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Data Cleaner

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MAGA2010
data-cleaner

Turn profile.json into a deterministic cleaning plan, execute only the planned operations, and prove byte-for-byte reproducibility. Additive-only.

Overview

PublisherMAGA2010
Repositorydata-master
Skill namedata-cleaner
Stars
55
Forks
1
Bundled files
Instructions only
LicenseMIT
Links
  • 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 Cleaner 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/MAGA2010/data-master/tree/main/skills/data-cleaner
  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/MAGA2010/data-master.git /tmp/data-master
mkdir -p .claude/skills
cp -r /tmp/data-master/skills/data-cleaner .claude/skills/data-cleaner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Cleaner 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 Cleaner 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 Cleaner 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-cleaner

Stage 1c 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

Whenever profile.json exists. Cleaning is never ad hoc: the plan is the gate, and only plan["auto_actions"] may execute.

Inputs

  • analysis/profile.json
  • references/cleaning-rule-catalog.md
  • references/never-automatic-list.md

Outputs

  • cleaning_plan.json -- auto_actions, never_automatic_flags, principle
  • cleaned/cleaned.csv
  • cleaned/clean_<slug>.py -- standalone, re-runnable
  • cleaned/cleaning_run.json -- applied_ops, withheld, failed, dtype_manifest, columns_touched_by_ops, untouched_column_audit, reproducibility_check, case_variant_merge_map
  • cleaned/cleaning_report.md

Commands

powershell
python scripts/cleaning_plan.py --profile outputs/analysis/profile.json --out outputs/cleaning_plan.json
python scripts/execute_cleaning.py --in <raw.csv> --plan outputs/cleaning_plan.json --out outputs/cleaned
python scripts/validate_clean.py --raw <raw.csv> --clean outputs/cleaned/cleaned.csv --profile outputs/analysis/profile.json --out outputs/cleaned/validation.json

Gate (default FAIL)

All five must hold, or the run freezes to audit/:

  1. reproducibility_check.passed -- the generated script reproduces the cleaned hash;
  2. untouched_column_audit.passed -- no unnamed column changed;
  3. no conversion ran unless every non-null value parsed (no parse-rate threshold);
  4. every never-automatic item is recorded in withheld with its evidence;
  5. dtype_manifest exists and is replayed by the analysis stage.

Failure modes seen in real runs

  • A 96%% parse rate looks like a green light. Converting the remaining 4%% turns recorded sentinels (--, unknown) into missing values, which are then indistinguishable from values that were never collected. Hold the whole conversion.
  • Case variants split a group. Setosa / setosa halves both n and narrows every interval without losing information; merge with an explicit case_variant_merge_map.
  • A rule rewrites a column it did not name. The untouched-column audit compares raw and cleaned in text space, so this is caught rather than shipped.

Do not

  • do not modify, move, or delete the raw file;
  • do not impute, deduplicate, or merge datasets automatically;
  • do not add a parse-rate threshold -- the only safe threshold is 100%%.

Upstream / downstream

  • previous: ../data-profiler/SKILL.md
  • next: ../data-explorer/SKILL.md, ../statistical-analyzer/SKILL.md
  • rules: ../../references/cleaning-rule-catalog.md

Frequently asked questions

What does the Data Cleaner AI skill do?

Turn profile.json into a deterministic cleaning plan, execute only the planned operations, and prove byte-for-byte reproducibility. Additive-only.

Why use Data Cleaner on TypingMind?

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

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

Which AI models can use Data Cleaner?

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 Cleaner?

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

Is the Data Cleaner 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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