Add Azuredevops Agent Skill

Adds Azure DevOps connector to a Power Apps code app. Use when querying work items, creating bugs, managing pipelines, or making ADO API calls.

937 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

601

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/microsoft/power-platform-skills --skill add-azuredevops

What it tells the agent to use

found in the instruction text

Edit edits files in place

Grep reads your files

The instruction itself

8 sections, as written by the author

📋 Shared Instructions: shared-instructions.md - Cross-cutting concerns.

Add Azure DevOps

Workflow

Check Memory Bank → 2. Add Connector → 3. Apply HttpRequest Fix → 4. Configure → 5. Build → 6. Update Memory Bank

Step 1: Check Memory Bank

Check for memory-bank.md per shared-instructions.md.

Step 2: Add Connector

First, find the connection ID (see connector-reference.md):

Run the /list-connections skill. Find the Azure DevOps connection in the output. If none exists, direct the user to create one using the environment-specific Connections URL — construct it from the active environment ID in context (from power.config.json or a prior step): https://make.powerapps.com/environments/<environment-id>/connections → + New connection → search for the connector → Create.

pa app add data-source --connector azuredevops -c <connection-id>

Step 3: Apply HttpRequest Fix (Required)

The generated code has a known issue: the HttpRequest method uses parameters as the parameter name, but the API expects body. Rename parameters to body in these 3 files:

Use the Edit tool to rename parameters to body in each file:

1. src/generated/services/AzureDevOpsService.ts:

Find the HttpRequest method. Rename the parameter and its usage:

// BEFORE (generated):
async HttpRequest(parameters: any) {
  const params = { parameters: parameters, ... };

// AFTER (fixed):
async HttpRequest(body: any) {
  const params = { body: body, ... };

2. .power/appschemas/dataSourceInfo.ts:

Find the visualstudioteamservices → HttpRequest → parameters section. Rename the property key:

// BEFORE (generated):
HttpRequest: {
  parameters: {
    parameters: { ... }

// AFTER (fixed):
HttpRequest: {
  parameters: {
    body: { ... }

3. .power/schemas/visualstudioteamservices/visualstudioteamservices.Schema.json:

Find the /{connectionId}/httprequest → post → parameters array. Change the name field:

// BEFORE (generated):
{ "name": "parameters", "in": "body", ... }

// AFTER (fixed):
{ "name": "body", "in": "body", ... }

Step 4: Configure

Ask the user what Azure DevOps operations they need (query work items, create items, trigger pipelines, etc.).

HttpRequest -- make arbitrary ADO REST API calls:

await AzureDevOpsService.HttpRequest({
  Uri: "https://dev.azure.com/{org}/{project}/_apis/wit/wiql?api-version=7.2",
  Method: "POST",
  Body: JSON.stringify({
    query:
      "SELECT [System.Id], [System.Title] FROM WorkItems WHERE [System.TeamProject] = @project"
  })
});

Docs: Azure DevOps REST API

Use Grep to find specific methods in src/generated/services/AzureDevOpsService.ts (generated files can be very large -- see connector-reference.md).

Step 5: Build

npm run build

Fix TypeScript errors before proceeding. Do NOT deploy yet.

Step 6: Update Memory Bank

Update memory-bank.md with: connector added, HttpRequest fix applied, build status.

Other skills for the same job

different authors, same section of the catalogue

Modal

by christophacham

×3

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.

17k tokens

Github Workflow Automation

by ComeOnOliver

×3

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

Google Cloud Platform CLI - manage GCP resources including Compute Engine, Cloud Run, GKE, Cloud Functions, Storage, BigQuery, and more.

Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems. Masters REST/GraphQL/gRPC APIs, event-driven architectures, service mesh patterns, and modern backend frameworks. Handles service boundary definition, inter-service communication, resilience patterns, and observability. Use PROACTIVELY when creating new backend services or APIs.

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

21k tokens

Bigquery Pipeline Audit

by github

vendor ×1

Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.

Microsoft Store Developer CLI (msstore) for publishing Windows applications to the Microsoft Store. Use when asked to configure Store credentials, list Store apps, check submission status, publish submissions, manage package flights, set up CI/CD for Store publishing, or integrate with Partner Center. Supports Windows App SDK/WinUI, UWP, .NET MAUI, Flutter, Electron, React Native, and PWA applications.

4k tokens

How to use it

Copy the folder

Take microsoft/add-azuredevops from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.