Sven laptop
635a130f0b
### Summary of Changes
1. Documentation Consolidation:
• Relocated the docs/ folder and all repository .md files (README.md, DEPLOYMENT.md, DOCKER_README.md,
DOMAINS_README.md, GETTING_STARTED.md, CI_CD_PORTAINER_GUIDE.md, ADMIN_DEPLOYMENT_SUMMARY.md,
SECURITY_PRIVATE_FILES.md, handoff.md, azure-project-plan.md, plus service-specific READMEs) into .
• Updated document links in README.md.
2. AI Agent Project Map:
• Created PROJECT_MAP.md in the project root containing complete architecture diagrams, service specs, ports, DB
schemas, environment variables, and developer workflows.
• Added PROJECT_MAP.md to .gitignore.
3. Monitoring UI & Infrastructure Integration:
• Added monitoring_ui service to docker-compose.yml (port 5175).
• Configured Gitea workflow docker-build.yml for monitoring_ui builds.
• Added CORS middleware headers in index.js.
• Added nginx.conf copy step in Dockerfile.
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### Recommended Commit Message
docs: consolidate project documentation into monitoring_ui and add agent project map
- Move all markdown documentation files and docs/ folder into monitoring_ui/docs/
- Update documentation hub index (monitoring_ui/docs/README.md) with relative paths
- Generate comprehensive AI agent reference map (PROJECT_MAP.md) in repository root
- Add PROJECT_MAP.md and monitoring_ui build output/env overrides to .gitignore
- Integrate monitoring_ui service into docker-compose.yml and Gitea CI workflow
- Configure CORS handling in employee_api for monitoring dashboard access
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### Alternative Short Commit Message
refactor(docs): move docs to monitoring_ui and create AI agent PROJECT_MAP.md
2.5 KiB
Important: This skill provides guidance and recommended instructions to assist the AI system. Outputs are not guaranteed to be complete, correct, secure, or applicable to every scenario. Results should be reviewed and validated by a human before being applied. The AI model may choose not to follow all instructions exactly, and additional verification may be required.
End-to-End Wire-Up Verification
Read at Step 4. How to run the frontend and backend together and prove they communicate.
Run both processes concurrently (cross-platform)
Start each in its own terminal (async), never with shell-specific backgrounding (&, Start-Job chains):
- Backend — the run command from the artifact, e.g.
func start(cwd = backend folder). Wait until the host prints that all functions registered. - Frontend —
npm --prefix services/web run dev(cwd-independent). Wait until the dev server prints its local URL.
The frontend dev proxy (configured in Step 3) forwards /api to the backend, so the two talk over localhost.
Prove the wiring
You need at least one real /api/... request from the frontend that hits the backend and returns 200 with live data — not a mock placeholder. Any one of:
- Logs: the backend host logs an incoming
GET /api/...→200that originated from the dev server (not your manual curl). - Browser: load a page with the browser tool; confirm it renders data that came from the backend (and that the network panel shows the
/apicall succeeding). The data must match what the empty-schema backend returns (often an empty list rendering the "empty" state — that is still a valid live response, distinct from mock content). - Write path (if the app has one): perform a create/update in the UI; confirm the backend receives it and the UI reflects the result.
Capture evidence & shut down
- Record the proof: the request path, method, and status (e.g.
GET /api/users → 200) that demonstrates frontend → backend wiring. - Stop both processes cleanly when done (do not leave servers running).
Distinguishing live from mock
If a page still shows the scaffold's mock content (named demo records, lorem data) while the backend DB is empty, the frontend is still on mock data — return to Step 3. A correctly wired app against an empty schema typically shows empty/loading states populated by real (empty) responses, or real data you created via a write path.