model Builder is an agent skill from nimrodfisher/data-analytics-skills. Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.
Its SKILL.md is about 650 tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `assets/dimension_definition.yaml`, `assets/entity_definition.yaml` and `assets/metric_definition.yaml`).
It sits in Data & Analytics, covering Data pipelines and ETL. It works with dbt and SQL. The repository describes itself as: A comprehensive list of Claude & Codex skills for a wide range of data analytics tasks. The licence is MIT.
When your agent uses it
- Tasks that involve Data pipelines and ETL
Example prompts
- “/semantic-model-builder”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Identify the object type — decide whether you're documenting a metric, a dimension, or an entity. Use the frameworks in…
- Gather the definition inputs — collect: calculation logic (SQL or formula), business context, data source(s), grain, edge cases, and known…
- Generate the YAML template — run scripts/metric_template_generator.py to scaffold the initial YAML structure for the object type. Fill in…
- Validate the YAML — run scripts/model_yaml_validator.py to check required fields, type constraints, and reference integrity (referenced…
- Add dbt context — if this will be deployed to dbt Semantic Layer, consult references/dbt_semantic_layer_guide.md for the exact field names…
- Save final definitions — save metrics to assets/metric_definition.yaml, dimensions to assets/dimension_definition.yaml, entities to…
What it can do on your machine
Read from SKILL.md and the folder at commit 9449d36. 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
Ships 2 files in scripts/ (Python), which the agent can run.
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
Semantic Model Builder loads about 647 tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 67 tokens; SKILL.md has 283 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); the scripts in this folder are not scanned.
SKILL.md
The full file from nimrodfisher/data-analytics-skills at commit 9449d36, republished under its MIT licence (© nimrodfisher). 283 words, ~647 tokens.
Download SKILL.mdSave it as
.claude/skills/semantic-model-builder/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- semantic-model-builder
- description
- Build structured semantic layer documentation for metrics, dimensions, and entities. Activate when you need to define a business metric, document a data model, or create YAML definitions compatible with dbt Semantic Layer or similar frameworks.
When to use
- A stakeholder asks "how is [metric] calculated?" and no canonical definition exists
- You're setting up dbt Semantic Layer and need YAML metric/dimension/entity definitions
- Multiple teams are using different SQL queries for the same metric — you need to codify the one true definition
- You're building a data catalog entry for a core model and need structured metadata
Process
- Identify the object type — decide whether you're documenting a metric, a dimension, or an entity. Use the frameworks in
references/metric_definition_framework.mdfor metrics andreferences/dimension_hierarchy_patterns.mdfor dimensions. - Gather the definition inputs — collect: calculation logic (SQL or formula), business context, data source(s), grain, edge cases, and known gotchas. Ask the data owner if anything is unclear.
- Generate the YAML template — run
scripts/metric_template_generator.pyto scaffold the initial YAML structure for the object type. Fill in the generated template. - Validate the YAML — run
scripts/model_yaml_validator.pyto check required fields, type constraints, and reference integrity (referenced dimensions exist in the same file). - Add dbt context — if this will be deployed to dbt Semantic Layer, consult
references/dbt_semantic_layer_guide.mdfor the exact field names and constraints for your dbt version. - Save final definitions — save metrics to
assets/metric_definition.yaml, dimensions toassets/dimension_definition.yaml, entities toassets/entity_definition.yaml.
Inputs the skill needs
- Required: the metric name or model name to document
- Required: calculation logic — SQL snippet, formula, or plain-English steps
- Required: business context — who uses it, what decision it informs, what a "good" value looks like
- Optional: data source table(s) and column names
- Optional: target semantic layer framework (dbt Semantic Layer, Cube.js, LookML, etc.)
- Optional: existing YAML to validate
Output
assets/metric_definition.yaml— filled metric YAML definition(s)assets/dimension_definition.yaml— filled dimension YAML definition(s)assets/entity_definition.yaml— filled entity YAML definition(s)- Validation report from
scripts/model_yaml_validator.py(inline output)
© nimrodfisher, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 8 other files (scripts, references, assets) in 02-documentation-knowledge/semantic-model-builder of nimrodfisher/data-analytics-skills.
- SKILL.md
- assets/dimension_definition.yaml
- assets/entity_definition.yaml
- assets/metric_definition.yaml
- references/dbt_semantic_layer_guide.md
- references/dimension_hierarchy_patterns.md
- references/metric_definition_framework.md
- scripts/metric_template_generator.py
- scripts/model_yaml_validator.py
Open the folder on GitHubat commit 9449d36
Compare with similar skills
Semantic 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 |
|---|---|---|---|---|---|---|
| Semantic Model Builder this skillnimrodfisher/data-analytics-skills | 465 | — | ~647 | Automated safety check: Pass | MIT | |
| Dbt Databricks PR Readydatabricks/dbt-databricks | 380 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Senior Data Engineerbenchflow-ai/skillsbench | 1.8k | — | ~5.9k | Automated safety check: Pass | MIT | |
| dbt Model BuilderAltimateAI/data-engineering-skills | 128 | — | ~890 | Automated safety check: Pass | MIT | |
| dbt Error DebuggingAltimateAI/data-engineering-skills | 128 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Analytics Engineerborghei/Claude-Skills | 881 | — | ~3.4k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Semantic Model Builder
What does Semantic Model Builder do?
Build structured semantic layer documentation for metrics, dimensions, and entities. Semantic Model Builder is an agent skill from nimrodfisher/data-analytics-skills. Build structured semantic layer documentation for metrics, dimensions, and entities.
When should I use Semantic Model Builder?
Semantic Model Builder fits situations like: tasks that involve Data pipelines and ETL.
How do I install Semantic Model Builder in Claude Code?
Run `npx skills add nimrodfisher/data-analytics-skills --skill semantic-model-builder -a claude-code`. Or copy the skill folder (02-documentation-knowledge/semantic-model-builder in nimrodfisher/data-analytics-skills) into .claude/skills/semantic-model-builder in your project. Claude Code loads it when a task matches its description.
How do I install Semantic Model Builder in Codex?
Run `npx skills add nimrodfisher/data-analytics-skills --skill semantic-model-builder -a codex`. Or copy the skill folder (02-documentation-knowledge/semantic-model-builder in nimrodfisher/data-analytics-skills) into .agents/skills/semantic-model-builder in your project. Codex loads it when a task matches its description.
Can I use Semantic 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 nimrodfisher/data-analytics-skills --skill semantic-model-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/semantic-model-builder, .gemini/skills/semantic-model-builder, .github/skills/semantic-model-builder and .opencode/skills/semantic-model-builder in your project.
What does Semantic Model Builder need to run?
Going by SKILL.md and its folder, Semantic Model Builder needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Semantic 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 Semantic 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Semantic Model Builder use?
Semantic 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 Semantic Model Builder use?
About 647 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3k tokens, read only when the agent opens those files.
What are the alternatives to Semantic Model Builder?
Skills that share tags, products or a category with Semantic Model Builder: Dbt Databricks PR Ready (databricks/dbt-databricks, 380 stars), Senior Data Engineer (benchflow-ai/skillsbench, 1.8k stars), dbt Model Builder (AltimateAI/data-engineering-skills, 128 stars) and dbt Error Debugging (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Semantic Model Builder?
nimrodfisher (a GitHub user) maintains it in nimrodfisher/data-analytics-skills, which has 465 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on September 25, 2026.
Source: nimrodfisher/data-analytics-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.