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Send Data

Organization
grafana
send-data

Sending telemetry data to Grafana Cloud — metrics via Prometheus remote write or OTLP, logs via Loki push or Alloy, traces via OTLP to Tempo, profiles via Pyroscope. Covers Alloy-based pipelines, direct SDK/agent integrations, cloud integrations catalog, and credentials management. Use when connecting an application or infrastructure to Grafana Cloud, setting up data ingestion, configuring remote write, or choosing between ingestion methods.

Overview

Publishergrafana
Repositoryskills
Skill namesend-data
Stars
289
Forks
27
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by grafana on GitHub. Read the source before you install it.

Installation

Install the Send Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/grafana/skills/tree/main/skills/grafana-cloud/send-data
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/grafana/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/grafana-cloud/send-data .claude/skills/send-data
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Send Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Send Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Send Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sending Data to Grafana Cloud

Docs: https://grafana.com/docs/grafana-cloud/send-data/

Quick Start: Find Your Credentials

In Grafana Cloud portal → My Account → Stack → Details:

SignalCredential Fields
MetricsPrometheus remote write URL, username, password/API key
LogsLoki URL, username, password/API key
TracesTempo OTLP endpoint, username, password/API key
ProfilesPyroscope URL, username, password/API key

Alloy (Recommended — All Signals)

alloy
// METRICS
prometheus.scrape "app" {
  targets    = [{"__address__" = "localhost:8080"}]
  forward_to = [prometheus.remote_write.cloud.receiver]
}

prometheus.remote_write "cloud" {
  endpoint {
    url = "https://prometheus-prod-xx.grafana.net/api/prom/push"
    basic_auth {
      username = sys.env("PROM_USER")
      password = sys.env("GRAFANA_CLOUD_API_KEY")
    }
  }
}

// LOGS
loki.source.file "app" {
  targets = [{__path__ = "/var/log/app/*.log", job = "app"}]
  forward_to = [loki.write.cloud.receiver]
}

loki.write "cloud" {
  endpoint {
    url = "https://logs-prod-xx.grafana.net/loki/api/v1/push"
    basic_auth {
      username = sys.env("LOKI_USER")
      password = sys.env("GRAFANA_CLOUD_API_KEY")
    }
  }
}

// TRACES (OTLP receive → forward)
otelcol.receiver.otlp "default" {
  grpc { endpoint = "0.0.0.0:4317" }
  http { endpoint = "0.0.0.0:4318" }
  output {
    traces = [otelcol.exporter.otlp.cloud.input]
  }
}

otelcol.exporter.otlp "cloud" {
  client {
    endpoint = "tempo-prod-xx.grafana.net:443"
    auth = otelcol.auth.basic.cloud.handler
  }
}

otelcol.auth.basic "cloud" {
  username = sys.env("TEMPO_USER")
  password = sys.env("GRAFANA_CLOUD_API_KEY")
}

Direct Prometheus Remote Write

yaml
# prometheus.yml
remote_write:
  - url: https://prometheus-prod-xx.grafana.net/api/prom/push
    basic_auth:
      username: "123456"
      password: "your-api-key"
    write_relabel_configs:
      - source_labels: [__name__]
        regex: "go_.*"
        action: drop   # optional: drop high-cardinality metrics

Direct Loki Push (curl)

bash
curl -X POST https://logs-prod-xx.grafana.net/loki/api/v1/push \
  -H "Content-Type: application/json" \
  -u "123456:your-api-key" \
  -d '{
    "streams": [{
      "stream": { "app": "myapp", "env": "prod" },
      "values": [
        ["1706745600000000000", "application started"]
      ]
    }]
  }'

OpenTelemetry SDK → Grafana Cloud

bash
# Environment variables for OTLP export
export OTEL_EXPORTER_OTLP_ENDPOINT="https://otlp-gateway-prod-xx.grafana.net/otlp"
export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Basic $(echo -n '123456:your-api-key' | base64)"
export OTEL_SERVICE_NAME="my-service"

Python example:

python
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

provider = TracerProvider()
exporter = OTLPSpanExporter()  # reads OTEL_EXPORTER_OTLP_* env vars
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)

Cloud Integrations

Pre-built integrations for common infrastructure (install from Grafana Cloud UI or API):

bash
# List available integrations
curl https://integrations-api-prod.grafana.net/api/v1/integrations \
  -H "Authorization: Bearer <api-key>"

# Install AWS CloudWatch integration
curl -X POST https://integrations-api-prod.grafana.net/api/v1/integrations/cloudwatch \
  -H "Authorization: Bearer <api-key>" \
  -H "Content-Type: application/json" \
  -d '{"name": "aws-prod", "config": {...}}'

Popular integrations: AWS CloudWatch, Azure Monitor, GCP, Kubernetes, Docker, MySQL, PostgreSQL, Redis, Nginx, Apache, JVM, Node.js, Python, .NET.

Kubernetes Agent Operator

yaml
# values.yaml for grafana/k8s-monitoring Helm chart
cluster:
  name: production

externalServices:
  prometheus:
    host: https://prometheus-prod-xx.grafana.net
    basicAuth:
      username: "123456"
      password:
        secretName: grafana-cloud-secret
        secretKey: api-key

  loki:
    host: https://logs-prod-xx.grafana.net
    basicAuth:
      username: "234567"
      password:
        secretName: grafana-cloud-secret
        secretKey: api-key

metrics:
  enabled: true
  podMonitors:
    enabled: true
  serviceMonitors:
    enabled: true

logs:
  pod_logs:
    enabled: true

traces:
  enabled: true
bash
helm install k8s-monitoring grafana/k8s-monitoring \
  --version 4.1.4 \
  -n monitoring --create-namespace \
  -f values.yaml

API Key Management

bash
# Create API key via Grafana API
curl -X POST https://yourstack.grafana.net/api/auth/keys \
  -H "Content-Type: application/json" \
  -u "admin:adminpassword" \
  -d '{"name": "alloy-writer", "role": "MetricsPublisher", "secondsToLive": 0}'

Roles for data ingestion: MetricsPublisher, LogsPublisher, TracesPublisher, ProfilesPublisher

Frequently asked questions

What does the Send Data AI skill do?

Sending telemetry data to Grafana Cloud — metrics via Prometheus remote write or OTLP, logs via Loki push or Alloy, traces via OTLP to Tempo, profiles via Pyroscope. Covers Alloy-based pipelines, direct SDK/agent integrations, cloud integrations catalog, and credentials management. Use when connecting an application or infrastructure to Grafana Cloud, setting up data ingestion, configuring remote write, or choosing between ingestion methods.

Why use Send Data on TypingMind?

Because you install it once and use it with any model. Send Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Send Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/grafana/skills/tree/main/skills/grafana-cloud/send-data. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Send Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Send Data?

As many as you like. As long as a model supports skills, you can use Send Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Send Data AI skill free?

Yes. It is published on GitHub by grafana under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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