Datasources Provisioning logo

Datasources Provisioning

Organization
grafana
datasources-provisioning

Generate a copy-paste Grafana data source provisioning file (YAML or Terraform) for any plugin from its standardized settings schema on the plugins CDN. Use when the user wants to provision or configure a data source as code — e.g. "provision infinity", "datasource yaml for clickhouse", "terraform for the github datasource" — even when they only name the plugin and not the word "provisioning".

Overview

Publishergrafana
Repositoryskills
Skill namedatasources-provisioning
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 Datasources Provisioning 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-datasources/datasources-provisioning
  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-datasources/datasources-provisioning .claude/skills/datasources-provisioning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Datasources Provisioning 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 Datasources Provisioning 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 Datasources Provisioning 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.

Workflow

1. Ask the starting point: from scratch, or from an existing data source?

Ask this before anything else (skip only if the user already made it clear):

  • From scratch — the user names a plugin type to provision → continue with step 2.
  • From an existing data source in a running instance → jump to Convert an existing data source, then return to step 6.

2. Resolve the full plugin id

Provisioning needs the canonical plugin id (<org>-<name>-datasource), not the short name a user might say.

  • Already canonical (contains -datasource or -app)? Use as-is: yesoreyeram-infinity-datasource.
  • Short name only (e.g. infinity, clickhouse)? Search the catalog API with filter=<keyword>:
    bash
    curl -s "https://grafana.com/api/plugins?filter=infinity" \
      | jq -r '.items[] | "\(.slug)\t\(.name)"'
    # → yesoreyeram-infinity-datasource    Infinity
    Multiple matches → show the candidates and ask which one.

The snippets below use Infinity (yesoreyeram-infinity-datasource) as the worked example — substitute the id resolved here (and the version from step 3) in every command and output.

3. Resolve the latest version

bash
curl -s "https://grafana.com/api/plugins/yesoreyeram-infinity-datasource" | jq -r '.version'

Never hardcode a version — the CDN path is version-pinned and a stale version 404s.

4. Fetch the settings schema (primary structured source)

https://plugins-cdn.grafana.net/<PLUGIN_ID>/<VERSION>/public/plugins/<PLUGIN_ID>/schema/dsconfig.json
bash
ID=yesoreyeram-infinity-datasource
VER=$(curl -s "https://grafana.com/api/plugins/$ID" | jq -r '.version')
curl -sf "https://plugins-cdn.grafana.net/$ID/$VER/public/plugins/$ID/schema/dsconfig.json"

This file conforms to the dsconfig schema spec — the source of truth for how to interpret it. Don't re-derive field semantics from memory (valueType alone spans string, number, boolean, array, object, map, any); consult the spec when a field isn't a plain scalar:

What you need from each field to provision: key (the provisioning key), valueType, target (root | jsonData | secureJsonData), and validations (honor allowedValues for selectors like auth_method). Orientation example (schemaVersion: "v1"):

json
{
  "pluginType": "yesoreyeram-infinity-datasource",
  "fields": [
    {
      "key": "auth_method",
      "valueType": "string",
      "target": "jsonData",
      "validations": [
        {
          "type": "allowedValues",
          "values": [
            "none",
            "basicAuth",
            "apiKey",
            "bearerToken",
            "oauth2",
            "aws",
            "azureBlob"
          ]
        }
      ]
    }
  ]
}

Select only the fields relevant to what the user asked for (chosen auth method + connection), not all of them. Each field's description tells you which auth method it belongs to.

For ready-made example configs, fetch v0alpha1.json:

https://plugins-cdn.grafana.net/<PLUGIN_ID>/<VERSION>/public/plugins/<PLUGIN_ID>/schema/v0alpha1.json
bash
ID=yesoreyeram-infinity-datasource
VER=$(curl -s "https://grafana.com/api/plugins/$ID" | jq -r '.version')
curl -sf "https://plugins-cdn.grafana.net/$ID/$VER/public/plugins/$ID/schema/v0alpha1.json"

Worked examples live under settingsExamples.examples, an object keyed by scenario (e.g. apiKey, oauth2ClientCredentials). Each entry has a summary/description (the scenario) and a value holding the jsonData/secureJsonData payload to lift straight into the file:

bash
# list scenarios, then pull one payload
... | jq -r '.settingsExamples.examples | keys[]'
... | jq '.settingsExamples.examples.apiKey.value'

5. Fallback when no schema is published

If schema/dsconfig.json 404s (older plugins):

  • Last resort: the generic structure in grafana-oss skill (§ Data source provisioning) can also tell the user the field names are best-effort, not plugin-authoritative.

NOTE: grafana-oss skill is available in grafana-core plugin and also available as a standalone skill from the https://github.com/grafana/skills repository

6. Map each field by its target

targetYAMLTerraform (grafana_data_source)
roottop-level key on the datasource (url, basicAuth, basicAuthUser)top-level argument (url) / inside json_data_encoded
jsonDataunder jsonData:key inside json_data_encoded = jsonencode({ … })
secureJsonDataunder secureJsonData: as ${ENV_VAR}key inside secure_json_data_encoded = jsonencode({ … }) via a sensitive variable

Use each field's valueType for the scalar (string quoted in YAML, boolean→true/false, number bare). Never inline a real secret. Nested objects (oauth2, aws) and arrays (allowedHosts, scopes) map directly.

