Data Validator

Community

MAGA2010

data-validator

Run the 8 Nature statistical gates and the journal checklist against the finished artifacts. Writes audit/final_audit.md and refuses the label submission-ready on a mandatory FAIL.

Overview

Publisher	MAGA2010
Repository	`data-master`
Skill name	`data-validator`
Stars	55
Forks	1
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by MAGA2010 on GitHub. Read the source before you install it.

Installation

Install the Data Validator 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/MAGA2010/data-master/tree/main/skills/data-validator
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.

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/MAGA2010/data-master.git /tmp/data-master
mkdir -p .claude/skills
cp -r /tmp/data-master/skills/data-validator .claude/skills/data-validator

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

Use it in TypingMind

Enable Data Validator 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 Validator 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 Validator 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-validator

Stage 3c of the data-master pipeline. The router in ../../SKILL.md owns the end-to-end run; this skill owns one stage and one gate. Read the parent contract before changing anything here: the invariants in ../../SKILL.md (additive-only, types decided once, default-FAIL gates, three-part provenance) are not restated, they are inherited.

When to invoke

After the report exists. Also run it on a partially finished run to see which gate is still open -- the audit reads artifacts, so missing ones surface as FAIL/N/A rather than as an exception.

Inputs

outputs/report.md
outputs/report_build.json
outputs/analysis/*.json
outputs/cleaned/cleaning_run.json
references/journal-reporting-standards.json

Outputs

outputs/audit/final_audit.md
outputs/audit/final_audit.json
exit code 0 (submission-ready), 1 (mandatory gate failed), 2 (cannot run)

Commands

powershell
python scripts/final_audit.py --outdir outputs --journal nature
python scripts/final_audit.py --outdir outputs --journal nejm --design randomized_trial
python scripts/final_audit.py --outdir outputs --template standard --json

Gate (default FAIL)

Gates 1, 2, 7 and 8 are mandatory at every template. Gates 3-6 are mandatory for nature and relax only where the selected template says so -- and the relaxation is printed, never silent.

Gate 1 also requires explicit question provenance for an estimable comparison: --unit-key, --group, and --outcomes must be user-declared. Automatic selections must be labelled exploratory in the report; a missing label or incomplete declaration fails the gate.

Failure modes seen in real runs

An audit that trusts the report. Gate 3 must read effect_sizes from inferential.json, not the sentence the report wrote about it.
A checklist borrowed from the wrong design. CONSORT applied to a cross-sectional survey produces a page of N/A and a false sense of coverage.
n = 1 matching inside n = 158. Token matching is boundary-aware for a reason.

Do not

do not edit the report to make the audit pass;
do not turn a FAIL into a WARN because the run is late;
do not print PASS for registration, funding, consent or data availability -- those are author-supplied and are reported as AUTHOR INPUT NEEDED.

Upstream / downstream

checklist: ../../references/nature-statistics-checklist.md
journals: ../../references/journal-reporting-standards.md
script: ../../scripts/final_audit.py

Frequently asked questions

What does the Data Validator AI skill do?

Run the 8 Nature statistical gates and the journal checklist against the finished artifacts. Writes audit/final_audit.md and refuses the label submission-ready on a mandatory FAIL.

Why use Data Validator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MAGA2010/data-master/tree/main/skills/data-validator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Validator?

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 Validator?

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

Is the Data Validator AI skill free?

Yes. It is published on GitHub by MAGA2010 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

data-validator

When to invoke

Inputs

Outputs

Commands

Gate (default FAIL)

Failure modes seen in real runs

Do not

Upstream / downstream

Frequently asked questions

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