adapt-workflow
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Works with
--- name: adapt-workflow description: Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context. license: MIT --- ## MANDATORY PREPARATION Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Additionally gather: what the workflow is being adapted to. --- Adapt a working workflow for a different context. ### Adaptation Assessment | Dimension | Current | Target | Impact | |-----------|---------|--------|--------| | Model provider | ? | ? | Prompt format, capabilities, pricing | | Model tier | ? | ? | Context window, reasoning ability | | Deployment env | ? | ? | Latency, availability, compliance | | Team structure | ? | ? | Monitoring, escalation, ownership | | Data sensitivity | ? | ? | Guardrails, logging, access controls | ### Provider Adaptation - **Prompt format**: Adjust for provider-specific features - **Context limits**: Resize context budget for different window sizes - **Capability gaps**: Identify features available in one provider but not another - **Pricing model**: Recalculate cost estimates - **API differences**: Update tool calling interfaces, error handling, retry logic ### Environment Adaptation - **Latency requirements**: Adjust timeout values, caching strategy - **Compliance**: Add or adjust guardrails, logging, and data handling - **Scale**: Adjust concurrency limits, batch sizes - **Monitoring**: Adapt alerting thresholds ### Adaptation Checklist - [ ] All provider-specific APIs updated - [ ] Context budget recalculated for target model - [ ] Cost estimates updated with target pricing - [ ] Guardrails adjusted for target compliance requirements - [ ] Tests updated and passing in target environment - [ ] Documentation updated for target team ### Recommended Next Step After adaptation, run `/evaluate` to verify the workflow performs correctly in the target environment, then `/diagnose` for a full health check. **NEVER**: - Assume prompts work identically across providers - Copy production config to development without adjusting guardrails - Adapt without updating the evaluation suite - Skip cost re-estimation
More Deployment & CI/CD skills
azure-enterprise-infra-planner
microsoft/azure-skills
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment', 'Azure Backup for VM workloads'. PREFER azure-prepare FOR app-centric workflows.
azure-kubernetes-app-deploy
microsoft/azure-skills
Use when deploying an existing web application or API to an already-running Azure Kubernetes Service cluster. Detects the framework, generates a Dockerfile and Kubernetes manifests, validates against AKS Deployment Safeguards, and deploys with verification. WHEN: deploy app to AKS, deploy to existing AKS cluster, containerize app for Kubernetes, generate K8s manifests for Azure, set up CI/CD for AKS, my AKS deployment is failing safeguard checks, I have a Django/Express/Spring Boot app to run on AKS. DO NOT USE FOR: creating or provisioning an AKS cluster (use azure-kubernetes), assessing migration to AKS Automatic (use azure-kubernetes-automatic-readiness), or deploying to non-AKS targets like Web Apps, Container Apps, or Functions.
finetuning
microsoft/azure-skills
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
