Manages existing Elastic Cloud Serverless projects: list, get, update, delete, reset credentials, resume, and load saved credentials. Connects to existing projects by resolving endpoints and acquiring scoped Elasticsearch API keys. Use when performing day-2 operations on serverless projects, connecting to an existing project, loading or resetting project credentials, or looking up project details.
Instrucciones de origen · Vista previa de solo lectura
name
cloud-manage-project
description
Manages existing Elastic Cloud Serverless projects: list, get, update, delete, reset credentials, resume, and load saved credentials. Connects to existing projects by resolving endpoints and acquiring scoped Elasticsearch API keys. Use when performing day-2 operations on serverless projects, connecting to an existing project, loading or resetting project credentials, or looking up project details.
compatibility
Requires Python 3.8+, network access to the Elastic Cloud API (api.elastic-cloud.com) for API commands; load-credentials works locally without network access. Environment variables: EC_API_KEY (required for API commands, set by cloud-setup).
metadata
{"author":"elastic","version":"0.1.0"}
Manage Serverless Project
Perform day-2 operations on Elastic Cloud Serverless projects using the Serverless REST API.
Prerequisites and permissions
Ensure EC_API_KEY is configured. If not, run cloud-setup skill first.
Updating project settings requires Admin or Editor role on the target project.
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 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.
cloud-setup
Variable
Required
Description
EC_API_KEY
Yes
Elastic Cloud API key used for project management 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, direct the user to generate one
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:
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 — even when using it to create an API key, pass it
directly via shell variable substitution without echoing.
Confirm before destructive actions. Always ask the user to confirm before deleting a project or resetting
credentials.
Credentials are saved to file. After a credential reset, the script writes the new password to
.elastic-credentials automatically. The password is redacted from stdout. Never read or display the contents of
.elastic-credentials in chat.
Admin credentials are for API key creation only. The admin password saved by create-project and
reset-credentials exists solely to bootstrap a scoped API key — never use it for direct Elasticsearch operations.
load-credentials excludes admin credentials by default; pass --include-admin only for key creation.
Always prefer API keys. Do not proceed with Elasticsearch operations until an ELASTICSEARCH_API_KEY is set. If
only admin credentials are available, create a scoped API key via elasticsearch-authn. If that skill is not
installed, ask the user to install it or create the key manually in Kibana > Stack Management > API keys.
Identify projects by type and ID. Every command requires both --type and --id (except list, which only needs
--type).
Two kinds of API keys. This skill uses the Cloud API key (EC_API_KEY) for project management operations
(list, get, update, delete). Elasticsearch operations require a separate Elasticsearch API key
(ELASTICSEARCH_API_KEY) that authenticates against the project's Elasticsearch endpoint. Do not confuse the two.
Workflow: Connect to an existing project
Use this workflow when the user asks to query or manage a project the agent did not create in the current session. It
resolves the project, saves its endpoints, and ensures working Elasticsearch credentials before proceeding.
This workflow only applies to Elastic Cloud Serverless projects. If the user's Elasticsearch instance is
self-managed or Elastic Cloud Hosted, this skill does not apply — skip it and proceed with the relevant skill directly.
If unsure, ask the user: "Is your Elasticsearch instance an Elastic Cloud Serverless project?"
Connect to Existing Project:
- [ ] Step 1: Resolve the project
- [ ] Step 2: Get project details and load credentials
- [ ] Step 3: Acquire Elasticsearch credentials
Step 1: Resolve the project
Ask the user for the project name if not already provided. Infer the project type from the user's request:
If the type is ambiguous, list all three types to find the project.
python3 skills/cloud/manage-project/scripts/manage-project.py list \
--type elasticsearch
Match the user's reference (name, partial name, or alias) against the list results. If multiple projects match or none
match, present the candidates and ask the user to pick.
Step 2: Get project details and load credentials
Once a single project is identified, check whether .elastic-credentials already has entries for this project (from a
previous session). If so, load them with load-credentials:
This sets all saved environment variables for the project — endpoints and any previously created Elasticsearch API keys
— in a single command. Admin credentials (ELASTICSEARCH_USERNAME/ELASTICSEARCH_PASSWORD) are intentionally excluded.
Later sections for the same project automatically overwrite earlier values, so the most recent credentials always win.
If load-credentials reports no matching entries, fetch the project details from the API and export endpoints manually:
python3 skills/cloud/manage-project/scripts/manage-project.py get \
--type elasticsearch \
--id <project-id>
Then export the endpoint URLs from the response. The available endpoints depend on the project type.
Confirm the response contains a valid username and "authentication_type": "api_key" before proceeding. If
verification succeeds, skip the rest of this step.
If no credentials were loaded, or verification fails, ask the user: "Do you have an existing Elasticsearch API key for
this project?"
If yes — have the user add it to .elastic-credentials (see "Credential file format"). Do not accept keys in chat.
Reload and verify:
Use the admin credentials to create a scoped Elasticsearch API key via elasticsearch-authn if available. If that
skill is not installed, ask the user to install it or create the key manually in Kibana > Stack Management > API
keys. Scope the key to only the privileges the user needs.
