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### 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.
──────
### 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
──────
### Alternative Short Commit Message
refactor(docs): move docs to monitoring_ui and create AI agent PROJECT_MAP.md
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Markdown
60 lines
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Markdown
<!-- azure-cor-disclaimer -->
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> **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.
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# Subagent Template — Security + Adversarial Review (Steps 6–9)
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Review generated IaC for security compliance and correctness. Follow the workflow below — each step specifies which reference to read and what to check.
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## Critical Rules
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- ⛔ **Do NOT invoke any other agents or hand off** — no external agent calls of any kind. Use the procedures in THIS file only.
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- ⛔ **Do NOT run `az deployment` commands** — review is read-only analysis of generated files.
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- ⛔ **Do NOT modify IaC files** — report findings only. The caller fixes issues.
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## Input (provided by caller)
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| Field | Required |
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|-------|----------|
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| All generated IaC file contents (every `.bicep` or `.tf` file) | YES |
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| `prepare-plan.json` — services (service types, SKUs), naming, deploymentVariables sections | YES |
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| `scaffold-manifest.json.files[]` list | YES |
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| `prereq-output.json.warnings[]` — all prereq warnings that require IaC fixes | YES |
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## Output
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Return JSON (≤1000 tokens):
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```json
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{
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"findings": [
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{ "layer": "L1|L2|L3|L4", "file": "modules/app.bicep", "claim": "...", "rating": "VERIFIED|PLAUSIBLE|FLAGGED", "detail": "..." }
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],
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"summary": "N/N VERIFIED, N PLAUSIBLE, N FLAGGED"
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}
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```
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## Workflow
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### Step 1 — Read security patterns + run L1 security baseline
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Read [bicep-patterns-security.md](bicep-patterns-security.md) and [rbac-roles.md](rbac-roles.md).
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**Do:** Check every generated IaC file against ALL security checks defined in the reference file. The file contains the complete check table with FLAGGED conditions, edge cases, and Bicep code patterns. Do NOT guess checks from memory — use the reference file as the checklist.
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### Step 2 — Read checklist + run L2–L4 adversarial review
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Read [self-review-checklist.md](self-review-checklist.md).
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**Do:** First run the **cross-module reference trace** from the checklist's § Cross-Module Reference Validation: parse every `module` call in `main.bicep`, read each target module's `param`/`output` declarations and `secrets[]` entries, then verify every reference resolves (params passed match params declared, outputs referenced exist, every CA `secretRef` has a matching native `secrets[]` entry — ⛔ NO `keyVaultUrl` / Key Vault). Then run L2–L4:
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- **L2 (Pattern Validation):** File structure matches `main.bicep` → `modules/*.bicep`, naming follows plan, Container Apps uses two-phase wiring, every `files[]` entry exists on disk, no `azure.yaml`, cross-module references all resolve
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- **L3 (Hallucination Detection):** Resource names match `naming.resources[]` exactly, API versions are real (verify via `az bicep build`), SKU names match plan, no invented resource types
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- **L4 (WAF Alignment):** Check per-pillar:
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- Reliability: zone redundancy (prod SKUs), health probes, GRS storage, min replicas ≥1
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- Security: managed identity, on-compute app-internal secrets (no Key Vault), HTTPS+TLS 1.2, no public blob, no `administratorLogin`
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- Cost: SKU matches budget, scale-to-zero for dev/test CA, free grants applied
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- Ops: App Insights, 5 AppOnboard tags, all values parameterized
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- Performance: autoscale (prod), CDN for SPA, connection pooling, cache tier
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### Step 3 — Compile findings + return
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**Do:** Merge L1–L4 results into the findings JSON. Apply rating per [self-review-checklist.md](self-review-checklist.md) § Rating System: VERIFIED (evidence confirms claim), PLAUSIBLE (no counter-evidence but unverified), FLAGGED (evidence contradicts or missing critical pattern). ⛔ FLAGGED at L1 (Security) or L3 (Hallucination) → caller must fix before deploy. Return to caller.
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