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ML AI

Grafana LabsOfficialPublished by Grafana Labs

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Turn on AI + ML features in Grafana Cloud — Grafana Assistant (NL → PromQL/LogQL/TraceQL, dashboard build, incident investigation, MCP integration), Dynamic Alerting (Prophet forecasting + DBSCAN outlier detection), Sift (8-analysis automated root-cause), Knowledge Graph + RCA Workbench, and the LLM Plugin (OpenAI / Anthropic / Azure / Ollama / vLLM / LiteLLM). Use when you want anomaly alerts without static thresholds, natural-language querying, automated incident investigation, dashboards generated from a sentence, or a managed LLM proxy for plugins — even when the user says "alert when something looks weird", "explain this PromQL", "find the root cause", "make this a dashboard", or "wire Claude into Grafana" without naming any of these products.
— the description by Grafana Labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive guide to enabling and configuring AI and machine learning capabilities within the Grafana Cloud ecosystem. It covers the setup of Dynamic Alerting for anomaly detection, the Sift automated root-cause analysis engine, the Grafana Assistant for natural language interactions, and the LLM plugin for connecting external AI providers. The documentation details how to leverage these tools to reduce manual monitoring overhead, automate incident response, and simplify complex data exploration tasks.

What it does

  • Configures Dynamic Alerting using Prophet and DBSCAN algorithms for anomaly detection without static thresholds.
  • Enables Sift for automated root-cause analysis across eight distinct diagnostic categories.
  • Integrates Grafana Assistant to translate natural language into PromQL, LogQL, or TraceQL queries.
  • Provides a managed LLM proxy to connect providers like OpenAI, Anthropic, and Ollama to Grafana panels.
  • Utilizes Knowledge Graph and RCA Workbench to visualize entity relationships and blast radiuses.
  • Supports automated dashboard generation and incident investigation workflows.

When to use it

  • When you need to move beyond static alert thresholds to seasonality-aware or outlier-based alerting.
  • When you want to accelerate incident response through automated root-cause analysis.
  • When users require natural language interfaces to query metrics, logs, or traces.
  • When you need to centralize LLM access for various Grafana plugins and panel enhancements.
  • When troubleshooting complex service dependencies and resource contention issues.

Advantages

  • Reduces alert fatigue by using machine learning to identify true anomalies.
  • Accelerates mean time to resolution (MTTR) with automated incident investigation.
  • Lowers the barrier to entry for complex query languages via natural language processing.
  • Provides a unified interface for managing multiple AI and LLM integrations.
  • Offers deep visibility into infrastructure relationships through automated knowledge graphs.

Usage examples

“Set up an alert that fires when CPU usage deviates from its normal weekly pattern.”
The agent guides you to configure a Dynamic Alerting job using the Prophet algorithm to forecast expected metric behavior.
“Find the root cause of the latency spike in the checkout service.”
The agent triggers a Sift investigation, running eight automated analyses to correlate logs, deployments, and resource contention.
“Explain what this PromQL query is doing.”
The Grafana Assistant analyzes the query and provides a plain-English explanation of its logic and purpose.
“Connect Claude to my Grafana instance.”
The agent provides the configuration steps to provision the LLM plugin with your Anthropic API credentials.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/grafana/ml-ai/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip ml-ai.zip -d .agents/skills

Documentation by Grafana Labs

Grafana Cloud AI & ML

Docs: https://grafana.com/docs/grafana-cloud/alerting-and-irm/machine-learning/

ML alerting + automated RCA + LLM-powered Assistant in one Grafana Cloud stack.

