gcp-cloud-functions
Build serverless functions on Google Cloud Functions. Deploy HTTP and event-driven functions triggered by Pub/Sub, Cloud Storage, and Firestore. Configure runtime settings, manage dependencies, and connect to other GCP services.
What this skill does
# GCP Cloud Functions
Google Cloud Functions is a serverless execution environment for building event-driven applications. Write single-purpose functions that respond to HTTP requests, Pub/Sub messages, Cloud Storage events, or Firestore changes — no infrastructure to manage.
## Core Concepts
- **HTTP Function** — triggered by HTTP requests, returns a response
- **Event Function** — triggered by cloud events (Pub/Sub, Storage, Firestore)
- **Gen 2** — latest version, built on Cloud Run, longer timeouts, concurrency
- **Trigger** — the event source that invokes the function
- **Runtime** — language environment (Node.js, Python, Go, Java, etc.)
## HTTP Functions
```javascript
// index.js — HTTP function that processes webhook payloads
const functions = require('@google-cloud/functions-framework');
functions.http('handleWebhook', (req, res) => {
const { event, data } = req.body;
if (req.method !== 'POST') {
return res.status(405).send('Method not allowed');
}
console.log(`Received event: ${event}`, data);
switch (event) {
case 'order.created':
// Process new order
res.json({ status: 'processed', orderId: data.id });
break;
default:
res.json({ status: 'ignored', event });
}
});
```
```bash
# Deploy an HTTP function (Gen 2)
gcloud functions deploy handle-webhook \
--gen2 \
--runtime nodejs20 \
--region us-central1 \
--source . \
--entry-point handleWebhook \
--trigger-http \
--allow-unauthenticated \
--memory 256Mi \
--timeout 60 \
--set-env-vars "NODE_ENV=production"
```
## Pub/Sub Triggered Functions
```python
# main.py — process Pub/Sub messages
import base64
import json
import functions_framework
@functions_framework.cloud_event
def process_message(cloud_event):
"""Triggered by a Pub/Sub message."""
data = base64.b64decode(cloud_event.data["message"]["data"]).decode()
message = json.loads(data)
print(f"Processing order: {message['order_id']}")
# Process the order
result = fulfill_order(message)
print(f"Order {message['order_id']} fulfilled: {result}")
```
```bash
# Deploy Pub/Sub triggered function
gcloud functions deploy process-order \
--gen2 \
--runtime python312 \
--region us-central1 \
--source . \
--entry-point process_message \
--trigger-topic order-events \
--memory 512Mi \
--timeout 120 \
--set-secrets "DATABASE_URL=db-url:latest"
```
## Cloud Storage Triggered Functions
```python
# main.py — process uploaded files
import functions_framework
from google.cloud import storage, vision
@functions_framework.cloud_event
def process_upload(cloud_event):
"""Triggered when a file is uploaded to Cloud Storage."""
data = cloud_event.data
bucket_name = data["bucket"]
file_name = data["name"]
if not file_name.lower().endswith(('.jpg', '.png', '.jpeg')):
print(f"Skipping non-image file: {file_name}")
return
print(f"Processing image: gs://{bucket_name}/{file_name}")
# Generate thumbnail, run OCR, etc.
client = storage.Client()
bucket = client.bucket(bucket_name)
blob = bucket.blob(file_name)
image_data = blob.download_as_bytes()
# Process image...
print(f"Processed {file_name} ({len(image_data)} bytes)")
```
```bash
# Deploy Storage triggered function
gcloud functions deploy process-upload \
--gen2 \
--runtime python312 \
--region us-central1 \
--source . \
--entry-point process_upload \
--trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
--trigger-event-filters="bucket=my-uploads-bucket" \
--memory 1Gi \
--timeout 300
```
## Firestore Triggered Functions
```javascript
// index.js — react to Firestore document changes
const functions = require('@google-cloud/functions-framework');
const { Firestore } = require('@google-cloud/firestore');
functions.cloudEvent('onUserCreated', async (cloudEvent) => {
const data = cloudEvent.data;
const newValue = data.value.fields;
const email = newValue.email.stringValue;
const name = newValue.name.stringValue;
console.log(`New user created: ${name} (${email})`);
// Send welcome email, create default settings, etc.
const db = new Firestore();
await db.collection('user-settings').doc(data.value.name.split('/').pop()).set({
theme: 'light',
notifications: true,
createdAt: Firestore.FieldValue.serverTimestamp()
});
});
```
```bash
# Deploy Firestore triggered function
gcloud functions deploy on-user-created \
--gen2 \
--runtime nodejs20 \
--region us-central1 \
--source . \
--entry-point onUserCreated \
--trigger-event-filters="type=google.cloud.firestore.document.v1.created" \
--trigger-event-filters="database=(default)" \
--trigger-event-filters-path-pattern="document=users/{userId}" \
--memory 256Mi
```
## Managing Functions
```bash
# List deployed functions
gcloud functions list --gen2 --region us-central1
```
```bash
# View function details
gcloud functions describe process-order --gen2 --region us-central1
```
```bash
# View logs
gcloud functions logs read process-order --gen2 --region us-central1 --limit 50
```
```bash
# Delete a function
gcloud functions delete process-order --gen2 --region us-central1
```
## Local Development
```bash
# Run function locally
npx @google-cloud/functions-framework --target=handleWebhook --port=8080
```
```bash
# Test locally with curl
curl -X POST http://localhost:8080 \
-H "Content-Type: application/json" \
-d '{"event":"order.created","data":{"id":"12345"}}'
```
## Best Practices
- Use Gen 2 for all new functions (better performance, concurrency support)
- Set memory and timeout based on actual needs — don't over-provision
- Use Secret Manager for credentials, not environment variables
- Implement idempotent handlers — events may be delivered more than once
- Use structured logging for better observability in Cloud Logging
- Set min-instances to avoid cold starts for latency-sensitive functions
- Use the functions framework for local development and testing
- Keep functions focused — one function, one purpose
Related in Cloud & DevOps
appbuilder-action-scaffolder
IncludedCreate, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
orchestrating-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use observing-agentforce), standard CRM SOQL (use querying-soql), or Apex implementation (use generating-apex).
github-project-automation
IncludedAutomate GitHub repository setup with CI/CD workflows, issue templates, Dependabot, and CodeQL security scanning. Includes 12 production-tested workflows and prevents 18 errors: YAML syntax, action pinning, and configuration. Use when: setting up GitHub Actions CI/CD, creating issue/PR templates, enabling Dependabot or CodeQL scanning, deploying to Cloudflare Workers, implementing matrix testing, or troubleshooting YAML indentation, action version pinning, secrets syntax, runner versions, or CodeQL configuration. Keywords: github actions, github workflow, ci/cd, issue templates, pull request templates, dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
sf-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
fabric-cli
IncludedUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
lark
IncludedLark/Feishu CLI skills: lark-cli operations for docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, event. Use when the user needs to operate Lark/Feishu resources via lark-cli, send messages, manage documents, spreadsheets, calendars, tasks, OKRs, deploy web pages, or any Feishu/Lark workspace operations.