Create Serverless Project

Create Elastic Cloud Serverless projects using the Serverless REST API. Use the cloud-manage-project skill for day-2 operations like listing, updating, or deleting projects.

Prerequisites and permissions

• Ensure EC_API_KEY is configured. If not, run cloud-setup skill first.
• Creating projects requires a Cloud API key with Admin or Organization owner role.
• This skill does not perform a separate role pre-check. Attempt the requested operation and let the API enforce authorization. If the API returns an authorization error (for example, 403 Forbidden), stop and ask the user to verify the provided API key permissions.

Manual setup fallback (when cloud-setup is unavailable)

If this skill is installed standalone and cloud-setup is not available, instruct the user to configure Cloud environment variables manually before running commands. Never ask the user to paste API keys in chat.

Variable	Required	Description
EC_API_KEY	Yes	Elastic Cloud API key used for project creation operations.
EC_BASE_URL	No	Cloud API base URL (default: https://api.elastic-cloud.com).

Note: If EC_API_KEY is missing, or the user does not have a Cloud API key yet, first check whether the user has an Elastic Cloud account. If not, propose starting a free trial at Elastic Cloud free trial — 14 days of full access with no credit card required. Once registered, direct the user to generate a key at Elastic Cloud API keys, then configure it locally using the steps below.

Preferred method (agent-friendly): create a .env file in the project root:

EC_API_KEY=your-api-key
EC_BASE_URL=https://api.elastic-cloud.com

All cloud/* scripts auto-load .env from the working directory.

Alternative: export directly in the terminal:

export EC_API_KEY="<your-cloud-api-key>"
export EC_BASE_URL="https://api.elastic-cloud.com"

Terminal exports may not be visible to sandboxed agents running in separate shell sessions, so prefer .env when using an agent.

Critical principles

• Never display secrets in chat. Do not echo, log, or repeat API keys, passwords, or credentials in conversation messages or agent thinking. Direct the user to the .elastic-credentials file instead. The admin password must never appear in chat history, thinking traces, or agent output.
• Confirm before creating. Always present the project configuration to the user and ask for confirmation before running the creation script.
• Admin credentials are for API key creation only. The script saves the admin password to .elastic-credentials for bootstrapping a scoped API key. The admin user has full privileges and cannot be modified in serverless. Never use admin credentials for direct Elasticsearch operations (querying, indexing, etc.) — always create a scoped API key first (see Step 8). The load-credentials command excludes admin credentials by default — use --include-admin only during Step 7/8, then reload without it once the API key is created. Never read or display the contents of .elastic-credentials in chat.
• Recover lost credentials. If the script fails to write .elastic-credentials (disk full, permissions, etc.), the save may be incomplete. Check .elastic-credentials for the password first. If missing, use the cloud-manage-project skill's reset-credentials command to generate a new password.
• Region is permanent. A project's region cannot be changed after creation.
• Prefer automatic readiness checks. Pass --wait to the creation script so it polls until the phase changes from initializing to initialized. Only fall back to manually polling the status endpoint if --wait is unavailable.

Project types

Type	Description	Key endpoints
elasticsearch	Search, analytics, and vector workloads	Elasticsearch, Kibana
observability	Logs, metrics, traces, and APM	Elasticsearch, Kibana, APM, OTLP
security	SIEM, endpoint protection, cloud security	Elasticsearch, Kibana, OTLP

Project type inference

Map the user's request to the correct --type value:

User says	--type
"search project", "elasticsearch project", vector search	elasticsearch
"observability project", "o11y", logs, metrics, traces, APM	observability
"security project", "SIEM", detections, endpoint protection	security

Do not silently default to any type. If the user does not specify a type, infer it from the conversation context (for example, discussing log ingestion suggests observability, discussing detections or SIEM suggests security, discussing search or vector workloads suggests elasticsearch). Always present the inferred type to the user and ask for confirmation before proceeding. If context is insufficient to infer a type, ask the user to choose.

Product tiers

Observability and security projects support a --product-tier flag. Default to complete unless the user explicitly requests a different tier.

