django.md

Bookkeeping practices: form collection using Django

Summary

A form collection blueprint for bookkeeping practices built with Django on Ample. Domain schema: client_entities (legal_name, entity_type, accounting_software, period_cadence): clients and their bookkeeping cadence; engagements (client_entity_id, period_start, period_end, close_status): monthly or quarterly close engagements; document_requests (engagement_id, category, due_at, status, uploaded_object_key): receipts, statements and payroll summaries requested; close_checklists (engagement_id, item, done_at): close tasks per period; uploads (id, owner_reference, object_key, content_type, size, uploaded_at): validated uploads linked to their owner. Public information: service packages and cadence, onboarding checklist, contact details. Kept out of scope until handling is reviewed: bank statements and receipts, payroll data, login credentials for accounting software. Never store client accounting-software credentials in these flows; financial records are sensitive and no compliance is claimed. Technical basis verified on Django: content-type and size validation that rejects disallowed files, an object written to a private bucket and a row linking the record to the object key (reject=ok upload=ok linked=ok).

Infrastructure requirements

Prerequisites

Exact tested configuration

Steps

  1. Model the bookkeeping practices domain. Create the tables client_entities, engagements, document_requests, close_checklists, uploads. Clients and their bookkeeping cadence lives in client_entities; keep the sensitive classes (bank statements and receipts, payroll data, login credentials for accounting software) out of this schema.

  2. Workflow step 1. Model document requests per engagement with due dates and status

  3. Workflow step 2. Validate uploaded files (type, size) before storing them in the private bucket and linking them to the request

  4. Workflow step 3. Authorize uploads by client entity

  5. Workflow step 4. Deploy and verify rejection of disallowed files and a linked upload

  6. Deploy. Run the synchronous deploy once and read the result. Re-running with no change is a no-op.

    ample deploy . --name <app-name> --public --start "python3 run.py" --env S3_ENDPOINT=... --env S3_REGION=... --env S3_BUCKET=... --env S3_ACCESS_KEY_ID=... --env S3_SECRET_ACCESS_KEY=...
    
  7. Verify. Run the pattern self-test(s) from the example (/p/browser-file-uploads) and your own acceptance checks for the bookkeeping practices workflow.

    ample logs <deployment_id> --kind build
    

Tested examples

Success checks

Limitations

Cost estimate

Estimated 10.00 USD per month (size prices from pricing.toml at build revision a1b8c38919e59cd035ebabaced73cf84ece24371).

Always-on monthly price of the tested sizes; apps and databases auto-pause when idle. Buckets are allocation-priced per quota and not included.

Verification evidence

Last verified: 2026-09-20T03:31:44Z

Execution binding

MCP tool ample_deploy (registry mcp:ample_deploy), schema hash 876465fce906da0c observed 2026-09-21T03:19:59.461917+00:00 at revision 49962bcade4f, binding state current, required scopes: servers:write, databases:read, buckets:read.