npx skills add ...
npx skills add coralogix/cx-cli --skill cx-olly
This skill should be used when the user asks to "chat with AI", "ask Olly", "ask the agent", "send message to AI", "continue a chat", "follow up on chat", "get artifact", "download artifact", "list artifacts", "retrieve generated content", "AI-generated charts", "AI analysis", "conversational observability", "natural language query", or wants to interact with the Coralogix Observability Agent (Olly) using the cx CLI.
npx skills add coralogix/cx-cli --skill cx-olly
Use this skill to interact with Coralogix's Observability Agent (Olly) via the cx olly CLI commands. Olly can analyze your observability data, answer questions about alerts, metrics, logs, and generate artifacts like charts and reports.
cx olly ask defaults --agent-to-agent-mode to false. If you're an LLM/agent, pass --agent-to-agent-mode - see "Agent-to-agent mode" below.
| Command | Purpose | Key flags |
|---|---|---|
cx olly ask "message" | Send a message to the Observability Agent | --chat-id, --model, --timeout, --agent-to-agent-mode |
cx olly artifacts list | List all generated artifacts | - |
cx olly artifacts get <id> | Get artifact content by ID | - |
Output format: append -o json or -o toon for machine-readable output.
Single-profile only: cx olly commands do not support multi-profile fan-out. Use -p <profile> to specify a single profile.
Run cx olly ask asynchronously by default: launch it as a background process and poll it for completion rather than blocking on it. Olly investigations routinely take minutes, so this is the normal mode for cx olly ask.
Only run cx olly ask inline (foreground, blocking) for a short question you expect Olly to answer quickly — a quick lookup or a one-line follow-up. When in doubt, run it in the background.
This creates a new chat and returns a response along with a Chat ID that you can use for follow-up questions.
Remove --agent-to-agent-mode if you don't have context to share (like quick access to source files) or if the created chat is only for human usage.
Use --chat-id to continue a conversation and maintain context from previous messages. Background the follow-up too when it kicks off another investigation.
Available models include gpt-5.2 (default), claude-sonnet-4-5, sonnet-4.6, gpt-5.4, claude-haiku-4-5.
For complex queries that may take longer, increase Olly's response timeout (default: 900 seconds):
--http-timeout <SECONDS> (or CX_HTTP_TIMEOUT) sets the HTTP request
deadline for all CLI commands, including Olly. It is separate from the Olly
response timeout.
--agent-to-agent-mode defaults to false, since cx olly ask is used directly by humans as well as by agents. If you're an LLM/agent, pass --agent-to-agent-mode to opt into shorter, sub-agent-style responses: no charts/tables, clarifying questions instead of guessing, and reliance on your broader context.
It's per-call, not per-chat. --chat-id does not remember it - re-pass --agent-to-agent-mode on every follow-up turn, or the mode silently flips back to human-facing mid-conversation.
Olly can generate artifacts like query results, previews, and citations. Artifact IDs appear as links in the agent's response text.
The artifacts get command automatically:
Output behavior:
/tmp/cx_results_artifact_<id>_<hash>.txt)cx olly ask as a background process and poll it for completion; only run inline (blocking) for short questions you expect Olly to answer quickly-o json for scripting - pipe to jq for filtering and extraction[Chart](https://...artifact_view/<id>)cx olly does not support multi-profile queries--agent-to-agent-mode when calling as an LLM/agent - it defaults to false (human-facing); agents should opt in for shorter, sub-agent-style responsescx-telemetry-querying - for direct DataPrime/PromQL queries without AI agent assistance (covers logs, spans, metrics, RUM)cx-alerts - for managing alert definitionscx olly ask "Explain this error" --model claude-sonnet-4-5 --agent-to-agent-modecx olly ask "Deep analysis of last week's incidents" --timeout 1800 --agent-to-agent-modecx olly ask "Analyze this for me" --agent-to-agent-modecx olly artifacts list
cx olly artifacts list -o jsoncx olly artifacts get <artifact-id>
cx olly artifacts get <artifact-id> -o json# Start investigation — run in the background and poll for completion
cx olly ask "Why is the checkout service showing high latency? Check logs with 'checkout:' strings and aws related metrics" --agent-to-agent-mode
# Follow up with the chat ID from the response — also background and poll
cx olly ask "What changed in the last hour?" --chat-id abc-123-def --agent-to-agent-mode
# Once the interaction has completed, get any generated charts
cx olly artifacts list -o json | jq '.[0].id'
cx olly artifacts get <artifact-id># Get response as JSON
cx olly ask "List top 5 error messages" -o json --agent-to-agent-mode | jq '.response'
# Parse artifacts
cx olly artifacts list -o json | jq '.[] | {id, filename, created_at}'cx olly ask "Perform root cause analysis for the outage on 2024-01-15" \
--model claude-sonnet-4-5 \
--timeout 1800 \
--agent-to-agent-mode