cloud_scheduler_setup

Name: cloud_scheduler_setup
Author: VeerMuchandi

15stars1forks

How to install cloud_scheduler_setup?

veermuchandi/rad-skills/cloud_scheduler_setup

$npx -y skills add VeerMuchandi/rad-skills --skill cloud_scheduler_setup

Installs into the current project.

Summary

Provisions a scheduled Cloud Scheduler job with secure OIDC token authentication to invoke HTTP targets (such as Reasoning Engine endpoints) using Terraform.

Files · 1

View on GitHub

SKILL.md

1	# Cloud Scheduler Setup Skill
2
3	This skill allows you to easily provision a scheduled cron job using Cloud Scheduler on Google Cloud Platform via Terraform. It automatically enables required APIs, configures a secure HTTP target call, and hooks up secure OIDC token authentication using a specified service account to allow triggering private endpoints (like Vertex AI Reasoning Engines).
4
5	## Prerequisites
6	- Terraform Version: `v1.5+` (Recommended `v1.14+`)
7	- GCP Project: Active Google Cloud Project with billing enabled.
8	- Permissions:
9	- `roles/cloudscheduler.admin` (to manage Cloud Scheduler)
10	- `roles/serviceusage.serviceUsageConsumer` (to enable services)
11	- The target Service Account must have `roles/iam.serviceAccountUser` assigned or be callable by the scheduler.
12
13	## Skill Directory Structure
14	```text
15	cloud_scheduler_setup/
16	├── SKILL.md # This instructions file
17	├── resources/
18	│ └── templates/
19	│ ├── main.tf # Core Cloud Scheduler resources
20	│ ├── variables.tf # Customizable variables
21	│ └── outputs.tf # Job name and state outputs
22	└── scripts/
23	└── deploy_scheduler.py # Helper Python script to orchestrate deployment
24	```
25
26	## 1. How to Use this Skill (Step-by-Step)
27
28	### Step 1: Initialize the Deployment Workspace
29	Create a dedicated deployment directory outside your agent code package to prevent Terraform state/binaries from being packaged with the agent.
30
31	```bash
32	mkdir -p deploy_scheduler
33	```
34
35	### Step 2: Copy the Templates
36	Copy the templates from this skill to your new `deploy_scheduler` folder.
37	(Note: Replace `<path_to_skill>` with the path of this skill folder, e.g. `skills/cloud_scheduler_setup`)
38
39	```bash
40	cp -r <path_to_skill>/resources/templates/* deploy_scheduler/
41	```
42
43	### Step 3: Configure Deployment (`deploy_scheduler/terraform.tfvars`)
44	Create a file named `deploy_scheduler/terraform.tfvars` and set the following parameters:
45
46	```hcl
47	project_id = "your-gcp-project-id"
48	region = "us-central1"
49	job_name = "my-daily-agent-trigger"
50	schedule = "0 9 * * *" # Daily at 9:00 AM
51	time_zone = "America/New_York"
52	target_uri = "https://us-central1-aiplatform.googleapis.com/v1/projects/your-gcp-project-id/locations/us-central1/reasoningEngines/your-engine-id:streamQuery"
53	http_method = "POST"
54	body = "{\"class_method\": \"stream_query\", \"input\": {\"message\": \"Process daily sales data\"}}"
55	service_account_email = "your-agent-service-account@your-gcp-project-id.iam.gserviceaccount.com"
56	```
57
58	### Step 4: Run Terraform
59	Navigate to the `deploy_scheduler` directory, initialize, and deploy the resources:
60
61	```bash
62	cd deploy_scheduler
63	terraform init
64	terraform apply -auto-approve
65	```
66
67	### Step 5: Capture Output
68	Upon successful completion, Terraform outputs the created job ID and state:
69	```hcl
70	job_id = "projects/your-project/locations/us-central1/jobs/my-daily-agent-trigger"
71	job_state = "ENABLED"
72	```
73
74	---
75
76	### Option B: Using the Orchestration Helper Script (Recommended)
77	You can automate the deployment in a single step using the helper Python script.
78
79	Run the script:
80	```bash
81	python3 <path_to_skill>/scripts/deploy_scheduler.py \
82	--project "your-gcp-project-id" \
83	--job-name "my-daily-agent-trigger" \
84	--schedule "0 9 * * *" \
85	--target-uri "https://us-central1-aiplatform.googleapis.com/v1/projects/your-project/locations/us-central1/reasoningEngines/your-engine-id:streamQuery" \
86	--service-account "your-service-account@your-project.iam.gserviceaccount.com" \
87	--body "{\"class_method\": \"stream_query\", \"input\": {\"message\": \"Process daily sales data\"}}" \
88	--work-dir "deploy_scheduler"
89	```
90
91	To destroy the job later:
92	```bash
93	python3 <path_to_skill>/scripts/deploy_scheduler.py \
94	--project "your-gcp-project-id" \
95	--job-name "my-daily-agent-trigger" \
96	--schedule "0 9 * * *" \
97	--target-uri "https://us-central1-aiplatform.googleapis.com/v1/projects/your-project/locations/us-central1/reasoningEngines/your-engine-id:streamQuery" \
98	--service-account "your-service-account@your-project.iam.gserviceaccount.com" \
99	--work-dir "deploy_scheduler" \
100	--destroy
101	```
102
103	---
104
105	## 2. Best Practices & Security
106	- OIDC Token Auth: Always specify a service account with the minimal roles required to call the target endpoint (e.g. `roles/aiplatform.user` for Vertex AI Agent Engine).
107	- Deadlines: The default deadline is set to `320s` to accommodate longer-running Reasoning Engine tasks (e.g. processing large GCS datasets or running database migrations).

