create-mdslisted

# create-mds > **Status**: v0.10.0 — default stack is **Tailscale + dlt + BigQuery + dbt-core + a single linear script on systemd timers + (opt-in) MCP**, on a small disposable VPS. Phase 1, Phase 2, and Phase 3 playbooks complete, with a discovery-and-adapt step (Step 0) that asks what the user already has before provisioning, and an early harness write (Step 0c) that drops a per-client `CLAUDE.md` + `status: building` marker into the folder before provisioning. Airbyte OSS + cron are kept as documented alternatives, not the default. See [`shared-references/ai-native-principles.md`](../../shared-references/ai-native-principles.md) for the design philosophy this skill must honor, and [`shared-references/discovery-and-adaptation.md`](../../shared-references/discovery-and-adaptation.md) for the ask-first discipline. ## What this skill does Builds a complete Modern Data Stack for a PYME from zero — no existing infrastructure assumed. End state: - A small disposable VPS joined to a Tailscale tailnet, running dlt + dbt in Python venvs - A BigQuery project with a **write** service account, a **budget alert**, and `raw_<source>` datasets receiving data - A GitHub repo holding the dlt pipeline + reconcile scripts, the dbt project, the systemd units, the per-client `CLAUDE.md`, and the `.agentic-data-engineer.json` marker - One or more data sources loading via dlt, each **reconciled** (row-count / freshness / sequence-gap — mandatory) - A **single linear pipeline script** (`dlt lo