Project type	Tier	Description
observability	complete	Full observability suite (logs, metrics, traces, APM)
observability	logs_essentials	Log management only
security	complete	Full security suite (SIEM, cloud, endpoint)
security	essentials	Core SIEM only

Elasticsearch projects do not have a product tier — use --optimized-for instead.

Sensible defaults

Present these defaults to the user before creation. Ask if they want to use or change them:

Setting	Default
Region	gcp-us-central1

Project type must be confirmed with the user — do not assume a default. See "Project type inference" above.

Always use --optimized-for general_purpose unless the user explicitly requests vector. Do not proactively offer the vector option.

If the user does not specify a name, ask for one — it is required.

Workflow: Create a project

Project Creation:
- [ ] Step 1: Verify API key is set
- [ ] Step 2: Present defaults and confirm with user
- [ ] Step 3: List available regions (optional)
- [ ] Step 4: Create the project
- [ ] Step 5: Save credentials and endpoints
- [ ] Step 6: Wait for project to initialize
- [ ] Step 7: Set environment variables
- [ ] Step 8: Recommend creating a scoped API key

Step 1: Verify API key is set

echo "${EC_API_KEY:?Not set}"

If EC_API_KEY is not set, run the cloud-setup skill first to configure authentication and defaults.

Step 2: Present summary and confirm with user

Before presenting the summary, ensure the project type has been explicitly confirmed by the user. If no type was specified, infer one from the conversation context and propose it. If the context is ambiguous, ask the user to choose from elasticsearch, observability, or security.

Always show a confirmation summary before creating. Include different fields depending on project type:

Elasticsearch project:

Project Summary:
  Type:          elasticsearch
  Name:          my-project
  Region:        gcp-us-central1

Observability project:

Project Summary:
  Type:          observability
  Name:          my-project
  Region:        gcp-us-central1
  Product tier:  complete

Security project:

Project Summary:
  Type:          security
  Name:          my-project
  Region:        gcp-us-central1
  Product tier:  complete

Ask the user to confirm or override any values before proceeding.

Step 3: List available regions (optional)

python3 skills/cloud/create-project/scripts/create-project.py list-regions

The output is grouped by cloud provider (AWS, Azure, GCP) and sorted alphabetically. Regions marked with * do not support project creation.

Step 4: Create the project

python3 skills/cloud/create-project/scripts/create-project.py create \
  --type elasticsearch \
  --name "my-project" \
  --region gcp-us-central1 \
  --optimized-for general_purpose \
  --wait

Always pass --optimized-for general_purpose for Elasticsearch projects. Only use vector if the user explicitly requests it.

For observability and security projects, pass --product-tier complete unless the user explicitly requests a different tier.

Always pass --wait so the script automatically polls until the project is ready.

Step 5: Save credentials and endpoints

The script automatically writes credentials to .elastic-credentials in the working directory. The password is redacted from the JSON output on stdout.

If saving succeeds, tell the user:

Credentials saved to .elastic-credentials — open that file to retrieve your password.

Do not read, cat, or display the contents of .elastic-credentials in chat.

If saving fails, the script prints an error to stderr. Check whether .elastic-credentials exists and contains a password (a partial write is possible). If the password is missing or the file does not exist, immediately run the cloud-manage-project skill's reset-credentials command to generate a new password.

The creation response also contains:

• Project ID — needed for all subsequent operations
• Cloud ID — for client libraries
• Elasticsearch and Kibana endpoints — safe to display in chat

The admin credentials are for initial bootstrap only. Recommend creating a scoped API key for ongoing access (Step 8).

Step 6: Wait for project to initialize

When --wait is passed (recommended), the script polls automatically until the project phase becomes initialized. No manual polling is needed.

If the agent ran without --wait, poll manually:

python3 skills/cloud/create-project/scripts/create-project.py status \
  --type elasticsearch \
  --id <project-id>

Repeat until phase changes from initializing to initialized.

