MarieKondo­ForAgents

Make your organisation's data agent-ready, the KonMari way.

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Point an AI agent at a SharePoint, a CRM and a file share and it finds angebot_final_v3(2).docx, three price lists from three years and a customer table nobody has touched since 2021. Then it answers confidently and wrongly.

The KonMari-Kit fixes the data, not the prompt. It takes one use case, discards what doesn't belong, sorts the rest by category, gives every item an owner, a definition and the right permissions, and serves the result to your agents through a read-only MCP server. Every step is gated, every decision is made by a named person and logged, and every change can be undone.

Quick start with Claude Code

/plugin marketplace add KI-Klub-Werder-e-V/MarieKondoForAgents
/plugin install konmari@konmari-kit

Open Claude Code in an empty folder and say what you need:

Tidy our data for an FAQ agent in the club office.

Claude interviews you for the charter, registers your sources, runs the scans and checks, and explains each report. It stops before every approval and asks the person the charter names. Requires Python 3.12 and uv. Details: docs/agents.md.

git clone https://github.com/KI-Klub-Werder-e-V/MarieKondoForAgents
cd MarieKondoForAgents && uv sync
cd examples/verein-faq-assistant          # fictional sports club, FAQ assistant

uv run konmari next                       # where are we, what's next
uv run konmari charter validate
uv run konmari charter approve --as anna
uv run konmari metrics validate
uv run konmari metrics approve --as ben
uv run konmari charter gate --step inventory

The gate refuses until every must-have metric has a baseline:

unmet: metrics: must_have metric 'answer_accuracy' has no baseline or measurement plan

Continue with the guide. All commands: docs/cli.md.

Related MCP server: dst

How it works

Nine steps for one use case. Each step checks that the previous one is done.

Low-risk categories go first (public FAQ), special-category personal data last.

The joy test

Every asset is scored 0–3 on six criteria, with evidence:

Features

• Deterministic engine. Gates, scoring, deduplication and policies are rules with versioned thresholds. Same inputs, same result. Models are optional and pinned.

• Humans approve. Discarding, masking, erasing and publishing need sign-off from the role the charter names, recorded with identity and time.

• Reversible and auditable. Archive instead of delete, restore by command, a hash-chained audit trail you can verify with konmari audit verify.

• Duplicates at three levels. Identical files, near-duplicates (MinHash/LSH), semantic duplicates (embeddings, optional).

• GDPR built in. Local PII detection (Presidio, spaCy), retention rules and legal holds per category, keyed pseudonymisation, record of processing activities export.

• Open standards. DCAT inventory, OpenLineage lineage, ODPS/ODCS data products, SKOS glossary, MCP for agents.

• Self-hosted. DuckDB locally, Postgres 16 in production. No cloud service required.

Documentation

Contributing

The most useful contribution is a pilot report: which use case, which systems, how far you got and where it got stuck. Connectors (Nextcloud, Confluence, Google Drive, more CRMs), examples and translations are welcome too.

uv sync && uv run pytest -q
openspec validate --all --strict

Behaviour changes start as an OpenSpec change. See CONTRIBUTING.md and AGENTS.md.

License

Apache-2.0. A project of KI Klub Werder e. V.