Data Quality Audit

Community

nimrodfisher

data-quality-audit

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.

Overview

Publisher	nimrodfisher
Repository	`data-analytics-skills`
Skill name	`data-quality-audit`
Stars	493
Forks	87
Bundled files	9
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.
9 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 Data Quality Audit 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/01-data-quality-validation/data-quality-audit
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/01-data-quality-validation/data-quality-audit

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/01-data-quality-validation/data-quality-audit .claude/skills/data-quality-audit

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

Use it in TypingMind

Enable Data Quality Audit 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 Audit 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 Audit 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 data pipeline has just loaded new data and needs validation before downstream reports consume it
A stakeholder has flagged data quality concerns (wrong totals, unexpected nulls, stale data)
You need to produce a formal data quality scorecard for a data asset as part of a data governance process
You are onboarding a new data source and need to understand its quality profile before building on it

Process

Null and completeness audit — run scripts/null_counter.py for a column-by-column null profile. Flag columns above acceptable thresholds for the business context.
Duplicate detection — run scripts/duplicate_finder.py to identify full-row and key-level duplicates. Determine if duplicates are intentional (versioning) or errors (pipeline fan-out).
Referential integrity check — run scripts/referential_integrity.py to validate that foreign key values in child tables exist in parent tables. Report orphan rate per relationship.
Value range validation — run scripts/value_range_validator.py with business rules defined in references/business_rule_patterns.md. Flag values outside acceptable ranges.
Freshness check — run scripts/freshness_check.py to verify the dataset is up to date — compare the latest record timestamp against the expected lag for this pipeline.
Score and classify findings — map each finding to a quality dimension using references/quality_dimensions.md. Assign severity (CRITICAL / HIGH / MEDIUM / LOW).
Produce deliverables — fill assets/audit_report_template.html for a shareable report; fill assets/quality_rubric.md for a concise scorecard.

Inputs the skill needs

Required: dataset (CSV / Parquet / database table reference)
Required: schema relationships — which columns are primary keys, which are foreign keys to which tables
Required: business rules — acceptable value ranges, expected value sets, freshness SLA
Optional: acceptable error rates — at what threshold does a failure become CRITICAL vs. HIGH
Optional: pipeline schedule — to assess freshness relative to expected update frequency

Output

assets/audit_report_template.html (filled) — full quality report, shareable with stakeholders
assets/quality_rubric.md (filled) — one-page quality scorecard with dimension scores
Script console output — per-check pass/fail counts for each validation script

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 Audit AI skill do?

Why use Data Quality Audit on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nimrodfisher/data-analytics-skills/tree/main/01-data-quality-validation/data-quality-audit. 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 Audit?

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 Audit?

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

Is the Data Quality Audit 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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