workflow-feature-development
Complete workflow for developing new features from design to deployment. Use when starting a new feature, adding functionality, or building something new.
Works with
--- name: workflow-feature-development description: Complete workflow for developing new features from design to deployment. Use when starting a new feature, adding functionality, or building something new. license: MIT --- # Feature Development Workflow Step-by-step process for developing features properly. ## Phase 1: Design **Agents:** `system-architect` - Design feature architecture - Identify components and boundaries - Define API contracts - Document data flow **Output:** Architecture diagram, component list, API contracts ## Phase 2: Planning **Agents:** `requirements-analyst` - Break down into implementable tasks - Identify dependencies - Estimate timeline - Define acceptance criteria **Output:** Task breakdown, dependency graph, timeline ## Phase 3: Implementation - Implement feature following architecture - Work in small, testable increments - Commit frequently with clear messages ## Phase 4: Review **Agents:** `code-reviewer`, `security-auditor` - Code review for quality and standards - Security review for vulnerabilities - Focus: auth, input validation, data access **Blocking:** Must pass before proceeding ## Phase 5: Testing **Agents:** `test-automator` - Unit tests (80% coverage target) - Integration tests - E2E tests for critical paths ## Phase 6: Performance **Agents:** `performance-engineer` Validate against thresholds: - Response time: <200ms - Memory usage: <100MB - Bundle size: <500KB ## Phase 7: Documentation **Agents:** `technical-writer` - API documentation - User guide updates - Changelog entry ## Phase 8: Deployment Prep **Agents:** `deployment-engineer` Checklist: - [ ] Version bump - [ ] Changelog updated - [ ] Migration scripts ready - [ ] Rollback plan documented ## Success Criteria - [ ] All tests pass - [ ] Security scan clean - [ ] Performance within limits - [ ] Documentation complete ## Rollback Plan 1. Revert database migrations 2. Restore previous version 3. Notify stakeholders
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).
