Expert guidance for Google Cloud Platform services and cloud-native architecture.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongcp-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches gcp-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate gcp-expert. Access via /gcp-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Expert guidance for Google Cloud Platform services and cloud-native architecture.
# Initialize
gcloud init
# Create Compute Engine instance
gcloud compute instances create my-instance \
--zone=us-central1-a \
--machine-type=e2-medium \
--image-family=ubuntu-2004-lts \
--image-project=ubuntu-os-cloud
# Deploy App Engine
gcloud app deploy
# Create Cloud Storage bucket
gsutil mb gs://my-bucket-name/
# Upload file
gsutil cp myfile.txt gs://my-bucket-name/
import functions_framework
from google.cloud import firestore
@functions_framework.http
def hello_http(request):
request_json = request.get_json(silent=True)
name = request_json.get('name') if request_json else 'World'
return f'Hello {name}!'
@functions_framework.cloud_event
def hello_pubsub(cloud_event):
import base64
data = base64.b64decode(cloud_event.data["message"]["data"]).decode()
print(f'Received: {data}')
from google.cloud import bigquery
client = bigquery.Client()
# Query
query = """
SELECT name, COUNT(*) as count
FROM `project.dataset.table`
WHERE date >= '2024-01-01'
GROUP BY name
ORDER BY count DESC
LIMIT 10
"""
query_job = client.query(query)
results = query_job.result()
for row in results:
print(f"{row.name}: {row.count}")
# Load data
dataset_id = 'my_dataset'
table_id = 'my_table'
table_ref = client.dataset(dataset_id).table(table_id)
job_config = bigquery.LoadJobConfig(
source_format=bigquery.SourceFormat.CSV,
skip_leading_rows=1,
autodetect=True
)
with open('data.csv', 'rb') as source_file:
job = client.load_table_from_file(source_file, table_ref, job_config=job_config)
job.result()
from google.cloud import firestore
db = firestore.Client()
# Create document
doc_ref = db.collection('users').document('user1')
doc_ref.set({
'name': 'John Doe',
'email': '[email protected]',
'age': 30
})
# Query
users_ref = db.collection('users')
query = users_ref.where('age', '>=', 18).limit(10)
for doc in query.stream():
print(f'{doc.id} => {doc.to_dict()}')
# Real-time listener
def on_snapshot(doc_snapshot, changes, read_time):
for doc in doc_snapshot:
print(f'Received document: {doc.id}')
doc_ref.on_snapshot(on_snapshot)
from google.cloud import pubsub_v1
# Publisher
publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path('project-id', 'topic-name')
data = "Hello World".encode('utf-8')
future = publisher.publish(topic_path, data)
print(f'Published message ID: {future.result()}')
# Subscriber
subscriber = pubsub_v1.SubscriberClient()
subscription_path = subscriber.subscription_path('project-id', 'subscription-name')
def callback(message):
print(f'Received: {message.data.decode("utf-8")}')
message.ack()
streaming_pull_future = subscriber.subscribe(subscription_path, callback=callback)
❌ No IAM policies ❌ Storing credentials in code ❌ Ignoring costs ❌ Single region deployments ❌ No data backup ❌ Overly broad permissions
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
erichowens/some_claude_skills
wispbit-ai/skills
jeffallan/claude-skills
Useful defaults in gcp-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: gcp-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
gcp-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added gcp-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: gcp-expert is focused, and the summary matches what you get after install.
Keeps context tight: gcp-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
gcp-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
gcp-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in gcp-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for gcp-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
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