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
4.2 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.
Inventory
Scan the workspace to populate the plan. For multi-service workspaces, loop over each service in services[] from classify.md; deduplicate emulators across services per multi-service.md.
Step 1: Prerequisites
Autopilot: Reuse the Project Plan Prerequisites
Autopilot mode only. In autopilot, if .azure/project-plan.md exists and lists Prerequisites (its ### Run and ### Debug groups), carry those prerequisites over wholesale into the debug plan instead of re-deriving them — the planning stage already inventoried them, and autopilot has no approval gate to catch a dropped prerequisite. Add any prerequisites the workspace scan reveals that project-plan is missing, and don't silently override a project-plan entry. Only when project-plan has no Prerequisites do you fall back to deriving them from the workspace scan below.
In interactive (non-autopilot) mode, derive prerequisites from the workspace scan as usual; the user reviews and edits the plan before approving.
Deriving Prerequisites from the Workspace Scan
Identify required tools and then inventory them by following prerequisites.md.
The required tools are derived from a scan of the currently opened workspace project — check only the tools and extensions relevant to the detected project types, runtimes, and Azure bindings. Both tool sets defined in prerequisites.md apply here — the Run tools (Node.js, .NET SDK, Python, Functions Core Tools, ...) and the Debug tools (Docker, Docker Compose, VS Code extensions, ...) — since debugging exercises the full local stack.
For every detected project type, include its VS Code debug-integration extension from the Debug Tools table as its own Debug row (e.g. an Azure Functions project always includes ms-azuretools.vscode-azurefunctions) — these extensions are required for the debug experience and are separate from the Run-group CLI/runtime tools, so never omit them.
Step 2: Azure Dependencies
For each service, identify Azure service dependencies by scanning bindings or SDK packages.
- Functions projects: Scan bindings per project-types.md § functions
- Other project types: Scan dependency files (e.g.
package.json,requirements.txt,*.csproj) for packages that indicate an Azure service dependency
The table below shows common SDK-to-service mappings — this is not exhaustive. Any package that implies connectivity to an Azure service should be mapped accordingly.
| Example Packages | Azure Service | Emulator |
|---|---|---|
@azure/storage-blob, @azure/storage-queue, @azure/data-tables |
Azure Storage | azurite |
pg, postgres, @prisma/client, typeorm, sequelize, Npgsql, psycopg2 |
PostgreSQL | postgresql |
@azure/cosmos |
Cosmos DB | cosmosdb-emulator |
@azure/service-bus |
Service Bus | servicebus-emulator |
@azure/event-hubs |
Event Hubs | eventhubs-emulator |
mssql, Microsoft.Data.SqlClient |
Azure SQL | azure-sql-edge |
Multiple storage bindings (blob + queue + table) consolidate to a single azurite entry. Cross-check
local.settings.json,.env, and app config for existing connection references to confirm findings.
Step 3: API Test Collection Inventory
For each service, identify whether it exposes testable HTTP endpoints or triggers and provide a brief summary for the plan. Inventory the implemented route and trigger registrations in the workspace, not only the route table in .azure/project-plan.md. The project plan intentionally omits derived authentication routes, so include implemented registration, login, and current-user endpoints when API Login is enabled. Detailed endpoint parsing happens during the generation phase.