Step 7: Set environment variables

The creation script saves credentials and endpoints to .elastic-credentials with the project name in the header. Load them into the current shell with --include-admin so admin credentials are available for API key creation in Step 8:

eval $(python3 skills/cloud/manage-project/scripts/manage-project.py load-credentials \
  --name "<project-name>" --include-admin)

This sets ELASTICSEARCH_URL, KIBANA_URL, any project-type specific endpoints (APM_URL, INGEST_URL), and the admin ELASTICSEARCH_USERNAME/ELASTICSEARCH_PASSWORD needed to bootstrap an API key.

Step 8: Create a scoped API key

The admin user has full privileges and cannot be modified in serverless projects. Do not proceed with Elasticsearch operations using admin credentials. Create a scoped Elasticsearch API key with only the permissions the user needs.

If the elasticsearch-authn skill is available, use it for API key creation — it covers the full lifecycle (create, grant, invalidate, query) and handles scoping privileges correctly. If the skill is not installed, ask the user to either install it or create the API key manually through Kibana > Stack Management > API keys. After creation, save the API key to .elastic-credentials using the project-specific header format (see manage-project skill's "Credential file format" section), then reload without --include-admin to drop admin credentials from the environment:

eval $(python3 skills/cloud/manage-project/scripts/manage-project.py load-credentials \
  --name "<project-name>")

Examples

Create an Elasticsearch project with defaults

python3 skills/cloud/create-project/scripts/create-project.py create \
  --type elasticsearch \
  --name "my-search-project" \
  --region gcp-us-central1 \
  --optimized-for general_purpose \
  --wait

Create an observability project

python3 skills/cloud/create-project/scripts/create-project.py create \
  --type observability \
  --name "prod-o11y" \
  --region aws-eu-west-1 \
  --product-tier complete \
  --wait

Create a security project

python3 skills/cloud/create-project/scripts/create-project.py create \
  --type security \
  --name "siem-prod" \
  --region gcp-us-central1 \
  --product-tier complete \
  --wait

Guidelines

• Run the cloud-setup skill first if EC_API_KEY is not set.
• Always confirm the project configuration with the user before creating.
• Never display passwords or API keys in chat. Direct the user to .elastic-credentials.
• Never silently default to a project type. Infer from context and confirm with the user.
• Default to general_purpose optimization. Only use vector if the user explicitly requests it.
• Default to complete product tier for observability and security projects. Only use logs_essentials or essentials if the user explicitly requests it.
• Always pass --wait so the script polls until the project is ready.
• If credential saving fails, immediately reset credentials using the cloud-manage-project skill.
• After creation, recommend creating a scoped API key instead of relying on admin credentials.
• Region cannot be changed after creation — confirm the choice before proceeding.

Script reference

Command	Description
create	Create a new serverless project
status	Get project initialization status
list-regions	List available regions

Flag	Commands	Description
--type	create, status	Project type: elasticsearch, observability, security
--name	create	Project name (required)
--region	create	Region ID (default: gcp-us-central1)
--id	status	Project ID
--optimized-for	create	Elasticsearch subtype: general_purpose or vector
--product-tier	create	Observability/security tier (see "Product tiers" section)
--wait	create	Poll until project is initialized before exiting

