fabric-deploy

⚠️ Artifacts-only mode: When deploy_mode is artifacts_only, this skill is NOT invoked — the pipeline runner handles this phase deterministically. The instructions below apply only when deploy_mode is live.

Fabric Deployment

⚡ Pre-generated handoff: The runner writes deployment-handoff.md with every item in status: not_started and <!-- AGENT: FILL --> markers before this skill is invoked, alongside deploy/workspace/, config.yml, the deploy script, and the taskflow JSON. Your job after running (or choosing to skip) deployment is to update each item's status to deployed / failed and replace every <!-- AGENT: FILL --> marker with real content.

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, update each item in deployment-handoff.md to status: deployed (or failed with a reason), and replace every <!-- AGENT: FILL --> marker.

If deploy_mode is artifacts_only:

Review the generated artifacts. Present a summary of what would be deployed (items, waves, script location). Update deployment-handoff.md:

• deployment_mode: artifacts_only
• All items set to status: planned
• Fill in Implementation Notes (deviations only) and the Configuration Rationale table
• Replace every <!-- AGENT: FILL --> marker

Constraints

• Do NOT read registry JSON files directly — use Python tools (deploy-script-gen.py, 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.