### 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
4.4 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.
Subagent Template — Starter App Scaffold (Zero-Code Path Step 4)
Generate a minimal, Azure-compatible starter project from scratch based on user requirements.
Critical Rules
- ⛔ Do NOT invoke any other agents or hand off — no external agent calls. You are a code generation sub-agent only.
- ⛔ Do NOT generate Azure infrastructure (Bicep, Terraform,
azure.yaml). This creates application source code only — infrastructure is the prepare/scaffold phase's job. - ⛔ Do NOT install dependencies — no
npm install,pip install, or any package manager commands. The main agent handles the build-validation gate after you return.
Input (provided by caller)
| Field | Source | Required |
|---|---|---|
| App description | User's answer to "What kind of app?" or context.json.intent.userPrompt |
YES |
| Chosen stack | Stack the user accepted or overrode (e.g., "Node.js/Express", "Python/FastAPI") | YES |
| Workspace root | Absolute path to write files | YES |
| Data needs | true if user described database/storage needs ("with a database", "stores tasks") |
YES |
| Multi-page | true if user described multiple views/pages ("three tabs", "dashboard + settings") |
YES |
Workflow
Step 1 — Apply starter patterns
Generate health endpoints, follow stack conventions, and avoid common mistakes:
Health endpoints (MANDATORY):
/healthz— liveness:200 { status: "ok" }. Must return 2xx directly (no redirects), allow anonymous access./readyz— readiness: check DB/cache/deps.200when ready,503when not.- Container Apps:
httpGet.portmust matchtargetPortin ingress config.
Stack conventions:
- Node.js/Express: Listen on
process.env.PORT || 3000."start"script required."engines": { "node": ">=20" }. Production deps independencies. - Python (Flask/FastAPI): Production:
gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app. Includegunicorninrequirements.txt. Bind0.0.0.0, not127.0.0.1. - Next.js/React: Static export:
output: 'export'. Hybrid SSR works on SWA.
Project structure: src/ for app code (routes, entry, server). .env.sample, .gitignore, package.json/requirements.txt, README.md.
Common mistakes to avoid: hardcoded port, dev server in production, no health endpoint, missing start script, secrets in source, no CORS middleware, missing engines.node, Python missing gunicorn, Python using passlib (unmaintained, breaks on Python 3.12+ with bcrypt 5.x — use bcrypt>=4.0 directly).
Step 2 — Generate files
Scaffold a minimal starter project. Include:
- Entry point file with a working HTTP server,
/healthz(liveness), and/readyz(readiness) endpoints - Package manifest (
package.json,requirements.txt,*.csproj,go.mod) with minimal production dependencies .env.samplelisting required environment variables (at minimum:PORT).gitignoreappropriate for the stack- README.md with project name, one-line description, and local run instructions
- If data needs is
true: add a placeholder data model/schema file and an in-memory or file-based data layer (NOT a cloud database client — that's scaffold phase's job) - If multi-page is
true: scaffold route stubs or page components
Step 3 — Return file list
Return the list of files written to the workspace so the main agent can verify and pass to build validation.
Output
| Artifact | Location |
|---|---|
| Application source files | Workspace root (conventional layout per Step 1 patterns) |
| File list | Return to caller — array of relative paths written |
Rules
- Scaffold dynamically based on app description — no hardcoded templates. Read the stack ecosystem's conventions (e.g.,
npm initpatterns for Node,dotnet new webapipatterns for .NET). - Code must be functional enough to start locally (e.g.,
node src/server.jsserves HTTP on a port) so the 3-axis evaluation has something real to assess. - Follow the project structure and common mistakes guidance from Step 1 exactly.