After creating the API key, save it to .elastic-credentials using the project-specific header format (see
"Credential file format" below). Then reload without --include-admin to drop admin credentials from the
environment and verify:
Parses .elastic-credentials, merges all sections for the matching project, and prints export statements. Admin
credentials (ELASTICSEARCH_USERNAME/ELASTICSEARCH_PASSWORD) are excluded by default — only endpoints and API keys
are exported. Add --include-admin when you need admin credentials to create an API key.
Workflow: List projects
python3 skills/cloud/manage-project/scripts/manage-project.py list \
--type elasticsearch
Use --type observability or --type security to list other project types.
Workflow: Get project details
python3 skills/cloud/manage-project/scripts/manage-project.py get \
--type elasticsearch \
--id <project-id>
Only the fields provided are updated (PATCH semantics). Supported fields: --name, --alias, --tag,
--search-power, --boost-window, --max-retention-days, --default-retention-days.
Alias
The alias is an RFC-1035 domain label (lowercase alphanumeric and hyphens, max 50 chars) that becomes part of the
project's endpoint URLs. Changing the alias changes all endpoint URLs, which breaks existing clients pointing to the
old URLs. Warn the user about this before applying.
Tags are key-value metadata pairs for team tracking, cost attribution, and organization. Pass --tag KEY:VALUE for each
tag. Multiple tags can be set in a single update.
Tags are sent as metadata.tags in the API request. Setting tags replaces all existing tags on the project — include
any existing tags the user wants to keep.
Elasticsearch search_lake settings
For Elasticsearch projects, two fields control query performance and data caching in the Search AI Lake. Ingested data
is stored in cost-efficient general storage. A cache layer on top provides faster search speed for recent and frequently
queried data — this cached data is considered search-ready.
Flag
Range
Description
--search-power
28–3000
Query performance level. Higher values improve performance but increase cost
--boost-window
1–180
Days of data eligible for boosted caching (default: 7)
Search Power
Search Power controls the speed of searches by provisioning more or fewer query resources. Common presets (matching the
Cloud UI):
Value
Preset
Behavior
28
On-demand
Autoscales with lower baseline. More variable latency, reduced max throughput
100
Performant
Consistently low latency, autoscales for moderately high throughput
250
High availability
Optimized for high-throughput scenarios, maintains low latency at high volumes
When the user asks for a preset by name, map it to the corresponding value. Custom values within 28–3000 are also valid.
Warn the user about cost implications before updating search_power. Higher values increase VCU consumption and may
result in higher bills. Confirm the new value with the user before applying.
Search Boost Window
Non-time-series data is always search-ready. The boost window determines how much time-series data (documents with a
@timestamp field) is also kept in the fast cache layer. Increasing the window means a larger portion of time-series
data becomes search-ready, which improves query speed for recent data but increases the search-ready data volume.
Security data retention settings
For security projects, two fields control how long data is retained in the Search AI Lake. Retention is configured per
data stream, but these project-level settings enforce global boundaries.
Flag
Unit
Description
--max-retention-days
days
Maximum retention period for any data stream in the project
--default-retention-days
days
Default retention applied to data streams without a custom one
Maximum retention — enforces an upper bound across all data streams. When lowered, it replaces the retention for
any stream that currently has a longer period. Data older than the new maximum is permanently deleted.
Default retention — automatically applied to data streams that do not have a custom retention period set. Does not
affect streams with an existing custom retention.
Warn the user before reducing max-retention-days. Lowering the maximum permanently deletes data older than the new
limit. Confirm the new value with the user before applying.
Connect to an existing project (credentials on file)
User: I want to run a query against my search project "prod-search"
Agent: Let me find that project.
[runs: list --type elasticsearch]
I found "prod-search" (ID: abc123...). Loading saved credentials...
[runs: eval $(load-credentials --name prod-search)]
Credentials loaded. Verifying...
[runs: curl -H "Authorization: ApiKey ..." "${ELASTICSEARCH_URL}/_security/_authenticate"]
Verified — authenticated as user "my-service" via API key. You're all set.
Connect to a project (no saved credentials)
User: let's ingest some logs into my o11y project "siem-prod"
Agent: Let me find that project.
[runs: list --type observability]
Found "siem-prod" (ID: def456...). No saved credentials found for this project.
[runs: get --type observability --id def456...]
Endpoints set. Since there are no Elasticsearch credentials on file, I can reset the
admin bootstrap password to get initial access. Shall I proceed?
User: yes
Agent: [runs: reset-credentials --type observability --id def456...]
Credentials saved to .elastic-credentials — open that file to retrieve the password.
[runs: eval $(load-credentials --name siem-prod --include-admin)]
Admin credentials loaded for API key creation. Creating a scoped Elasticsearch API key
now — what access does it need?
User: read-only on logs-*
Agent: [creates scoped API key via elasticsearch-authn using admin credentials]
[appends API key to .elastic-credentials with project header]
[runs: eval $(load-credentials --name siem-prod)]
[verifies against ELASTICSEARCH_URL]
API key created and verified. Admin credentials removed from environment.
Delete a project after confirmation
User: delete my elasticsearch project abc123...
Agent: Are you sure you want to delete project abc123...? This cannot be undone.
User: yes
