Observability Manage Slos Agent Skill
> Create and manage SLOs in Elastic Observability using the Kibana API. Use when defining SLIs, setting error budgets, or managing SLO lifecycle.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/elastic/agent-skills --skill observability-manage-slosThe instruction itself
8 sections, as written by the authorService-Level Objectives (SLOs)
Create and manage SLOs in Elastic Observability. SLOs track service performance against measurable targets using
service-level indicators (SLIs) computed from Elasticsearch data.
Authentication
SLO operations go through the Kibana API. Authenticate with either an API key or basic auth:
# API key
curl -H "Authorization: ApiKey <base64-encoded-key>" -H "kbn-xsrf: true" <KIBANA_URL>/api/observability/slos
# Basic auth
curl -u "$KIBANA_USER:$KIBANA_PASSWORD" -H "kbn-xsrf: true" <KIBANA_URL>/api/observability/slos
For non-default spaces, prefix the path: /s/<space_id>/api/observability/slos.
Include kbn-xsrf: true on all POST, PUT, and DELETE requests.
SLI Types
| Type | API value | Use case |
| ----------------------- | ------------------------------ | ------------------------------------------- |
| Custom KQL | sli.kql.custom | Raw logs — good/total using KQL queries |
| Custom metric | sli.metric.custom | Metric fields — equations with aggregations |
| Timeslice metric | sli.metric.timeslice | Metric fields — per-slice threshold check |
| Histogram metric | sli.histogram.custom | Histogram fields — range/value_count |
| APM latency | sli.apm.transactionDuration | APM — latency threshold |
| APM availability | sli.apm.transactionErrorRate | APM — success rate |
| Synthetics availability | sli.synthetics.availability | Synthetics monitors — uptime percentage |
Guidelines
objective.targetis a decimal between 0 and 1 (for example0.995for 99.5%).- Timeslice metric indicators require
budgetingMethod: "timeslices". - Updating an SLO resets the underlying transform — historical data is recomputed.
- The cluster needs nodes with both
transformandingestroles. - Use
POST .../slos/{id}/_resetwhen an SLO is stuck or after index mapping changes. - Group-by SLOs create one instance per unique value — avoid high-cardinality fields.
- Synthetics SLOs are auto-grouped by monitor and location; do not set
groupBymanually. - Burn rate alert rules are not auto-created using the API — set them up separately.
Additional references
For official documentation, refer to the following resources:
SLO documentation
— concepts, SLI types, budgeting methods, and dashboard panels.
- Create an SLO — step-by-step
guide for creating SLOs in the Kibana UI.
searching, filtering, and managing existing SLOs.
Kibana SLO API
- Create an SLO — full request body schema
with all SLI type payloads.
Update |
Delete |
- Enable |
Disable |
Troubleshooting and access
Other skills for the same job
different authors, same section of the catalogue
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
How to use it
Take elastic/observability-manage-slos from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.