People who liked this also viewed

6 picks

Type

microsoft-foundryDeploy, evaluate, fine-tune, and manage Foundry agents end-to-end with azd: hosted agent scaffold/run/deploy, prompt agent create, batch eval, continuous eval, prompt optimizer, Agent Optimizer scaffoSkillsJul 2026476k1.3k
azure-deployExecute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application SkillsJul 2026472k1.3k
azure-preparePrepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wSkillsJul 2026471k1.3k
azure-validate--- name: azure-validate description: "Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity pSkillsJul 2026471k1.3k
azure-aigatewayConfigure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbSkillsJul 2026470k1.3k
azure-hosted-copilot-sdkOfficial agent plugin providing skills and MCP server configurations for Azure scenarios.SkillsJul 2026424k1.3k

cloud_scheduler_setup

15stars1forks

View on GitHub

How to install cloud_scheduler_setup?

veermuchandi/rad-skills/cloud_scheduler_setup

$npx -y skills add VeerMuchandi/rad-skills --skill cloud_scheduler_setup

Installs into the current project.

Summary

Provisions a scheduled Cloud Scheduler job with secure OIDC token authentication to invoke HTTP targets (such as Reasoning Engine endpoints) using Terraform.

Files · 1

View on GitHub

SKILL.md

1	# Cloud Scheduler Setup Skill
2
3	This skill allows you to easily provision a scheduled cron job using Cloud Scheduler on Google Cloud Platform via Terraform. It automatically enables required APIs, configures a secure HTTP target call, and hooks up secure OIDC token authentication using a specified service account to allow triggering private endpoints (like Vertex AI Reasoning Engines).
4
5	## Prerequisites
6	- Terraform Version: `v1.5+` (Recommended `v1.14+`)
7	- GCP Project: Active Google Cloud Project with billing enabled.
8	- Permissions:
9	- `roles/cloudscheduler.admin` (to manage Cloud Scheduler)
10	- `roles/serviceusage.serviceUsageConsumer` (to enable services)
11	- The target Service Account must have `roles/iam.serviceAccountUser` assigned or be callable by the scheduler.
12
13	## Skill Directory Structure
14	```text
15	cloud_scheduler_setup/
16	├── SKILL.md # This instructions file
17	├── resources/
18	│ └── templates/
19	│ ├── main.tf # Core Cloud Scheduler resources
20	│ ├── variables.tf # Customizable variables
21	│ └── outputs.tf # Job name and state outputs
22	└── scripts/
23	└── deploy_scheduler.py # Helper Python script to orchestrate deployment
24	```
25
26	## 1. How to Use this Skill (Step-by-Step)
27
28	### Step 1: Initialize the Deployment Workspace
29	Create a dedicated deployment directory outside your agent code package to prevent Terraform state/binaries from being packaged with the agent.
30
31	```bash
32	mkdir -p deploy_scheduler
33	```
34
35	### Step 2: Copy the Templates
36	Copy the templates from this skill to your new `deploy_scheduler` folder.
37	(Note: Replace `<path_to_skill>` with the path of this skill folder, e.g. `skills/cloud_scheduler_setup`)
