reference-and-concept-hygiene

name	reference-and-concept-hygiene
description	Builds and audits a concept-ontology for a regelrecht corpus to decide and verify reference types — cross-law source-binding vs intra-law source-binding vs open-norm leaf vs external-fact leaf — and to catch concept conflation across laws. Core principle: a value's reference type is fixed by WHO derives it, and a binding is sound only when producer and consumer denote the SAME concept; the lexical surface (the word in the text) is not the concept. Detects false friends (one term, different concepts — e.g. a register-based vs a factual "naar de omstandigheden"-based residence/ingezetene concept), orphan plain-params (a value a regulation actually derives, modeled as a brute input), negation/complement re-entry (¬X modeled as an independent leaf next to X), and duplication (one concept re-entered as several independent leafs, losing the single-source-of-truth invariant). Produces a law-level glossary and a stelsel-level concept map with sound same-concept binding edges and flagged lexical-match-but-concept-mismatch edges. Use when deciding whether a value should be cross-law/intra-law/leaf, when the same term means different things across laws, or when a binding might silently conflate concepts.
allowed-tools	Read, Write, Bash, Grep, Glob, WebFetch, WebSearch, AskUserQuestion
user-invocable	true

Cross-law Concept & Binding Ontology

Two recurring questions in a cross-law model: (1) what reference type should a value be — a cross-law source-binding, an intra-law source-binding, an open-norm leaf, or an external-fact leaf — and (2) is a given binding sound, given that the same term can denote a different concept in a different law. This skill answers both by building a concept-ontology and auditing references against it.

Core principle — type follows derivation, soundness follows concept identity

The reference type of a modeled value is fixed by who derives it. A source-binding is sound only when the producer and the consumer denote the same concept. The lexical surface — the word in the text: — is not the concept: two laws can use one word for different concepts (false friends), or different words for one concept (synonyms). The engine runs a conflated binding without complaint; only concept analysis catches it.

Reference-type decision procedure

For each modeled value, ask in order:

1. Does another regulation derive it (an endpoint computes it)? → cross-law source-binding.
2. Does the same regulation derive it in another article? → intra-law source-binding.
3. Does the law delegate the determination to a factual or discretionary judgment ("naar de omstandigheden te beoordelen", "naar het oordeel van", "zo nodig geschat" — an open norm)? → open-norm leaf (an injected judgment; the legislator opened the slot).
4. Brute fact that no regulation derives and the law does not delegate as an open norm? → external-fact leaf.

Anti-patterns to flag:

• Orphan plain-param — a value that a regulation actually derives (1 or 2) modeled as a brute input. Smell: a leaf whose name matches an existing endpoint, or a value the text grounds in "vastgesteld op grond van [andere regeling]".
• Negation/complement re-entry — a value that is the logical complement of an existing determination (¬X) modeled as an independent leaf, so the model permits X ∧ ¬X. Derive it from the single determination instead.

The ontology — cluster by concept, not by word

The discriminator is the determination-mode, not the lexeme. For each term the corpus reckons with, tag its mode:

• computed-rule — derived by an endpoint;
• register/administrative — an inschrijving in a register; a formal act;
• factual open-norm — "naar de omstandigheden"; a weging of facts;
• composite — a status = a factual core + a qualifier (e.g. lawful-X = factual-X + a legality predicate);
• brute fact.

Then cluster surface forms into canonical concepts by mode + legal referent, not by spelling:

• False friends — one lexeme, different concepts. A residence/ingezetene term that is register-based in statute A but a factual "naar de omstandigheden"-weging in statute B is a different concept. Binding A's predicate to B's endpoint is a conflation even though the words match and the engine runs.
• Synonyms — different lexemes, one concept → a binding is appropriate.
• Composites — never bind a composite to one of its parts (lawful-resident ↛ factual-resident); decompose into the part-bindings.

Binding-soundness audit

For each existing source-binding P (law A) ← E (law B):

• Same canonical concept (mode + referent)? If not → CONFLATION (false friend); flag.
• Is the consumer a composite and the producer one of its parts (or vice versa)? → PART/WHOLE mismatch; decompose.

Across the corpus:

• Is one concept re-entered as several independent leafs? → DUPLICATION; lost single-source-of-truth and lost mutual-exclusion invariant. Derive the rest from one determination.
• Does a plain-param correspond to something a regulation derives? → ORPHAN; rebind.

Classification of each finding

Bucket	Meaning	Action
structural-fix	the reference type or binding is mechanically wrong vs the derivation/concept facts	rebind / derive from one source / decompose
concept-question (jurist)	whether two concepts are legally equivalent is a normative call (e.g. "do we accept a register as proxy for a factual open norm?")	jurist decides; document the proxy-with-rationale or reject
accept-with-rationale	a deliberate proxy (e.g. a register as best machine-readable indicator of a factual norm)	keep — but model the register as an indicator that feeds the open-norm weging, never as its silent replacement; annotate

Cardinal rule: a register may inform a factual open-norm determination but must not silently replace it — that substitution is the canonical conflation (mirrors the standard "naar de omstandigheden te beoordelen" + "een enkele inschrijving is niet voldoende"-doctrine). Keep the model symmetric: if one side of a stelsel models a concept factually, the mirror side must too, unless a documented decision says otherwise.

Output

• Law-level glossary (per law): term → canonical concept → determination-mode → reference-type → endpoint/leaf id.
• Stelsel-level concept map: concepts as nodes; sound same-concept binding edges; and a flagged list of false-friend edges (lexical match, concept mismatch), orphan plain-params, complement re-entries, and duplications.
• A reference-hygiene report classifying each finding (structural-fix / concept-question / accept-with-rationale), with a jurist-question per concept-equivalence call.

Verification (no hunches)

Confirm a "derived" claim by locating the producing endpoint; confirm a conflation by showing the determination-modes differ, not by the wording. After a rebind/derive/decompose: schema-validation and cross-law-integriteit stay green, and end-to-end outcomes are unchanged where the concept was already equivalent (anti-masking: flip a source fact, see the outcome move). A rebinding that changes outcomes means the concepts were genuinely different — that is a finding, not a regression.

Relation to other skills

• law-letter-fidelity-audit — is the model faithful to the letter (vs toelichting / desired outcome)? Orthogonal to this skill, which asks whether the references are the right type and whether bindings respect concept identity. Run fidelity first (terms letter-true), then this (terms wired and clustered right).
• law-generate — the placement/binding mechanics (source under input:, not parameters:). This skill decides which binding type and whether a binding is conceptually sound.
• regelrecht-stelselanalyse — cross-law-integriteit (dangling / plain-param) is the mechanical layer; this skill adds the conceptual layer on top (same-concept binding, false-friend detection).