ai-system-intakelisted

Create or update the governance record for an AI system or agent — the shared artefact every other Remit assessment reads from. Use this whenever someone describes an AI system, model, agent, copilot, chatbot, or automation and there is any governance, risk, compliance, audit, or regulatory dimension to the conversation; whenever a new AI use case is proposed, onboarded, inventoried, or registered; whenever someone asks "do we need to assess this?", "is this in scope for the AI Act?", or "what AI do we have?"; and always as the first step before any EU AI Act, NIST AI RMF, ISO 42001, DORA, or autonomy assessment, because those skills need a system record to work from. Use it even when the user has not asked for an inventory — if they are describing an AI system that will touch real users or real decisions, the record is what makes everything downstream possible.
patkusch/remit · ★ 0 · AI & Automation · score 72

Install: claude install-skill patkusch/remit

# AI system intake The record you produce here is the spine of every other assessment. Framework skills read it; incident triage annotates it; the evidence pack assembles from it. When each assessment keeps its own private notion of what the system is, they drift, contradict each other, and an auditor finds the contradiction before you do. Your job is to produce a record that is **accurate about what is actually deployed**, not one that describes the design intention. That distinction is most of the value. ## What to produce A YAML or JSON file conforming to [`framework/system-record.schema.json`](../../framework/system-record.schema.json). Default to YAML for human editing unless the user is wiring this into tooling. Name it `<system-id>.record.yaml`. Ask where it should live if there is an existing inventory; otherwise propose `records/`. ## How to run the intake Work conversationally. This is an interview, not a form — the useful answers usually arrive as follow-ups to something the person said in passing. ### 1. Establish what it actually does Get the intended use in language a non-specialist could check. If the answer is abstract ("improves customer experience"), keep asking until you have something falsifiable ("ranks incoming support tickets by predicted urgency, and auto-closes ones scored below 0.2"). Then ask the question that drives most downstream classification: **what happens to a person as a result of this system?** Map to `purpose.decision_consequenc