observability-manage-slos▌
elastic/agent-skills · updated Apr 8, 2026
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Create and manage SLOs in Elastic Observability. SLOs track service performance against measurable targets using
- ›service-level indicators (SLIs) computed from Elasticsearch data.
Service-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
- Service-level objectives (SLOs) — concepts, SLI types, budgeting methods, and dashboard panels.
- Create an SLO — step-by-step guide for creating SLOs in the Kibana UI.
- View and manage SLOs — searching, filtering, and managing existing SLOs.
Kibana SLO API
- Create an SLO — full request body schema with all SLI type payloads.
- Get an SLO | Update | Delete | Reset
- Enable | Disable | Get definitions
Troubleshooting and access
How to use observability-manage-slos 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 observability-manage-slos
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches observability-manage-slos 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 observability-manage-slos. Access the skill through slash commands (e.g., /observability-manage-slos) 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.
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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
4.7★★★★★71 reviews- ★★★★★Hassan Brown· Dec 28, 2024
Solid pick for teams standardizing on skills: observability-manage-slos is focused, and the summary matches what you get after install.
- ★★★★★Evelyn Perez· Dec 28, 2024
observability-manage-slos has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zaid Choi· Dec 24, 2024
Registry listing for observability-manage-slos matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★William Kim· Dec 16, 2024
I recommend observability-manage-slos for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Sakshi Patil· Nov 19, 2024
Keeps context tight: observability-manage-slos is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Evelyn Abbas· Nov 19, 2024
observability-manage-slos has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Evelyn Choi· Nov 19, 2024
Solid pick for teams standardizing on skills: observability-manage-slos is focused, and the summary matches what you get after install.
- ★★★★★Li Haddad· Nov 15, 2024
observability-manage-slos reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Zaid Huang· Nov 7, 2024
observability-manage-slos fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Li Khan· Oct 26, 2024
observability-manage-slos has been reliable in day-to-day use. Documentation quality is above average for community skills.
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