Database Observability logo

Database Observability

Organization
grafana
database-observability

Set up Grafana Cloud Database Observability for MySQL and PostgreSQL — enables `pg_stat_statements` / Performance Schema, creates a least-privilege monitoring user, configures the `database_observability.postgres` / `database_observability.mysql` Alloy components, ships query samples + visual explain plans + RED metrics + schema details to Grafana Cloud, and correlates slow queries with application traces via `db.statement` / `db.system` OTel attributes. Use when monitoring database performance, diagnosing slow queries, setting up DB observability for RDS / Aurora / Cloud SQL / Azure Database / self-managed instances, correlating DB metrics with APM, or alerting on query latency — even when the user says "my database is slow", "find the slow queries", or "monitor RDS" without saying "observability".

Overview

Publishergrafana
Repositoryskills
Skill namedatabase-observability
Stars
289
Forks
27
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Database Observability 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/database-observability
  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/database-observability .claude/skills/database-observability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Database Observability 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 Database Observability 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 Database Observability 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.

Grafana Cloud Database Observability

Docs: https://grafana.com/docs/grafana-cloud/monitor-applications/database-observability/

Query-level insights (RED metrics, query samples, explain plans) for MySQL and PostgreSQL without application code changes. GA since April 2026.

Common Workflows

PostgreSQL setup

sql
-- 1. Enable pg_stat_statements (requires restart)
--    In postgresql.conf:  shared_preload_libraries = 'pg_stat_statements'
--    Then restart PostgreSQL.

-- 2. Create a least-privilege monitoring user
CREATE USER grafana_monitoring WITH PASSWORD 'secret';
GRANT pg_monitor TO grafana_monitoring;
GRANT CONNECT ON DATABASE mydb TO grafana_monitoring;

-- 3. Enable the extension on each monitored DB
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- 4. Verify
SELECT count(*) FROM pg_stat_statements;
-- Should return >= 0 (not an error). If "relation does not exist": step 3 didn't run.
  1. Add the Alloy config block from references/alloy-config.md § PostgreSQL
  2. Restart Alloy and wait ~60s for first scrape
  3. Verify in Grafana Cloud → Database Observability — the instance should appear with non-empty db_query_total{db_instance="mydb"} series. If empty after 2 minutes: check Alloy logs for connection errors, then re-verify pg_stat_statements permissions.

MySQL setup

sql
-- 1. Create least-privilege monitoring user
CREATE USER 'grafana_monitoring'@'%' IDENTIFIED BY 'secret';
GRANT SELECT, PROCESS, REPLICATION CLIENT ON *.* TO 'grafana_monitoring'@'%';
GRANT SELECT ON performance_schema.* TO 'grafana_monitoring'@'%';
FLUSH PRIVILEGES;

-- 2. Verify performance_schema is enabled (default in MySQL 8+)
SELECT @@performance_schema;
-- Returns 1 → enabled. 0 → set performance_schema=ON in my.cnf + restart.
  1. Add the Alloy config block from references/alloy-config.md § MySQL
  2. Restart Alloy, wait ~60s, verify the instance appears in Grafana Cloud → Database Observability

Supported databases

DatabaseVariants
MySQLSelf-managed, RDS MySQL, Aurora MySQL, Cloud SQL MySQL, Azure Database for MySQL
PostgreSQLSelf-managed, RDS PostgreSQL, Aurora PostgreSQL, Cloud SQL PostgreSQL, Azure Database for PostgreSQL

For managed databases (RDS / Cloud SQL / Azure), pg_stat_statements is preinstalled but disabled — enable it via the parameter group / flag, then re-create the extension at the DB level.

What you get

  • Query Performance dashboard (auto-provisioned): top queries by total time / call count / mean latency, sampled query texts with parameters, visual explain plans, RED metrics per query digest
  • APM correlation: drill from a slow service-latency span to the specific slow SQL via db.statement / db.system / db.name OTel attributes — see references/queries-alerts.md § Trace correlation
  • Alerting on query latency, error rate, and connection-pool saturation — full alert YAML in references/queries-alerts.md § Alert rules

References

  • references/alloy-config.md — full Alloy database_observability.postgres / database_observability.mysql config blocks + collector reference (pg_stat_statements, query_samples, schema_details, explain_plans)
  • references/queries-alerts.md — key PromQL queries, alert rule YAML, and trace-correlation setup

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Database Observability AI skill do?

Set up Grafana Cloud Database Observability for MySQL and PostgreSQL — enables `pg_stat_statements` / Performance Schema, creates a least-privilege monitoring user, configures the `database_observability.postgres` / `database_observability.mysql` Alloy components, ships query samples + visual explain plans + RED metrics + schema details to Grafana Cloud, and correlates slow queries with application traces via `db.statement` / `db.system` OTel attributes. Use when monitoring database performance, diagnosing slow queries, setting up DB observability for RDS / Aurora / Cloud SQL / Azure Databa...

Why use Database Observability on TypingMind?

Because you install it once and use it with any model. Database Observability 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 Database Observability in TypingMind?

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

Which AI models can use Database Observability?

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 Database Observability?

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

Is the Database Observability 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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