Automatically fix Azure SDK CI/CD pipeline failures by applying code changes and verifying locally. USE FOR: "fix pipeline failure", "fix CI", "fix failing tests", "auto-fix and commit the fix", "fix build error", "fix mypy/pylint/type-check/lint errors", "auto-fix pipeline", "resolve pipeline failure". DO NOT USE FOR: pipeline analysis (instead use azsdk-common-pipeline-analysis), API design review, SDK publishing. INVOKES: azure-sdk-mcp:azsdk_package_build_code, azure-sdk-mcp:azsdk_package_run_check, azure-sdk-mcp:azsdk_package_run_tests, azure-sdk-mcp:azsdk_verify_setup.
npx skills add https://github.com/Azure/azure-sdk-for-net --skill azsdk-common-pipeline-fixer
This skill automatically fixes Azure SDK CI/CD pipeline failures. It analyzes the failure, identifies the root cause, applies code changes, and verifies the fix locally using build, check, and test tools before committing.
azure-sdk-mcp server and a local clone of the SDK language repo.| Tool | Purpose |
| ---------------------------------------- | ------------------------------------- |
| azure-sdk-mcp:azsdk_verify_setup | Verify local environment is ready |
| azure-sdk-mcp:azsdk_package_build_code | Build the package locally |
| azure-sdk-mcp:azsdk_package_run_check | Run validation/lint/typecheck locally |
| azure-sdk-mcp:azsdk_package_run_tests | Run tests locally |
Prerequisites: azure-sdk-mcp server required. Local clone of the affected SDK language repo. Language build tools installed.
azsdk-common-pipeline-analysis skill.azsdk_package_build_code → azsdk_package_run_check → azsdk_package_run_tests on the package; all must pass. If the environment isn't ready, run azsdk_verify_setup. Never substitute raw shell build/test commands.azsdk_verify_setup, report its error, and stop — don't fall back to raw shell build/test commands.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 azure/azsdk-common-pipeline-fixer 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.