Answer user questions about monitoring LLMs and agentic components using data ingested into Elastic only. Focus on
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add elastic/agent-skills --skill observability-llm-obsCompatible with
Supported agents
LLM performance, cost and token utilization, response quality, and call chaining or agentic workflow orchestration. Use
ES|QL, Elasticsearch APIs, and (where needed) Kibana APIs. Do not rely on Kibana UI; the skill works without it. A
given deployment typically uses one or more ingestion paths (APM/OTLP traces and/or integration metrics/logs)—
discover what is available before query
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionobservability-llm-obsExecute the skills CLI command in your project's root directory to begin installation:
epx add elastic/agent-skills --skill observability-llm-obsFetches observability-llm-obs from elastic/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/observability-llm-obs
Restart Cursor to activate observability-llm-obs. Access via /observability-llm-obsin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
brainstorm
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Solid pick for teams standardizing on skills: observability-llm-obs is focused, and the summary matches what you get after install.
observability-llm-obs has been reliable in day-to-day use. Documentation quality is above average for community skills.
observability-llm-obs reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: observability-llm-obs is the kind of skill you can hand to a new teammate without a long onboarding doc.
observability-llm-obs fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
observability-llm-obs has been reliable in day-to-day use. Documentation quality is above average for community skills.
observability-llm-obs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: observability-llm-obs is focused, and the summary matches what you get after install.
We added observability-llm-obs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added observability-llm-obs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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