kibana-connectors▌
elastic/agent-skills · updated Apr 8, 2026
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Connectors store connection information for Elastic services and third-party systems. Alerting rules use connectors to
- ›route actions (notifications) when rule conditions are met. Connectors are managed per Kibana Space and can be
- ›shared across all rules within that space.
Kibana Connectors
Core Concepts
Connectors store connection information for Elastic services and third-party systems. Alerting rules use connectors to route actions (notifications) when rule conditions are met. Connectors are managed per Kibana Space and can be shared across all rules within that space.
Connector Categories
| Category | Connector Types |
|---|---|
| LLM Providers | OpenAI, Google Gemini, Amazon Bedrock, Elastic Managed LLMs, AI Connector, MCP (Preview, 9.3+) |
| Incident Management | PagerDuty, Opsgenie, ServiceNow (ITSM, SecOps, ITOM), Jira, Jira Service Management (9.2+), IBM Resilient, Swimlane, Torq, Tines, D3 Security, XSOAR (9.1+), TheHive |
| Endpoint Security | CrowdStrike, SentinelOne, Microsoft Defender for Endpoint |
| Messaging | Slack (API / Webhook), Microsoft Teams, Email |
| Logging & Observability | Server log, Index, Observability AI Assistant |
| Webhook | Webhook, Webhook - Case Management, xMatters |
| Elastic | Cases |
Authentication
All connector API calls require API key auth or Basic auth. Every mutating request must include the kbn-xsrf header.
kbn-xsrf: true
Required Privileges
Access to connectors is granted based on your privileges to alerting-enabled features. You need all privileges for
Actions and Connectors in Stack Management.
API Reference
Base path: <kibana_url>/api/actions (or /s/<space_id>/api/actions for non-default spaces).
| Operation | Method | Endpoint |
|---|---|---|
| Create connector | POST | /api/actions/connector/{id} |
| Update connector | PUT | /api/actions/connector/{id} |
| Get connector | GET | /api/actions/connector/{id} |
| Delete connector | DELETE | /api/actions/connector/{id} |
| Get all connectors | GET | /api/actions/connectors |
| Get connector types | GET | /api/actions/connector_types |
| Run connector | POST | /api/actions/connector/{id}/_execute |
Creating a Connector
Required Fields
| Field | Type | Description |
|---|---|---|
name |
string | Display name for the connector |
connector_type_id |
string | The connector type (e.g., .slack, .email, .webhook, .pagerduty, .jira) |
config |
object | Type-specific configuration (non-secret settings) |
secrets |
object | Type-specific secrets (API keys, passwords, tokens) |
Example: Create a Slack Connector (Webhook)
curl -X POST "https://my-kibana:5601/api/actions/connector/my-slack-connector" \
-H "kbn-xsrf: true" \
-H "Content-Type: application/json" \
-H "Authorization: ApiKey <your-api-key>" \
-d '{
"name": "Production Slack Alerts",
"connector_type_id": ".slack",
"config": {},
"secrets": {
"webhookUrl": "https://hooks.slack.com/services/T00/B00/XXXX"
}
}'
All connector types share the same request structure — only connector_type_id, config, and secrets differ. See the
Common Connector Type IDs table for available types and their required fields.
Example: Create a PagerDuty Connector
curl -X POST "https://my-kibana:5601/api/actions/connector/my-pagerduty" \
-H "kbn-xsrf: true" \
-H "Content-Type: application/json" \
-H "Authorization: ApiKey <your-api-key>" \
-d '{
"name": "PagerDuty Incidents",
"connector_type_id": ".pagerduty",
"config": {
"apiUrl": "https://events.pagerduty.com/v2/enqueue"
},
"secrets": {
"routingKey": "your-pagerduty-integration-key"
}
}'
Updating a Connector
PUT /api/actions/connector/{id} replaces the full configuration. connector_type_id is immutable — delete and
recreate to change it.
Listing and Discovering Connectors
# Get all connectors in the current space
curl -X GET "https://my-kibana:5601/api/actions/connectors" \
-H "Authorization: ApiKey <your-api-key>"
# Get available connector types
curl -X GET "https://my-kibana:5601/api/actions/connector_types" \
-H "Authorization: ApiKey <your-api-key>"
# Filter connector types by feature (e.g., only those supporting alerting)
curl -X GET "https://my-kibana:5601/api/actions/connector_types?feature_id=alerting" \
-H "Authorization: ApiKey <your-api-key>"
The GET /api/actions/connectors response includes referenced_by_count showing how many rules use each connector.
Always check this before deleting.
