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Propose a root cause explanation for an unmatched bank statement item.
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Sub-skill of `compose-domain`. Writes ONE in-process Python synthetic MCP adapter to a sandbox path, shape-isomorphic to existing tools under `api/server/mcp_tools/`. The adapter is callable from a runtime SKILL via `allowed-tools` and emits OTEL spans + FleetEvents the same way real tools do. Returns deterministic synthetic data — no real backend. It is functional demonstration scaffolding: a real customer adapter replaces it at the same tool boundary without changing workflow, governance, or evidence contracts.
Current design-time meta-skill. Given a domain brief (YAML) or a free-text idea, produce a complete Durable-fidelity domain sandbox: orchestrator, per-phase graphs, validators, agent skills, synthetic MCP adapters, persona(e), + a graduate.sh script that mechanically wires everything into the live trees. The current contract encodes: durable.* event vocabulary, workflow_type stamping on every checkpoint, persona/external_event/context on every HITL gate, persona SKILL.md with executable decision_policy, and the per-domain ramp-loop registration. Every generated domain inherits the contract by construction. Sandbox-only; never touches real trees directly. Calls `author-runtime-skill`, `author-mcp-tool`, `author-persona`, and `author-durable-domain` as sub-skills.
Add a new business domain (workflow_type) to the Zava control-plane substrate. Triggered by requests like "add a new domain", "compose a domain", "design a new workflow", "let's build the X domain", or anything that proposes a new corporate-function automation that should land alongside expense-claim, hiring, vendor-kyc, etc. End-to-end recipe: brief → compose-domain sandbox → graduate.sh → validate active pack → VERTICAL-PROOF.md.
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Based on SOC occupation classification
| name | root-cause-explainer |
| description | Propose a root cause explanation for an unmatched bank statement item. |
| allowed-tools | payment.reconcileStatement |
Given an unmatched bank statement item and its classification, propose a brief root cause. Output JSON: {root_cause: }.