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Clickhouse Managed Postgres Rca

Organization
ClickHouse
clickhouse-managed-postgres-rca

MUST USE when investigating performance issues on a ClickHouse-managed Postgres instance. Provides an evidence-based RCA workflow that scrapes the Prometheus endpoint for system signal, pulls per-digest evidence from the Slow Query Patterns API, and recommends (does not apply) a fix.

Overview

PublisherClickHouse
Repositoryagent-skills
Skill nameclickhouse-managed-postgres-rca
Stars
543
Forks
39
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Clickhouse Managed Postgres Rca 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/ClickHouse/agent-skills/tree/main/skills/clickhouse-managed-postgres-rca
  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/ClickHouse/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/clickhouse-managed-postgres-rca .claude/skills/clickhouse-managed-postgres-rca
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clickhouse Managed Postgres Rca 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 Clickhouse Managed Postgres Rca 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 Clickhouse Managed Postgres Rca 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.

ClickHouse Managed Postgres RCA

When to use

Trigger whenever a user reports slowness, high CPU, low throughput, cache thrash, or any unexplained pain on a ClickHouse-managed Postgres instance.

What you have access to

Two APIs on https://api.clickhouse.cloud (HTTP Basic auth using a ClickHouse Cloud API key/secret pair):

  • Prometheus metrics — operation postgresInstancePrometheusGet under the Prometheus tag. Returns Prometheus exposition format. System and workload metrics for one Postgres service.
  • Slow Query Patterns — operation slowQueryPatternsGetList under the Postgres tag. Returns per-digest latency, IO, and call statistics for normalized query patterns. Beta.

Both endpoints require an organizationId and a serviceId as path parameters. The user must supply both, plus the API key/secret pair.

What you do NOT have

  • Query plans / EXPLAIN output.
  • Per-table scan-type counters (seq_scan / idx_scan).
  • Autovacuum or last-ANALYZE timestamps.

Reason from IO and timing signals, not from a plan tree.

Workflow

Six steps, in order. Do not skip ahead.

Steps 2 and 3 only share auth — no data dependency between them. Run them in parallel (background curls, & + wait) to cut wall time from sequential ~2s to ~1s.

1. Discover the live API shape

These endpoints are Beta — paths, params, and JSON field names can shift. Follow rules/openapi-discovery.md to:

  1. Fetch the OpenAPI spec from https://api.clickhouse.cloud/v1.
  2. Locate the two operations by operationId:
    • postgresInstancePrometheusGet (Prometheus tag)
    • slowQueryPatternsGetList (Postgres tag)
  3. Resolve their path templates, required query parameters, and (for the slow-query endpoint) the response schema.
  4. Build a session-scoped role map from the schema property descriptions: { semantic role → actual field name }.

Use the resolved names in every subsequent request and citation. Never hardcode field names from memory.

2. Scrape Prom once for system gauges

Follow rules/prometheus-scrape.md. One scrape, no wait. You're after gauges (current values) that don't need a delta: CacheHitRatio, ActiveConnections, MemoryUsedPercent, FilesystemUsedPercent.

A CacheHitRatio well below ~95% on a workload that should fit in cache is a real signal on its own. Climbing ActiveConnections toward the pool ceiling is a real signal on its own. These don't need rate-of-change.

A second scrape for counter deltas is opt-in, used only when Step 4 triage points at write-congestion (where deadlock and rollback rates matter and the Slow Query Patterns API can't substitute). For the read-path case (the most common RCA shape) the single scrape is enough.

3. Pull top slow query patterns

Request the slow query patterns. Follow rules/slow-query-patterns-fields.md for the fields that matter and how to read them. This is the primary diagnostic — it returns per-pattern accumulated totals (call count, runtime, blocks, rows) over the window you request, which is the "rate-of-change" data you'd otherwise derive from two Prom scrapes — but per query and without waiting.

If no patterns return a meaningful totalDurationUs, the report may be overstated or the issue isn't query-shaped. Stop and tell the user what you looked at.

4. Triage: pick the right heuristic

Follow rules/triage.md. Match the combined Prom + slow-query signal to one of the heuristic shapes. Each shape points to a specific heuristic file:

  • rules/heuristic-full-scan.md — read-path full scan.
  • rules/heuristic-hot-loop.md — N+1 / hot loop from the app.
  • rules/heuristic-write-congestion.md — deadlocks, slow writes, high rollback rate.

If the signal does not match any shape cleanly, do not invent a hypothesis. Surface the top patterns and ask the user which workload they recognize. New heuristics are welcome as PRs.

5. Reason, then recommend

Use the format in rules/output-template.md. Always include: symptom, evidence, hypothesis (noting any alternative cause you cannot rule out from this surface alone), short-term fix, and long-term follow-ups.

6. Do not apply the fix

Follow rules/recommend-only.md. Never run DDL. Never call pg_cancel_backend or pg_terminate_backend. Write the recommendation, explain why, and let the human apply it.

Full Compiled Document

For the complete guide with every rule expanded in a single context load: AGENTS.md.

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 Clickhouse Managed Postgres Rca AI skill do?

MUST USE when investigating performance issues on a ClickHouse-managed Postgres instance. Provides an evidence-based RCA workflow that scrapes the Prometheus endpoint for system signal, pulls per-digest evidence from the Slow Query Patterns API, and recommends (does not apply) a fix.

Why use Clickhouse Managed Postgres Rca on TypingMind?

Because you install it once and use it with any model. Clickhouse Managed Postgres Rca 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 Clickhouse Managed Postgres Rca in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClickHouse/agent-skills/tree/main/skills/clickhouse-managed-postgres-rca. 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 Clickhouse Managed Postgres Rca?

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 Clickhouse Managed Postgres Rca?

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

Is the Clickhouse Managed Postgres Rca AI skill free?

Yes. It is published on GitHub by ClickHouse 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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