Summarize a single Azure DevOps work item (and its links and comments) by ID.
npx skills add https://github.com/microsoft/azure-devops-skills --skill boards-work-item-summary
This skill always works with one specific work item ID.
Use Azure DevOps MCP Server tools for all interactions with Azure DevOps.
wit_work_item (action: get): Get a work item from Azure DevOps by its ID.wit_work_item (action: list_comments): Get the list of comments for a work item by its ID.wit_work_item (action: get_batch): Get work item details in batch by their IDs. Use this tool to get the details of linked work items, using the links returned from wit_work_item with action get.wit_work_item with action get and expand=all for the requested ID so that you retrieve fields, relations, and links in a single call.System.Parent or a System.LinkTypes.Hierarchy-Reverse relation), include the parent ID in the set of linked IDs to summarize. You do not need a separate call; instead, rely on wit_work_item with action get_batch in the next step.relations array, collect all linked work item IDs (parent, child, related, and any others). Call wit_work_item with action get_batch once with that list of IDs to get the details of all linked work items.wit_work_item with action list_comments once to get the comments for the original work item.When displaying the results:
MM/DD/YYYY, Priority, and Tags.{ID}Group and order the rows in this sequence: Parent links first, then Child links, then Related links, then any other link types.
Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.
Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.
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.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
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.
Take microsoft/boards-work-item-summary 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.