Running a Connector (Test)
Execute a connector action directly, useful for testing connectivity.
curl -X POST "https://my-kibana:5601/api/actions/connector/my-slack-connector/_execute" \
-H "kbn-xsrf: true" \
-H "Content-Type: application/json" \
-H "Authorization: ApiKey <your-api-key>" \
-d '{
"params": {
"message": "Test alert from API"
}
}'
Deleting a Connector
curl -X DELETE "https://my-kibana:5601/api/actions/connector/my-slack-connector" \
-H "kbn-xsrf: true" \
-H "Authorization: ApiKey <your-api-key>"
Warning: Deleting a connector that is referenced by rules will cause those rule actions to fail silently. Check
referenced_by_count first.
Terraform Provider
Use the elasticstack provider resource elasticstack_kibana_action_connector.
terraform {
required_providers {
elasticstack = {
source = "elastic/elasticstack"
}
}
}
provider "elasticstack" {
kibana {
endpoints = ["https://my-kibana:5601"]
api_key = var.kibana_api_key
}
}
resource "elasticstack_kibana_action_connector" "slack" {
name = "Production Slack Alerts"
connector_type_id = ".slack"
config = jsonencode({})
secrets = jsonencode({
webhookUrl = "https://hooks.slack.com/services/T00/B00/XXXX"
})
}
resource "elasticstack_kibana_action_connector" "index" {
name = "Alert Index Writer"
connector_type_id = ".index"
config = jsonencode({
index = "alert-history"
executionTimeField = "@timestamp"
})
secrets = jsonencode({})
}
Key Terraform notes:
configandsecretsmust be JSON-encoded strings viajsonencode()- Secrets are stored in Terraform state; use a remote backend with encryption and restrict state file access
- Import existing connectors:
terraform import elasticstack_kibana_action_connector.my_connector <space_id>/<connector_id>(usedefaultfor the default space) - After import, secrets are not populated in state; you must supply them in config
Preconfigured Connectors (On-Prem)
For self-managed Kibana, connectors can be preconfigured in kibana.yml so they are available at startup without manual
creation:
xpack.actions.preconfigured:
my-slack-connector:
name: "Production Slack"
actionTypeId: .slack
secrets:
webhookUrl: "https://hooks.slack.com/services/T00/B00/XXXX"
my-webhook:
name: "Custom Webhook"
actionTypeId: .webhook
config:
url: "https://api.example.com/alerts"
method: post
hasAuth: true
secrets:
user: "alert-user"
password: "secret-password"
Preconfigured connectors cannot be edited or deleted via the API or UI. They show is_preconfigured: true and omit
config and is_missing_secrets from API responses.
Networking Configuration
Customize connector networking (proxies, TLS, certificates) via kibana.yml:
# Global proxy for all connectors
xpack.actions.proxyUrl: "https://proxy.example.com:8443"
# Per-host TLS settings
xpack.actions.customHostSettings:
- url: "https://api.example.com"
ssl:
verificationMode: full
certificateAuthoritiesFiles: ["/path/to/ca.pem"]
Connectors in Kibana Workflows
Connectors serve as the integration layer across multiple Kibana workflows, not just alerting notifications:
| Workflow | Connector Types | Key Pattern |
|---|---|---|
| ITSM ticketing | ServiceNow, Jira, IBM Resilient | Create ticket on active, close on Recovered |
| On-call escalation | PagerDuty, Opsgenie | trigger on active, resolve on Recovered; always set a deduplication key |
| Case management | Cases (system action) | UI-only; groups alerts into investigation Cases; can auto-push to ITSM |
| Messaging / awareness | Slack, Teams, Email | onActionGroupChange for incident channels; summaries for monitoring channels |
| Audit logging | Index | onActiveAlert to write full alert time-series to Elasticsearch |
| AI workflows | OpenAI, Bedrock, Gemini, AI Connector | Powers Elastic AI Assistant and Attack Discovery; system-managed |
| Custom integrations | Webhook | Generic HTTP outbound with Mustache-templated JSON body |
For detailed patterns, examples, and decision guidance for each workflow, see workflows.md.
Best Practices
-
Use preconfigured connectors for production on-prem. They eliminate secret sprawl, survive Saved Object import
How to use kibana-connectors on Cursor
AI-first code editor with Composer
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 kibana-connectors
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches kibana-connectors from GitHub repository elastic/agent-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate kibana-connectors. Access the skill through slash commands (e.g., /kibana-connectors) 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.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★34 reviews- ★★★★★Soo Ndlovu· Dec 24, 2024
kibana-connectors is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kofi Sharma· Dec 24, 2024
Solid pick for teams standardizing on skills: kibana-connectors is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Dec 4, 2024
Keeps context tight: kibana-connectors is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Oshnikdeep· Nov 23, 2024
Registry listing for kibana-connectors matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Isabella Menon· Nov 15, 2024
kibana-connectors reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Henry Bansal· Nov 15, 2024
We added kibana-connectors from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Oct 14, 2024
kibana-connectors reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anaya Robinson· Oct 6, 2024
Registry listing for kibana-connectors matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Mia Abebe· Oct 6, 2024
kibana-connectors fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Nia White· Sep 25, 2024
Useful defaults in kibana-connectors — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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