---

name: deployment-and-data-ops description: "Operate Jarbas and Rosie service setup, data loading, validation, assets, and maintenance workflows safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

Deployment and Data Operations

Use this sub-skill when the task is to install, start, seed, validate, or troubleshoot the service/data lifecycle around Serenata de Amor's Jarbas Django service and Rosie outputs.

Route elsewhere:

• Jarbas REST endpoint semantics, serializers, filters, and response shapes are owned by jarbas-data-api.
• Rosie classifier behavior, model/cache details, and suspicions.xz generation are owned by rosie-suspicion-pipeline.
• Production deployment automation, DigitalOcean updates, cron jobs, and external API publishing are reference-only here; do not execute credential-bound automation unless a human explicitly authorizes the exact target environment.

Fast operating map

1. Identify the environment path: Docker Compose local, local Python service stack, or reference-only production maintenance.

2. Prepare configuration using configuration.md. Never invent or print real credentials.

3. Bring up prerequisites from docker-and-services.md: legacy Python dependencies, PostgreSQL for full Jarbas semantics, optional RabbitMQ/Celery, cache, and optional Node/Elm asset tooling.

4. Run the safe preflight helper before service-changing operations:

   $ python sub-skills/deployment-and-data-ops/scripts/jarbas_manage_check.py --repo-root <serenata-checkout>
   

   Use --no-run to inspect the command and default environment keys without invoking Django.

5. Seed data using data-loading.md and management-commands.md. The safe sample order is migrations first, then reimbursements, companies, suspicions, search vector, and optional tweets/social-media/receipt commands.

6. Build or collect frontend/static assets only when the legacy Node/Elm stack is available; see frontend-assets.md.

7. For maintenance scripts and one-off research data acquisition, classify the script before running anything using research-and-maintenance-scripts.md.

8. Diagnose known failures with troubleshooting.md.

Safe defaults and validation boundaries

• manage.py check is a configuration/import preflight; it does not replace migrations, sample data loads, API tests, or PostgreSQL-backed search validation.
• SQLite can be useful for manage.py check, but full Jarbas behavior uses PostgreSQL-specific fields and search vectors.
• tweets, tweet, receipts, receipts_text, socialmedia, and update have side effects or external dependencies; read their command notes before use.
• Do not run DigitalOcean, cron, deploy, or research fetch scripts as generic setup. Treat them as historical/maintainer workflows unless the user provides credentials, target environment, rollback plan, and authorization.

Bundled files

• configuration.md — environment variables, secret handling, and check-time defaults.
• docker-and-services.md — Docker Compose topology, local install prerequisites, startup/validation sequences.
• data-loading.md — sample dataset formats, load order, validation counts, receipt text and social-media CSV assumptions.
• management-commands.md — command table, flags, prerequisites, outputs, and safety classification.
• frontend-assets.md — Node 8 / Elm 0.18 asset workflow and pitfalls.
• research-and-maintenance-scripts.md — research, cron, update, and deploy script classifications.
• troubleshooting.md — common failures and recovery steps.
• jarbas_manage_check.py — safe wrapper around python manage.py check.