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Gcp Cloud Functions

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BagelHole
gcp-cloud-functions

Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namegcp-cloud-functions
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Gcp Cloud Functions 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/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/gcp-cloud-functions
  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/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/infrastructure/cloud-gcp/gcp-cloud-functions .claude/skills/gcp-cloud-functions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gcp Cloud Functions 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 Gcp Cloud Functions 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 Gcp Cloud Functions 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.

GCP Cloud Functions

Build and deploy event-driven serverless applications with Google Cloud Functions (Gen1 and Gen2).

When to Use

  • Processing webhooks, API endpoints, or lightweight HTTP backends
  • Reacting to events from Pub/Sub, Cloud Storage, Firestore, or Eventarc
  • Running scheduled tasks (cron) without maintaining a server
  • Building data-processing pipelines triggered by file uploads
  • Prototyping microservices before committing to Cloud Run or GKE

Prerequisites

  • Google Cloud SDK (gcloud) installed and authenticated
  • APIs enabled: Cloud Functions, Cloud Build, Artifact Registry, Cloud Run (Gen2)
  • IAM role roles/cloudfunctions.developer (or roles/run.developer for Gen2)
bash
gcloud services enable cloudfunctions.googleapis.com cloudbuild.googleapis.com \
  artifactregistry.googleapis.com run.googleapis.com eventarc.googleapis.com

Gen1 vs Gen2 Comparison

FeatureGen1Gen2 (recommended)
RuntimeCloud Functions infraBuilt on Cloud Run
Max timeout9 minutes60 minutes
Max memory8 GB32 GB
Concurrency1 request/instanceUp to 1000/instance
Traffic splittingNoYes
Eventarc triggersNoYes

Deploy an HTTP Function (Gen2)

bash
# Python HTTP function
gcloud functions deploy hello-http \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --allow-unauthenticated \
  --entry-point=hello_http \
  --memory=256Mi --timeout=60s \
  --min-instances=0 --max-instances=100 \
  --set-env-vars=APP_ENV=production --source=.

# Node.js HTTP function
gcloud functions deploy hello-node \
  --gen2 --region=us-central1 --runtime=nodejs20 \
  --trigger-http --allow-unauthenticated \
  --entry-point=helloNode --memory=256Mi --source=.

Deploy a Pub/Sub Triggered Function

bash
gcloud pubsub topics create order-events

gcloud functions deploy process-order \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-topic=order-events \
  --entry-point=process_order \
  --memory=512Mi --timeout=120s --retry \
  --service-account=order-processor@${PROJECT_ID}.iam.gserviceaccount.com \
  --source=.

Deploy a Cloud Storage Triggered Function

bash
gcloud functions deploy process-upload \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
  --trigger-event-filters="bucket=my-upload-bucket" \
  --entry-point=process_upload \
  --memory=1Gi --timeout=300s --source=.

Deploy a Scheduled Function

bash
gcloud functions deploy daily-cleanup \
  --gen2 --region=us-central1 --runtime=python312 \
  --trigger-http --no-allow-unauthenticated \
  --entry-point=daily_cleanup --source=.

gcloud scheduler jobs create http daily-cleanup-job \
  --schedule="0 2 * * *" \
  --uri="https://us-central1-${PROJECT_ID}.cloudfunctions.net/daily-cleanup" \
  --http-method=POST \
  --oidc-service-account-email=scheduler-sa@${PROJECT_ID}.iam.gserviceaccount.com \
  --location=us-central1

Python Function Examples

python
# main.py
import functions_framework
import base64, json
from flask import jsonify
from google.cloud import firestore

@functions_framework.http
def hello_http(request):
    """HTTP Cloud Function."""
    name = request.args.get("name", "World")
    return jsonify({"message": f"Hello, {name}!", "status": "ok"}), 200

@functions_framework.cloud_event
def process_order(cloud_event):
    """Triggered by a Pub/Sub message."""
    data = base64.b64decode(cloud_event.data["message"]["data"]).decode("utf-8")
    order = json.loads(data)
    db = firestore.Client()
    db.collection("orders").document(order["id"]).set({
        "status": "processing", "items": order["items"], "total": order["total"],
    })

