Semantic Model Builder

Community

nimrodfisher

semantic-model-builder

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.

Overview

Publisher	nimrodfisher
Repository	`data-analytics-skills`
Skill name	`semantic-model-builder`
Stars	493
Forks	87
Bundled files	8
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nimrodfisher on GitHub. Read the source before you install it.

Installation

Install the Semantic Model Builder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/nimrodfisher/data-analytics-skills.git /tmp/data-analytics-skills
mkdir -p .claude/skills
cp -r /tmp/data-analytics-skills/02-documentation-knowledge/semantic-model-builder .claude/skills/semantic-model-builder

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Semantic Model Builder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Semantic Model Builder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Semantic Model Builder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.md for metrics and references/dimension_hierarchy_patterns.md for 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.py to scaffold the initial YAML structure for the object type. Fill in the generated template.
Validate the YAML — run scripts/model_yaml_validator.py to 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.md for the exact field names and constraints for your dbt version.
Save final definitions — save metrics to assets/metric_definition.yaml, dimensions to assets/dimension_definition.yaml, entities to assets/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)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Semantic Model Builder AI skill do?

Why use Semantic Model Builder on TypingMind?

Because you install it once and use it with any model. Semantic Model Builder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Semantic Model Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/semantic-model-builder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Semantic Model Builder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Semantic Model Builder?

As many as you like. As long as a model supports skills, you can use Semantic Model Builder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Semantic Model Builder AI skill free?

Yes. It is published on GitHub by nimrodfisher under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to use

Process

Inputs the skill needs

Output

Bundled files

Frequently asked questions

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