Pulumi Automation Api logo

Pulumi Automation Api

Organization
pulumi
pulumi-automation-api

Load this skill when a user asks how to run Pulumi programmatically, embed Pulumi in an application, orchestrate multiple stacks in code, build a self-service infrastructure portal, replace pulumi CLI shell scripts with code, or use the Pulumi Automation API (LocalWorkspace, createOrSelectStack, inline programs). Also load for questions about multi-stack sequencing, parallel deployments, or passing outputs between stacks via code.

Overview

Publisherpulumi
Repositoryagent-skills
Skill namepulumi-automation-api
Stars
78
Forks
8
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by pulumi on GitHub. Read the source before you install it.

Installation

Install the Pulumi Automation Api AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/pulumi/agent-skills/tree/main/pulumi/skills/pulumi-automation-api
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/pulumi/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/pulumi/skills/pulumi-automation-api .claude/skills/pulumi-automation-api
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pulumi Automation Api in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pulumi Automation Api on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pulumi Automation Api is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Pulumi Automation API

When to Use This Skill

Invoke this skill when:

  • Orchestrating deployments across multiple Pulumi stacks
  • Embedding Pulumi operations in custom applications
  • Building self-service infrastructure platforms
  • Replacing fragile Bash/Makefile orchestration scripts
  • Creating custom CLIs for infrastructure management
  • Building web applications that provision infrastructure

What is Automation API

Automation API provides programmatic access to Pulumi operations. Instead of running pulumi up from the CLI, you call functions in your code that perform the same operations.

typescript
import * as automation from "@pulumi/pulumi/automation";

// Create or select a stack
const stack = await automation.LocalWorkspace.createOrSelectStack({
    stackName: "dev",
    projectName: "my-project",
    program: async () => {
        // Your Pulumi program here
    },
});

// Run pulumi up programmatically
const upResult = await stack.up({ onOutput: console.log });
console.log(`Update summary: ${JSON.stringify(upResult.summary)}`);

When to Use Automation API

Good Use Cases

Multi-stack orchestration:

When you split infrastructure into multiple focused projects, Automation API helps offset the added complexity by orchestrating operations across stacks:

text
infrastructure → platform → application
     ↓              ↓            ↓
   (VPC)      (Kubernetes)   (Services)

Automation API ensures correct sequencing without manual intervention.

Self-service platforms:

Build internal tools where developers request infrastructure without learning Pulumi:

  • Web portals for environment provisioning
  • Slack bots that create/destroy resources
  • Custom CLIs tailored to your organization

Embedded infrastructure:

Applications that provision their own infrastructure:

  • SaaS platforms creating per-tenant resources
  • Testing frameworks spinning up test environments
  • CI/CD systems with dynamic infrastructure needs

Replacing fragile scripts:

If you have Bash scripts or Makefiles stitching together multiple pulumi commands, Automation API provides:

  • Proper error handling
  • Type safety
  • Programmatic access to outputs

When NOT to Use

  • Single project with standard deployment needs
  • When you don't need programmatic control over operations

Architecture Choices

Local Source vs Inline Source

Local Source - Pulumi program in separate files:

typescript
const stack = await automation.LocalWorkspace.createOrSelectStack({
    stackName: "dev",
    workDir: "./infrastructure",  // Points to existing Pulumi project
});

When to use:

  • Different teams maintain orchestrator vs Pulumi programs
  • Pulumi programs already exist
  • Want independent version control and release cycles
  • Platform team orchestrating application team's infrastructure

Inline Source - Pulumi program embedded in orchestrator:

typescript
import * as aws from "@pulumi/aws";

const stack = await automation.LocalWorkspace.createOrSelectStack({
    stackName: "dev",
    projectName: "my-project",
    program: async () => {
        const bucket = new aws.s3.Bucket("my-bucket");
        return { bucketName: bucket.id };
    },
});

When to use:

  • Single team owns everything
  • Tight coupling between orchestration and infrastructure is desired
  • Distributing as compiled binary (no source files needed)
  • Simpler deployment artifact

Language Independence

The Automation API program can use a different language than the Pulumi programs it orchestrates:

text
Orchestrator (Go) → manages → Pulumi Program (TypeScript)

This enables platform teams to use their preferred language while application teams use theirs.