38
39	```bash
40	cp -r <path_to_skill>/resources/templates/* deploy_scheduler/
41	```
42
43	### Step 3: Configure Deployment (`deploy_scheduler/terraform.tfvars`)
44	Create a file named `deploy_scheduler/terraform.tfvars` and set the following parameters:
45
46	```hcl
47	project_id = "your-gcp-project-id"
48	region = "us-central1"
49	job_name = "my-daily-agent-trigger"
50	schedule = "0 9 * * *" # Daily at 9:00 AM
51	time_zone = "America/New_York"
52	target_uri = "https://us-central1-aiplatform.googleapis.com/v1/projects/your-gcp-project-id/locations/us-central1/reasoningEngines/your-engine-id:streamQuery"
53	http_method = "POST"
54	body = "{\"class_method\": \"stream_query\", \"input\": {\"message\": \"Process daily sales data\"}}"
55	service_account_email = "your-agent-service-account@your-gcp-project-id.iam.gserviceaccount.com"
56	```
57
58	### Step 4: Run Terraform
59	Navigate to the `deploy_scheduler` directory, initialize, and deploy the resources:
60
61	```bash
62	cd deploy_scheduler
63	terraform init
64	terraform apply -auto-approve
65	```
66
67	### Step 5: Capture Output
68	Upon successful completion, Terraform outputs the created job ID and state:
69	```hcl
70	job_id = "projects/your-project/locations/us-central1/jobs/my-daily-agent-trigger"
71	job_state = "ENABLED"
72	```
73
74	---
75
76	### Option B: Using the Orchestration Helper Script (Recommended)
77	You can automate the deployment in a single step using the helper Python script.
78
79	Run the script:
80	```bash
81	python3 <path_to_skill>/scripts/deploy_scheduler.py \
82	--project "your-gcp-project-id" \
83	--job-name "my-daily-agent-trigger" \
84	--schedule "0 9 * * *" \
85	--target-uri "https://us-central1-aiplatform.googleapis.com/v1/projects/your-project/locations/us-central1/reasoningEngines/your-engine-id:streamQuery" \
86	--service-account "your-service-account@your-project.iam.gserviceaccount.com" \
87	--body "{\"class_method\": \"stream_query\", \"input\": {\"message\": \"Process daily sales data\"}}" \
88	--work-dir "deploy_scheduler"
89	```
90
91	To destroy the job later:
92	```bash
93	python3 <path_to_skill>/scripts/deploy_scheduler.py \
94	--project "your-gcp-project-id" \
95	--job-name "my-daily-agent-trigger" \
96	--schedule "0 9 * * *" \
97	--target-uri "https://us-central1-aiplatform.googleapis.com/v1/projects/your-project/locations/us-central1/reasoningEngines/your-engine-id:streamQuery" \
98	--service-account "your-service-account@your-project.iam.gserviceaccount.com" \
99	--work-dir "deploy_scheduler" \
100	--destroy
101	```
102
103	---
104
105	## 2. Best Practices & Security
106	- OIDC Token Auth: Always specify a service account with the minimal roles required to call the target endpoint (e.g. `roles/aiplatform.user` for Vertex AI Agent Engine).
107	- Deadlines: The default deadline is set to `320s` to accommodate longer-running Reasoning Engine tasks (e.g. processing large GCS datasets or running database migrations).

People who liked this also viewed

6 picks

Type