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Schema Mapper

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nimrodfisher
schema-mapper

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

Overview

Publishernimrodfisher
Repositorydata-analytics-skills
Skill nameschema-mapper
Stars
493
Forks
87
Bundled files
3
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 3 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 Schema Mapper AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/schema-mapper
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/schema-mapper .claude/skills/schema-mapper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Schema Mapper 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 Schema Mapper 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 Schema Mapper 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.

Schema Mapper

When to use

  • Integrating a new data source and need to map its fields to the existing data model
  • Designing an ETL or dbt transformation and need to document the logic
  • Auditing what happened to a field during a migration
  • Onboarding a new analyst who needs to understand where columns come from
  • Preparing a data catalog entry that requires lineage at the column level

Process

  1. Collect the source schema — list every column name, data type, nullable flag, and a brief description. Pull from INFORMATION_SCHEMA, a data dictionary, or the source API documentation.
  2. Collect the target schema — same structure for the destination table or model. If the target doesn't exist yet, draft it based on the analytical requirements.
  3. Map source columns to target columns — for each target column, identify the source column(s) that feed it. Record direct mappings (rename only) and derived mappings (calculation, type cast, lookup join). Use scripts/schema_compare.py to automate direct-name matches.
  4. Document transformation rules — for each derived mapping, write the exact transformation (e.g., CAST(amount_cents AS FLOAT) / 100.0, COALESCE(first_name, email)).
  5. Flag gaps — identify target columns with no source (need to be created or defaulted) and source columns with no target (dropped or deferred). Record a decision for each.
  6. Produce the mapping document — complete assets/schema_mapping_template.md with the full column inventory and share for review before implementation.

Inputs the skill needs

  • Source schema: table name, column names, data types, and descriptions
  • Target schema: same, or the analytical requirements that define it
  • Any existing transformation logic (SQL, dbt models, Python code)
  • Business rules that govern how values should be transformed or defaulted
  • Stakeholder who can resolve ambiguous fields

Output

  • scripts/schema_compare.py — compares two schemas and finds direct-name matches and type mismatches
  • assets/schema_mapping_template.md — completed column-by-column mapping with transformation rules, gaps, and decisions
  • Optional: transformation SQL or dbt YAML generated from the mapping

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 Schema Mapper AI skill do?

Document column-level mappings between source and target schemas. Use when integrating data from multiple systems, designing ETL transformations, or documenting how raw fields become analytical assets.

Why use Schema Mapper on TypingMind?

Because you install it once and use it with any model. Schema Mapper 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 Schema Mapper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/02-documentation-knowledge/schema-mapper. 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 Schema Mapper?

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 Schema Mapper?

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

Is the Schema Mapper 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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