Environment variables

Variable	Required	Description
EC_API_KEY	Yes	Elastic Cloud API key
EC_BASE_URL	No	Cloud API base URL (default: https://api.elastic-cloud.com)
ELASTIC how to use cloud-create-project CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity How to use cloud-create-project on Cursor AI-first code editor with Composer 1 Prerequisites Before installing skills in Cursor, ensure your development environment meets these requirements: ›Cursor installed and configured on your development machine ›Node.js version 16.0+ with npm package manager (verify with node --version) ›Active project directory or workspace where you want to add cloud-create-project 2 Execute installation command Execute the skills CLI command in your project's root directory to begin installation: $npx skills add https://github.com/elastic/agent-skills --skill cloud-create-projectcopy The skills CLI fetches cloud-create-project from GitHub repository elastic/agent-skills and configures it for Cursor. 3 Select Cursor when prompted The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor: ◆ Which agents do you want to install to? │ │ ── Universal (.agents/skills) ── always included ──── │ • Amp │ • Antigravity │ • Cline │ • Codex │ ●Cursor(selected) │ • Cursor │ • Windsurf 4 Verify installation Confirm successful installation by checking the skill directory location: .cursor/skills/cloud-create-project Reload or restart Cursor to activate cloud-create-project. Access the skill through slash commands (e.g., /cloud-create-project) or your agent's skill management interface. ⚠ Security & Verification Notice 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment. Additional Resources ›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills? List & Monetize Your Skill Submit your Claude Code skill and start earning GET_STARTED → Use Cases▌ Task Automation & Efficiency Automate repetitive workflows and reduce manual effort Example Generate reports, summarize documents, draft communications ✓ Save 3-5 hours per week on routine tasks Knowledge Enhancement Learn new skills, understand complex topics, get expert guidance Example Explain concepts, provide examples, suggest learning resources ✓ Accelerate learning and skill development by 2x Quality Improvement Enhance output quality through reviews, suggestions, and refinements Example Review drafts, suggest improvements, catch errors ✓ Improve work quality by 30-40% with less effort Implementation Guide▌ Prerequisites ›Claude Desktop or compatible AI client with skill support ›Clear understanding of task or problem to solve ›Willingness to iterate and refine outputs Time Estimate 15-45 minutes depending on use case complexity Installation Steps 1.Install skill using provided installation command 2.Test with simple use case relevant to your work 3.Evaluate output quality and relevance 4.Iterate on prompts to improve results 5.Integrate into regular workflow if valuable Common Pitfalls ⚠Expecting perfect results without iteration ⚠Not providing enough context in prompts ⚠Using skill for tasks outside its intended scope ⚠Accepting outputs without review and validation Best Practices▌ ✓ Do +Start with clear, specific prompts +Provide relevant context and constraints +Review and refine all outputs before using +Iterate to improve output quality +Document successful prompt patterns ✗ Don't −Don't use without understanding skill limitations −Don't skip validation of outputs −Don't share sensitive information in prompts −Don't expect skill to replace human judgment 💡 Pro Tips ★Be specific about desired format and style ★Ask for multiple options to choose from ★Request explanations to understand reasoning ★Combine AI efficiency with human expertise When to Use This▌ ✓ 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. Learning Path▌ 1Familiarize yourself with skill capabilities and limitations 2Start with low-risk, non-critical tasks 3Progress to more complex and valuable use cases 4Build expertise through regular use and experimentation Discussion Product Hunt–style comments (not star reviews) post comment No comments yet — start the thread. general reviews Ratings 4.7★★★★★47 reviews ★★★★★Olivia Sethi· Dec 20, 2024 Useful defaults in cloud-create-project — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows. ★★★★★Shikha Mishra· Dec 4, 2024 cloud-create-project reduced setup friction for our internal harness; good balance of opinion and flexibility. ★★★★★Layla Ramirez· Dec 4, 2024 Registry listing for cloud-create-project matched our evaluation — installs cleanly and behaves as described in the markdown. ★★★★★Sakura Kim· Dec 4, 2024 I recommend cloud-create-project for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area. ★★★★★Yash Thakker· Nov 23, 2024 I recommend cloud-create-project for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area. ★★★★★Layla Menon· Nov 23, 2024 Useful defaults in cloud-create-project — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows. ★★★★★Sakura Mensah· Nov 23, 2024 cloud-create-project reduced setup friction for our internal harness; good balance of opinion and flexibility. ★★★★★Emma Gupta· Nov 11, 2024 cloud-create-project is among the better-maintained entries we tried; worth keeping pinned for repeat workflows. ★★★★★Sakshi Patil· Nov 3, 2024 cloud-create-project is among the better-maintained entries we tried; worth keeping pinned for repeat workflows. ★★★★★Chaitanya Patil· Oct 22, 2024 Keeps context tight: cloud-create-project is the kind of skill you can hand to a new teammate without a long onboarding doc. showing 1-10 of 47 prev1 / 5next bookmarkshare