Common Patterns

Multi-Stack Orchestration

Deploy multiple stacks in dependency order:

typescript
import * as automation from "@pulumi/pulumi/automation";

async function deploy() {
    const stacks = [
        { name: "infrastructure", dir: "./infra" },
        { name: "platform", dir: "./platform" },
        { name: "application", dir: "./app" },
    ];

    for (const stackInfo of stacks) {
        console.log(`Deploying ${stackInfo.name}...`);

        const stack = await automation.LocalWorkspace.createOrSelectStack({
            stackName: "prod",
            workDir: stackInfo.dir,
        });

        await stack.up({ onOutput: console.log });
        console.log(`${stackInfo.name} deployed successfully`);
    }
}

async function destroy() {
    // Destroy in reverse order
    const stacks = [
        { name: "application", dir: "./app" },
        { name: "platform", dir: "./platform" },
        { name: "infrastructure", dir: "./infra" },
    ];

    for (const stackInfo of stacks) {
        console.log(`Destroying ${stackInfo.name}...`);

        const stack = await automation.LocalWorkspace.selectStack({
            stackName: "prod",
            workDir: stackInfo.dir,
        });

        await stack.destroy({ onOutput: console.log });
    }
}

Passing Configuration

Set stack configuration programmatically:

typescript
const stack = await automation.LocalWorkspace.createOrSelectStack({
    stackName: "dev",
    workDir: "./infrastructure",
});

// Set configuration values
await stack.setConfig("aws:region", { value: "us-west-2" });
await stack.setConfig("dbPassword", { value: "secret", secret: true });

// Then deploy
await stack.up();

Reading Outputs

Access stack outputs after deployment:

typescript
const upResult = await stack.up();

// Get all outputs
const outputs = await stack.outputs();
console.log(`VPC ID: ${outputs["vpcId"].value}`);

// Or from the up result
console.log(`Outputs: ${JSON.stringify(upResult.outputs)}`);

Error Handling

Handle deployment failures gracefully:

typescript
try {
    const result = await stack.up({ onOutput: console.log });

    if (result.summary.result === "failed") {
        console.error("Deployment failed");
        process.exit(1);
    }
} catch (error) {
    console.error(`Deployment error: ${error}`);
    throw error;
}

Parallel Stack Operations

When stacks are independent, deploy in parallel:

typescript
const independentStacks = [
    { name: "service-a", dir: "./service-a" },
    { name: "service-b", dir: "./service-b" },
    { name: "service-c", dir: "./service-c" },
];

await Promise.all(independentStacks.map(async (stackInfo) => {
    const stack = await automation.LocalWorkspace.createOrSelectStack({
        stackName: "prod",
        workDir: stackInfo.dir,
    });
    return stack.up({ onOutput: (msg) => console.log(`[${stackInfo.name}] ${msg}`) });
}));

Best Practices

Separate Configuration from Code

Externalize configuration into files or environment variables:

typescript
import * as fs from "fs";

interface DeployConfig {
    stacks: Array<{ name: string; dir: string; }>;
    environment: string;
}

const config: DeployConfig = JSON.parse(
    fs.readFileSync("./deploy-config.json", "utf-8")
);

for (const stackInfo of config.stacks) {
    const stack = await automation.LocalWorkspace.createOrSelectStack({
        stackName: config.environment,
        workDir: stackInfo.dir,
    });
    await stack.up();
}

This enables distributing compiled binaries without exposing source code.

Stream Output for Long Operations

Use onOutput callback for real-time feedback:

typescript
await stack.up({
    onOutput: (message) => {
        process.stdout.write(message);
        // Or send to logging system, websocket, etc.
    },
});

Quick Reference

ScenarioApproach
Existing Pulumi projectsLocal source with workDir
New embedded infrastructureInline source with program function
Different teamsLocal source for independence
Compiled binary distributionInline source or bundled local
Multi-stack dependenciesSequential deployment in order
Independent stacksParallel deployment with Promise.all

Related Skills

  • pulumi-best-practices: Code-level patterns for Pulumi programs

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pulumi Automation Api AI skill do?

Load this skill when a user asks how to run Pulumi programmatically, embed Pulumi in an application, orchestrate multiple stacks in code, build a self-service infrastructure portal, replace pulumi CLI shell scripts with code, or use the Pulumi Automation API (LocalWorkspace, createOrSelectStack, inline programs). Also load for questions about multi-stack sequencing, parallel deployments, or passing outputs between stacks via code.

Why use Pulumi Automation Api on TypingMind?

Because you install it once and use it with any model. Pulumi Automation Api is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pulumi Automation Api in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pulumi/agent-skills/tree/main/pulumi/skills/pulumi-automation-api. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pulumi Automation Api?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pulumi Automation Api?

As many as you like. As long as a model supports skills, you can use Pulumi Automation Api with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pulumi Automation Api AI skill free?

Yes. It is published on GitHub by pulumi under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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