# Nrakow Ae Skills Dev New Project Setup > When to Use This Workflow - Skill: `tomevault-io/nrakow-ae-skills-dev-new-project-setup` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/nrakow-ae-skills-dev-new-project-setup` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/nrakow-ae-skills-dev-new-project-setup/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/nrakow-ae-skills-dev-new-project-setup --- ## When to Use This Workflow Use `new-project-setup` when starting an analytics engineering project from zero: no dbt project exists yet, you're onboarding a new team, or you're rebuilding a project from scratch. If a dbt project already exists, use individual skills instead of this workflow. ## Overview This workflow sequences six skills in order. Each phase builds on the previous one. Complete each phase before moving to the next. --- ## Phase 1: Capture Stack Context **Skill**: `data-stack-context` Before writing any code, capture the team's full analytics stack. This file drives all subsequent skill decisions. **What to do:** 1. Check if `.claude/data-stack-context.md` already exists. If it does, skip to Phase 2. 2. Invoke the `data-stack-context` skill. 3. Use Path A (auto-draft) if `dbt_project.yml` or `profiles.yml` already exist. Use Path B (conversational) if starting from zero. 4. Confirm the context file is written to `.claude/data-stack-context.md`. **Phase complete when**: `.claude/data-stack-context.md` exists and covers warehouse, dbt version, orchestrator, and BI layer. --- ## Phase 2: Scaffold the dbt Project **Skill**: `dbt-project-setup` Set up the project structure, profiles, packages, and schema naming conventions. **What to do:** 1. Invoke the `dbt-project-setup` skill. 2. Generate `dbt_project.yml`, `profiles.yml`, `packages.yml`, and the `generate_schema_name` macro. 3. Run `dbt deps && dbt debug` to confirm connectivity. **Phase complete when**: `dbt debug` passes and `dbt parse` returns no errors. --- ## Phase 3: Establish SQL Style Guide **Skill**: `sql-style-guide` Lock in formatting and naming standards before writing any models — it's much harder to enforce retroactively. **What to do:** 1. Invoke the `sql-style-guide` skill. 2. Generate a `.sqlfluff` config tailored to the team's warehouse dialect. 3. Document key conventions (CTE naming, column aliasing, join style). **Phase complete when**: `.sqlfluff` config exists and `sqlfluff lint` runs without configuration errors. --- ## Phase 4: Design the Data Model Architecture **Skill**: `data-modeling` Plan the layer structure and identify initial domains before writing the first model. **What to do:** 1. Invoke the `data-modeling` skill. 2. Identify 2-3 initial data domains (e.g., customers, orders, events). 3. Sketch the staging → intermediate → mart flow for each domain. 4. Run `node tools/clis/schema-introspect.js` if source schemas are already accessible. **Phase complete when**: An entity-relationship sketch and initial model list exist for the first domain. --- ## Phase 5: Set Up Testing Strategy **Skill**: `data-quality-testing` Establish the testing standard before writing models — define what "good" means. **What to do:** 1. Invoke the `data-quality-testing` skill. 2. Define the minimum test set per model tier (staging, intermediate, marts). 3. Add Elementary or Soda if the stack includes an observability tool. **Phase complete when**: Testing standards are documented and at least one example `schema.yml` with tests is written. --- ## Phase 6: Configure CI/CD **Skill**: `dbt-ci-cd` Automate testing and deployment so the project is production-ready from day one. **What to do:** 1. Invoke the `dbt-ci-cd` skill. 2. Set up a CI workflow that runs `dbt build --select state:modified+` on PRs. 3. Configure production job scheduling in dbt Cloud or the orchestrator. **Phase complete when**: A CI pipeline exists and a test PR triggers a dbt build automatically. --- ## Final Verification ```bash dbt debug # Confirm warehouse connectivity dbt parse # Confirm no compilation errors dbt deps # Confirm packages installed sqlfluff lint models/ # Confirm style rules enforced ``` ## Verify Your Work **Do not present output from this skill as complete until every command below passes without error.** If a command fails, consult "If Something Goes Wrong" before asking the user. - Run `dbt debug` to confirm warehouse connectivity and that `profiles.yml` is correctly configured for your target environment. - Run `dbt parse` to confirm no compilation errors exist across all model paths defined in `dbt_project.yml`. - Run `dbt deps` to confirm all packages in `packages.yml` resolved and installed without version conflicts. - Run `sqlfluff lint models/` to confirm the `.sqlfluff` config is valid and style rules are enforced without configuration errors. - Open a test PR and confirm the CI pipeline triggers automatically and runs `dbt build --select state:modified+`. ## If Something Goes Wrong - **`dbt debug` fails**: check `profiles.yml` path and warehouse credentials. Ensure env vars are set for the correct target. - **`dbt parse` fails**: usually a missing `ref()` or misconfigured model path in `dbt_project.yml`. Check `model-paths` setting. - **`dbt deps` fails**: check `packages.yml` for version conflicts. Run `dbt deps --upgrade` to resolve. - **CI never triggers**: confirm the workflow file is in `.github/workflows/` and the branch protection rule runs the check. --- > Source: [nrakow/ae-skills-dev](https://github.com/nrakow/ae-skills-dev) — distributed by [TomeVault](https://tomevault.io).