@functions_framework.cloud_event
def process_upload(cloud_event):
    """Triggered when a file is uploaded to Cloud Storage."""
    data = cloud_event.data
    bucket_name, file_name = data["bucket"], data["name"]
    if not file_name.lower().endswith((".png", ".jpg", ".jpeg")):
        return
    from google.cloud import vision
    client = vision.ImageAnnotatorClient()
    image = vision.Image(source=vision.ImageSource(
        gcs_image_uri=f"gs://{bucket_name}/{file_name}"))
    labels = [l.description for l in client.label_detection(image=image).label_annotations]
    print(f"Labels for {file_name}: {labels}")
# requirements.txt
functions-framework==3.*
google-cloud-firestore==2.*
google-cloud-storage==2.*
google-cloud-vision==3.*
flask>=2.0

Node.js Function Examples

javascript
// index.js
const functions = require("@google-cloud/functions-framework");

functions.http("helloNode", (req, res) => {
  const name = req.query.name || "World";
  res.json({ message: `Hello, ${name}!`, status: "ok" });
});

functions.cloudEvent("processMessage", (cloudEvent) => {
  const data = Buffer.from(cloudEvent.data.message.data, "base64").toString();
  console.log(`Processing: ${JSON.parse(data)}`);
});

Managing Deployed Functions

bash
gcloud functions list --gen2 --region=us-central1
gcloud functions describe hello-http --gen2 --region=us-central1
gcloud functions logs read hello-http --gen2 --region=us-central1 --limit=50
gcloud functions delete hello-http --gen2 --region=us-central1 --quiet

# Update env vars without redeploying code
gcloud functions deploy hello-http --gen2 --region=us-central1 \
  --update-env-vars=APP_ENV=staging

# Test locally before deploying
functions-framework --target=hello_http --port=8080

Terraform Configuration

hcl
resource "google_cloudfunctions2_function" "api" {
  name     = "hello-http"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "hello_http"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    min_instance_count    = 0
    max_instance_count    = 100
    available_memory      = "256Mi"
    timeout_seconds       = 60
    service_account_email = google_service_account.fn.email
    environment_variables = { APP_ENV = "production" }
  }
}

resource "google_cloud_run_service_iam_member" "invoker" {
  location = google_cloudfunctions2_function.api.location
  service  = google_cloudfunctions2_function.api.name
  role     = "roles/run.invoker"
  member   = "allUsers"
}

resource "google_cloudfunctions2_function" "processor" {
  name     = "process-order"
  location = "us-central1"

  build_config {
    runtime     = "python312"
    entry_point = "process_order"
    source {
      storage_source {
        bucket = google_storage_bucket.source.name
        object = google_storage_bucket_object.source.name
      }
    }
  }

  service_config {
    max_instance_count    = 50
    available_memory      = "512Mi"
    timeout_seconds       = 120
    service_account_email = google_service_account.fn.email
  }

  event_trigger {
    trigger_region = "us-central1"
    event_type     = "google.cloud.pubsub.topic.v1.messagePublished"
    pubsub_topic   = google_pubsub_topic.orders.id
    retry_policy   = "RETRY_POLICY_RETRY"
  }
}

Troubleshooting

SymptomCauseFix
PERMISSION_DENIED on deployMissing Cloud Build or Artifact Registry permsGrant roles/cloudbuild.builds.builder to Cloud Build SA
Function deploys but returns 403Missing roles/run.invoker for Gen2Add --allow-unauthenticated or grant invoker role
Cold start latency > 5sLarge dependencies or no min instancesSet --min-instances=1; reduce deps; use lazy imports
Pub/Sub messages redeliveredFunction errors or times outIncrease --timeout; fix error handling; add dead-letter topic
Build failed during deploySyntax error or missing dependencyCheck gcloud builds log; verify requirements.txt
Cannot connect to VPC resourceFunction not on VPC connectorAdd --vpc-connector=my-connector to deploy

Related Skills

  • gcp-networking - VPC connectors for accessing private resources from functions
  • gcp-cloud-sql - Connecting Cloud Functions to managed databases
  • terraform-gcp - Deploy Cloud Functions with Infrastructure as Code
  • gcp-gke - When workloads outgrow serverless and need Kubernetes

Frequently asked questions

What does the Gcp Cloud Functions AI skill do?

Deploy serverless functions on Google Cloud Functions. Configure triggers and manage deployments. Use when implementing serverless workloads on GCP.

Why use Gcp Cloud Functions on TypingMind?

Because you install it once and use it with any model. Gcp Cloud Functions 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 Gcp Cloud Functions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/infrastructure/cloud-gcp/gcp-cloud-functions. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gcp Cloud Functions?

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 Gcp Cloud Functions?

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

Is the Gcp Cloud Functions AI skill free?

Yes. It is published on GitHub by BagelHole under the MIT 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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