Prerequisites

  • Grafana Cloud stack (Pro / Advanced — most features GA, some in preview)

  • API token with plugins:write for ML / Sift / LLM-plugin endpoints

  • For Dynamic Alerting: at least 14 days (ideally 90d) of history for the metric you want to forecast

Common Workflows

1. Forecasting alert with Dynamic Alerting
# 1. Create forecast job (Prophet — learns daily/weekly seasonality)
curl -X POST https://<stack>.grafana.net/api/plugins/grafana-ml-app/resources/ml/v1/forecast \
  -H "Authorization: Bearer <token>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "cpu-forecast",
    "metric": "avg(rate(node_cpu_seconds_total{mode=\"user\"}[5m]))",
    "datasourceId": 1,
    "interval": 300,
    "trainingWindow": "90d",
    "forecastWindow": "7d",
    "algorithm": { "name": "prophet", "config": {} }
  }'

# 2. Verify job is producing the predicted-value metric (may take a few minutes).
#    <datasourceId> must match the datasourceId used above (find it via
#    GET /api/datasources), or run the query from Explore instead.
curl -s -H "Authorization: Bearer <token>" \
  'https://<stack>.grafana.net/api/datasources/proxy/<datasourceId>/api/v1/query?query=ml_forecast_upper{job="cpu-forecast"}' \
  | jq '.data.result | length'
# Expect > 0

# 3. Add an alert that fires when actual exceeds the upper bound
# expr:  avg(rate(node_cpu_seconds_total{mode="user"}[5m]))
#         > ml_forecast_upper{job="cpu-forecast"} * 1.1
2. Outlier alert — one service deviates from peers
# 1. Create outlier job (DBSCAN — groups peers, flags the odd one)
curl -X POST https://<stack>.grafana.net/api/plugins/grafana-ml-app/resources/ml/v1/outlier \
  -H "Authorization: Bearer <token>" -H "Content-Type: application/json" \
  -d '{
    "name": "service-error-outliers",
    "metric": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) by (service)",
    "datasourceId": 1,
    "interval": 300,
    "algorithm": { "name": "dbscan", "sensitivity": 0.5, "config": { "epsilon": 0.5 } }
  }'

# 2. Verify the score metric exists (<datasourceId> must match the
#    datasourceId used above, or run the query from Explore instead)
curl -s -H "Authorization: Bearer <token>" \
  'https://<stack>.grafana.net/api/datasources/proxy/<datasourceId>/api/v1/query?query=ml_outlier_score{job="service-error-outliers"}' \
  | jq '.data.result | length'

# 3. Alert when ml_outlier_score{job="service-error-outliers"} > 0.8 for 5m
3. Run a Sift investigation
# 1. Trigger from API (or from Explore / Incident / OnCall)
curl -X POST https://<stack>.grafana.net/api/plugins/grafana-sift-app/resources/sift/v1/investigations \
  -H "Authorization: Bearer <token>" -H "Content-Type: application/json" \
  -d '{ "name":"checkout-spike","start":"2024-02-01T10:00:00Z","end":"2024-02-01T10:30:00Z",
        "filters":{"service":"checkout","namespace":"production"} }'

# 2. The response includes an investigation ID — open it in the UI:
#    https://<stack>.grafana.net/a/grafana-sift-app/investigations/<id>
# 3. Verify analyses ran — each of the 8 checks shows ✔ or ✖ with linked evidence.

See references/sift.md for the full 8-analysis table.

4. Wire up the LLM Plugin
# 1. Provision (provisioning/plugins/llm.yaml — see references/llm-and-graph.md)
apiVersion: 1
apps:
  - type: grafana-llm-app
    jsonData: { openAIUrl: https://api.openai.com, openAIModel: gpt-4o }
    secureJsonData: { openAIKey: sk-... }
# 2. Restart Grafana, then verify the health endpoint reports the configured provider
curl -s -H "Authorization: Bearer <token>" \
  https://<stack>.grafana.net/api/plugins/grafana-llm-app/health | jq
# Expect: {"status":"ok", ...}

# 3. Verify in a panel — open any panel, click the Assistant icon, ask "what does this query do?"

See references/llm-and-graph.md for Assistant capabilities, Knowledge Graph search syntax, and Adaptive Metrics recommendations.

Resources

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access

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What our scanner noticed· Low risk

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Attribution and licence

Published by
Grafana Labs
Publisher
Grafana Labs (Official)
Source
grafana/skills · skills/grafana-cloud/ml-ai · commit 8fb72a5
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

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