Always set access (root target) and default it to proxy — queries route through the Grafana server (the secure default); only use direct (browser → data source) if the user explicitly asks for it. In Terraform the argument is access_mode.

7. Ask the format, then emit the file

Now ask: YAML or Terraform? Same fields, different output file and syntax. Don't assume: "provision X" may mean either; skip the question only if the user already named a format ("terraform for X"). YAML file provisioning is the native, zero-dependency path; Terraform needs the official grafana/grafana provider.

ChoiceProduces
YAML config fileprovisioning/datasources/<name>.yaml
Terraform<name>.tf (grafana_data_source resource)

<name> is just the file's basename — cosmetic, since both loaders read every file in the directory regardless of name. Default it to the plugin name.

YAML → provisioning/datasources/<name>.yaml:

yaml
apiVersion: 1
datasources:
  - name: Infinity # must be unique across the instance — collides even with a different datasource type
    type: yesoreyeram-infinity-datasource # = pluginType from the schema
    access: proxy # always set; default proxy (route queries through the Grafana server)
    uid: infinity-ds # also unique and immutable so dashboards can reference it
    jsonData:
      auth_method: apiKey # value from validations.allowedValues
      apiKeyKey: X-API-Key
      apiKeyType: header
      allowedHosts:
        - https://api.example.com
    secureJsonData:
      apiKeyValue: ${API_KEY} # env var ref, never a literal secret
    editable: false

Terraform → <name>.tf:

hcl
variable "api_key" {
  type      = string
  sensitive = true
}

resource "grafana_data_source" "infinity" {
  type        = "yesoreyeram-infinity-datasource"
  name        = "Infinity"
  uid         = "infinity-ds"
  access_mode = "proxy" # always set; default proxy (route queries through the Grafana server)

  json_data_encoded = jsonencode({
    auth_method  = "apiKey"
    apiKeyKey    = "X-API-Key"
    apiKeyType   = "header"
    allowedHosts = ["https://api.example.com"]
  })

  secure_json_data_encoded = jsonencode({
    apiKeyValue = var.api_key
  })
}

grafana_data_source is from the grafana/grafana provider — the authoritative reference for argument names (access_mode, json_data_encoded, secure_json_data_encoded). This file is only the resource; the user supplies the required_providers + provider "grafana" block and credentials.

8. Return the file to the user

Present the complete file in a single code block for the user to copy and paste into their environment — note where it goes:

  • YAML → provisioning/datasources/<name>.yaml (apply on Grafana start or a provisioning reload).
  • Terraform → their Terraform config, applied with terraform apply.

Optionally, tell them how to confirm it worked once applied:

bash
curl -s https://grafana.example.com/api/datasources/uid/<uid>/health \
  -H "Authorization: Bearer <token>"
# { "status": "OK" }    → working
# { "status": "ERROR" } → URL unreachable or auth misconfigured

Or verify in the UI: visit <https://grafana.example.com>/connections/datasources/edit/<uid> and click Test.

Convert an existing data source

To codify a data source already configured in a running instance, read its config through the Grafana MCP server (grafana/mcp-grafana).

Precondition: the Grafana MCP server is connected with its Datasources toolset enabled (it holds the instance credentials). If it isn't available, do not support this path — never ask the user to paste a Grafana token into chat. Fall back to the from-scratch Workflow instead.

  1. Find the data source with the MCP tools — list_datasources to browse, then get_datasource (by uid or name) for the full config.
  2. The result carries every non-secret field directly: type, uid, url, access, basicAuth, basicAuthUser, and the full jsonData object. Copy them as-is.
  3. Secrets are never returned. The secureJsonFields map lists which secret keys are set (e.g. {"apiKeyValue": true}) without their values. Emit an ${ENV_VAR} placeholder in secureJsonData for each key it reports true.
  4. Cross-check against the schema (step 4) to confirm secret key names and target placement, then continue at step 6 (map) and step 7 (emit) as normal.

Related

  • grafana-oss — generic data source / dashboard provisioning structure and provisioning paths.

Frequently asked questions

What does the Datasources Provisioning AI skill do?

Generate a copy-paste Grafana data source provisioning file (YAML or Terraform) for any plugin from its standardized settings schema on the plugins CDN. Use when the user wants to provision or configure a data source as code — e.g. "provision infinity", "datasource yaml for clickhouse", "terraform for the github datasource" — even when they only name the plugin and not the word "provisioning".

Why use Datasources Provisioning on TypingMind?

Because you install it once and use it with any model. Datasources Provisioning 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 Datasources Provisioning in TypingMind?

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

Which AI models can use Datasources Provisioning?

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 Datasources Provisioning?

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

Is the Datasources Provisioning 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