> Microsoft 365 tenant administration for Global Administrators. Use for tenant setup, Azure AD user management, Exchange Online and Teams config, Conditional Access policies, license management, and PowerShell bulk-operation scripts.
npx skills add https://github.com/borghei/Claude-Skills --skill ms365-tenant-manager
The agent generates production-ready PowerShell scripts for M365 tenant setup, bulk user provisioning, Conditional Access policies, security audits, and license management. It automates user lifecycle operations (onboarding, offboarding), recommends license SKUs by role, and produces 7-category security audit reports via Microsoft Graph.
-WhatIf support.Before generating scripts, confirm these inputs. If any is unknown or vague, ASK — do not assume:
tenant_setup.py vs powershell_generator.py vs user_management.py)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
The scripts are importable Python modules (instantiate a class, not a CLI). Full API, parameters, and worked examples are in references/tools-and-workflows.md.
| Tool | Purpose | Entry point |
|------|---------|-------------|
| powershell_generator.py | Security audit, Conditional Access, and bulk license scripts | from powershell_generator import PowerShellScriptGenerator |
| user_management.py | Provisioning, offboarding, license/group recommendations, validation | from user_management import UserLifecycleManager |
| tenant_setup.py | Setup checklist, DNS records, setup script, license distribution | from tenant_setup import TenantSetupManager |
Load the reference that matches the task — keep this file lean and pull detail on demand:
What This Skill Covers
-WhatIf support, and Microsoft Graph best practicesWhat This Skill Does NOT Cover
senior-devops skill and Microsoft AD Connect toolingsenior-secops for device security posture| Skill | Integration | Data Flow |
|-------|-------------|-----------|
| senior-secops | Security audit findings feed into SecOps incident response and threat remediation workflows | Audit CSV reports (MFA status, admin roles, inactive users) → SecOps triage and hardening actions |
| senior-devops | Tenant setup scripts integrate with infrastructure-as-code pipelines for repeatable deployments | Generated PowerShell scripts → CI/CD pipeline execution → tenant configuration state |
| senior-architect | License distribution recommendations and tenant topology inform enterprise architecture decisions | License cost analysis and user count projections → architecture capacity planning |
| code-reviewer | Generated PowerShell scripts can be reviewed for security anti-patterns and credential handling | PowerShell script output → code review for hardcoded secrets, missing error handling |
| aws-solution-architect | Multi-cloud identity federation between Azure AD and AWS IAM for organizations using both platforms | Azure AD tenant configuration → cross-cloud SSO and role mapping |
| senior-security | Conditional Access policies and MFA enforcement align with broader organizational security posture | CA policy configurations and security audit results → security policy compliance validation |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take borghei/ms365-tenant-manager from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.