| name | fabric-deploy |
| description | The Engineer skill — deploys Fabric items in dependency-ordered waves and remediates issues found during validation. Use when user says "deploy", "create items", "run deployment", "fix deployment issues", "remediate", or after user sign-off is complete. Do NOT use for design (use fabric-design) or testing (use fabric-test).
|
| pre-compute | ["deploy-script-gen"] |
Fabric Deployment
⚡ Fast-forward: After sign-off approval, run-pipeline.py advance --approve auto-generates deployment artifacts: workspace/ with .platform files, config.yml, deploy script, and taskflow JSON. The agent creates deployment-handoff.md and phase-progress.md during this phase.
Deployment Mode
Check the deploy_mode field in the agent prompt (set in pipeline-state.json):
live — User wants to deploy to a live Fabric workspace. Present the deploy script and guide them through running it.artifacts_only (default) — No live deployment. Review generated artifacts and write the handoff with items in planned status.
Instructions
Step 1: Verify Artifacts
Confirm deploy/ contains: .platform files, config.yml, deploy script, and taskflow JSON (auto-generated by pipeline).
Step 2: Deploy or Review
If deploy_mode is live:
Present the deploy script to the user and instruct them to run it:
cd _projects/[name]/deploy
python deploy-[name].py
The script handles: workspace creation, capacity assignment, fabric-cicd deployment, and Variable Library population — all interactively. The user must have Azure credentials (az login).
After the user confirms deployment is complete, write deployment-handoff.md with items in deployed status.
If deploy_mode is artifacts_only:
Review the generated artifacts. Present a summary of what would be deployed (items, waves, script location). Write deployment-handoff.md with:
deployment_mode: artifacts_only- All items with
status: planned
- Implementation Notes (deviations only)
- Configuration Rationale table
Constraints
- Do NOT read registry JSON files directly — use Python tools (
deploy-script-gen.py, deployment_loader, registry_loader)
- Follow the architecture handoff exactly — no design decisions
- Never proceed to next wave if current wave has failures
Mode 2: Remediation (Phase 3+)
Fix deployment and configuration issues identified during validation.
Step 1: Load Remediation Log
Read _projects/[name]/docs/remediation-log.md. Filter issues where routed_to: engineer.
Step 2: Fix Issues
- Deployment issues — re-deploy item via
fabric-cicd, verify with validate-items.py
- Configuration issues — update settings via Fabric REST API or portal, re-run validation
- Transient issues — wait appropriate interval, retry validation
Step 3: Update Remediation Log
Set status: resolved, fill resolution (max 20 words), record resolved_iteration.
Step 4: Escalate Design Issues
If category: design → set outcome: escalated, STOP. Cannot fix architecture — escalate.
Step 5: Trigger Re-Validation
Set outcome: remediated and advance pipeline to re-trigger QA validation.
Max 3 remediation iterations.
Handoff
After producing the output file, advance:
python _shared/scripts/run-pipeline.py advance --project <project-name> -q
If the output shows 🟢 AUTO-CHAIN → <skill>, invoke that skill immediately — do NOT stop and ask the user.
Only 🛑 HUMAN GATE (Phase 2b sign-off) requires user action.
