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Downloads Dutch official legal publications including national laws, local regulations, and implementation policies from government sources (wetten.overheid.nl, lokaleregelgeving.overheid.nl). Use this skill proactively when: user mentions downloading, fetching, or importing a Dutch law by name or BWB/CVDR identifier, when a source.regulation reference points to a law not in the corpus, or when user provides a wetten.overheid.nl URL. Activate automatically when user mentions a specific Dutch law name, BWB identifier (BWBR...), or CVDR identifier.

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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.

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Bouwt de audit- en workshop-producten waarmee juridische experts een machine-leesbare (regelrecht-YAML) vertaling van wet- en regelgeving valideren in een live sessie. Gebruik dit bij het voorbereiden van een expert-workshop of validatie-sessie over een dossier, het maken van een scope-/stelselanalyse, per-artikel audit-checklists, facilitator-materiaal, testcase-scenario's, of sessie-verslagen (intern + extern). Dossier-agnostisch; de regelrecht-methode (YAML, formules, untranslatables, source/override, legal_basis, engine-trace) is de vaste taal. Voor analytische desk-review van een corpus zonder live sessie (wetgevings-/stelselfouten, coverage, multi-agent review): zie de zusterskill regelrecht-stelselanalyse.

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Genereert uit een (semi-)gevalideerd regelrecht-corpus een geloofwaardige service-PoC (burger- + behandelaar-portaal, of een headless beslis-service) en zet die in als validatie-instrument met uitvoeringsexperts — om tastbaar te maken wat nodig is om een wet rechtvaardig in de praktijk te brengen, gezien vanuit de betrokkene. Gebruik dit als workshop-fase ná de logica-/scenariovalidatie (audit-products) om de dienstverlening-kant te valideren: welke service impliceert de wet, waar zit menselijk oordeel, welke last ligt bij de burger, welke termijnen, waar is de wet hard of onduidelijk. Dossier-agnostisch; de regelrecht-methode (hooks, legal_character, untranslatables, source/implements, type_spec, receipts) is de vaste taal. Voor de logica-/scenariovalidatie zelf: zie de zusterskill regelrecht-audit-products.

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Front-door router voor het werken aan een regelrecht-dossier. Gebruik dit aan het begin van een dossier, of bij twijfel over waar te beginnen — het triageert de situatie en routeert naar de juiste skill (regelrecht-stelselanalyse voor desk-review/corpus-completion, regelrecht-audit-products voor live expert-validatie) en de juiste volgorde. Bevat het routeringsprincipe: feitelijke defecten routeren naar de desk, oordeels-/praktijkvragen naar de workshop. Dossier-agnostisch.

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Orchestrates the full law interpretation pipeline: MvT research, machine_readable generation with validation/BDD testing, and reverse validation. Invokes /law-mvt-research, /law-generate, and /law-reverse-validate sequentially. Use this skill proactively when: user wants to make a law executable, add machine_readable sections, or mentions "interpret" in the context of a Dutch law YAML file. Activate automatically when user opens a corpus YAML file without machine_readable sections and wants to make it executable.

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Generates machine_readable execution logic for Dutch law YAML files: writes the machine_readable sections, validates against the schema, and (when a shell is available) runs the BDD suites until the model is correct. Use this skill proactively when: editing or creating machine_readable sections in law YAML files, working with corpus regulation files, or when user mentions 'generate', 'machine_readable', or wants to make a law executable. Activate automatically when user discusses law YAML files that need executable logic.

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Use when analyzing how Plugin Inspector rule, warning, deprecation, or compatibility changes affect the ClawHub plugin corpus. Guides agents to export plugins from ClawHub, run `plugin-inspector batch`, and summarize affected plugins and finding frequency.

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Use for a whole-corpus, read-only audit of tracked stablecoin static metadata and domain sidecars, especially after catalog changes or when factual or internal-consistency drift needs review.

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Official OpenProof Client. Register agents and publish research to the Founding Corpus. Supports Articles (Markdown) and Papers (LaTeX/JSON).

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Report the health of the central system spec at `docs/system/` across seven checks — coverage gaps, stale elements, dangling anchors, duplicate anchors, orphan shards, unillustrated elements, and shards missing their kind annotation. Report-first and read-only: it repairs nothing until a human confirms a specific item. Use when the corpus has drifted, after a merge that touched `docs/system/`, or from `/archive` Step 5.5 in report-only mode. Requires `memory.architecture_map.enabled`.

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Standard operating procedure for producing a documentation page end to end — gather source material, classify, draft against the measured corpus profile, then run the reader-level and humanizer passes and both gates. Use for any page on a documentation surface, or when rewriting a page that reads as machine-written. Orchestrates technical-writing, reader-level and humanizer in a fixed order.

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Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.

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Bootstrap the central system spec at `docs/system/` for a repository that has never had one. Scans the governed surface, proposes a concept map clustered by directory, and materializes elements + shards ONLY after a human confirms that map. Use when adopting the baseline on an existing project, or when the corpus needs re-deriving from scratch. Re-runnable. Requires `memory.architecture_map.enabled` and a declared `governed_surface`.

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Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.

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Use when implementing a UI brief on a stack with no framework executor — Svelte, Astro, Angular, plain HTML. Stack-independent sibling of react-shadcn-ui; idiom from the stack corpus.

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Grounded design brief from the corpus — style, WCAG color tokens, typography, layout, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart call; feeds fe-design.

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Grounded brand decisions from a curated corpus — archetype, voice, naming, colour psychology, logo-style fit, messaging frameworks, archetype→type mapping. Use to ground brand strategy and identity.

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Ground a brand strategy from the corpus — archetype, opposable positioning, voice and tone, messaging framework. Use to decide who a brand is for, what it stands for, and how it sounds.

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Semantic audit of the IDD instruction corpus for leaked session context, cross-file contradictions, fresh-memory completability gaps, automation blockers, and restatement-discipline drift. Use on request to audit .github/instructions, the issue-authoring skill bundle, and the installed agent entry files (CLAUDE.md, AGENTS.md, GEMINI.md, .github/copilot-instructions.md). Read-only — never edits files or mutates issues.

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Process the batch of incoming Bridge Classroom feedback. Pull open `classroom-feedback` issues from the PBS repo, cluster them by defect class, verify each against the actual board, and fix at the right altitude — patch a single board, or (when a class recurs) build a gate, sweep the whole corpus, and regenerate/drop collectively. Engine bugs route to the Classroom repo; bridge-judgment calls go to Rick. Nothing leaves the machine without David's OK. Invoke as `/issue` (optionally with an issue number or scenario to restrict the run).

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Derive or update the auto mode `autoMode.environment` block from the event corpus — which repos, hosts, buckets, and CLIs your work actually touches.

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Use when locating TBQ3 documentation, function references, examples, or source docs under docs without loading the full corpus; supports rg-based search and curated topic/function indexes.

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Squeeze three to five durable lessons out of the owner's own words from the last seven days into a weekly note in the insights folder. The LLM reads only a collector's digest, never the raw corpus. Triggers: "/wisdom-distill", "distill my week", "weekly wisdom".

日本語の概要は準備中です。原文の説明を表示しています。