Data Quality Foundation Implementation Guidance

Organization

oracle

Provides CX Customer Data Management implementation guidance for the Data Quality Foundation functional area.

Overview

Publisher	oracle
Repository	`Fusion-Setup-Configuration-Registry`
Skill name	`Data Quality Foundation Implementation Guidance`
Stars	112
Forks	26
Bundled files	7
License	UPL-1.0
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.
7 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by oracle on GitHub. Read the source before you install it.

Installation

Install the Data Quality Foundation Implementation Guidance 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/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/cx/customer-data-management/data-quality-foundation
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/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/cx/customer-data-management/data-quality-foundation

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/oracle/Fusion-Setup-Configuration-Registry.git /tmp/Fusion-Setup-Configuration-Registry
mkdir -p .claude/skills
cp -r /tmp/Fusion-Setup-Configuration-Registry/pillars/cx/customer-data-management/data-quality-foundation .claude/skills/oracle-data-quality-foundation-implementation-guidance

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

Use it in TypingMind

Enable Data Quality Foundation Implementation Guidance 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 Data Quality Foundation Implementation Guidance 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 Data Quality Foundation Implementation Guidance 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.

Data Quality Foundation Implementation Guidance

Purpose

Provides CX Customer Data Management implementation guidance for the Data Quality Foundation functional area.

When to Use

Use this functional-area skill after Customer Data Management scope is confirmed and the project needs data quality foundation setup planning, baseline export, import sequencing, or validation checkpoints.

Catalog Metadata

Pillar: CX
Offering: Customer Data Management (ZCH_CDM)
Functional area / Setup Object: Data Quality Foundation (ORA_ZCQ_DATA_QLTY_FOUNDATION)

Setup Tasks

Manage Data Quality Lookups (ZCQ_MANAGE_DATA_QUALITY_LOOKUPS)
Manage Server Configurations (ZCQ_MANAGE_SERVER_CONFIGURATIONS)

Inputs

implementation_objective
project_context
data_quality_foundation_scope
data_quality_foundation_requirements

Outputs

functional_area_setup_plan
configuration_guidance
validation_checkpoints
skill_run_record
token_usage_record

Implementation Notes

Use the generated nested setup-task skills for task-level planning, sequencing, setup-data routing, and validation. Track token usage for any generated project artifact or skill-run record using the manifest policy.

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 Data Quality Foundation Implementation Guidance AI skill do?

Provides CX Customer Data Management implementation guidance for the Data Quality Foundation functional area.

Why use Data Quality Foundation Implementation Guidance on TypingMind?

Because you install it once and use it with any model. Data Quality Foundation Implementation Guidance 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 Data Quality Foundation Implementation Guidance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oracle/Fusion-Setup-Configuration-Registry/tree/main/pillars/cx/customer-data-management/data-quality-foundation. 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 Data Quality Foundation Implementation Guidance?

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 Data Quality Foundation Implementation Guidance?

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

Is the Data Quality Foundation Implementation Guidance AI skill free?

Yes. It is published on GitHub by oracle under the UPL-1.0 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

Data Quality Foundation Implementation Guidance

Purpose

When to Use

Catalog Metadata

Setup Tasks

Inputs

Outputs

Implementation Notes

Bundled files

Frequently asked questions

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