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Diagnose Clickhouse Clusters

Community
FrankChen021
diagnose-clickhouse-clusters

Diagnose ClickHouse cluster health and provide concrete remediation.

Overview

PublisherFrankChen021
Repositorydatastoria
Skill namediagnose-clickhouse-clusters
Stars
327
Forks
18
Bundled files
Instructions only
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 FrankChen021 on GitHub. Read the source before you install it.

Installation

Install the Diagnose Clickhouse Clusters 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/FrankChen021/datastoria/tree/master/resources/skills/diagnose-clickhouse-clusters
  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/FrankChen021/datastoria.git /tmp/datastoria
mkdir -p .claude/skills
cp -r /tmp/datastoria/resources/skills/diagnose-clickhouse-clusters .claude/skills/diagnose-clickhouse-clusters
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Diagnose Clickhouse Clusters 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 Diagnose Clickhouse Clusters 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 Diagnose Clickhouse Clusters 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.

Tool Usage Rules

  • Call collect_cluster_status before health conclusions about current cluster health.
  • For RCA questions, call collect_rca_evidence directly when the symptom and target are already clear. Use collect_cluster_status first only when you need current health context, severity/outliers, or help choosing the RCA symptom/scope.
  • Use only supported Phase 1 RCA symptoms: high_part_count and unknown.
  • For bounded-time questions, use status_analysis_mode="windowed" and reuse the same time window in follow-up calls.
  • If user asks for a chart, use the visualization skill. Do not emit chart specs directly from this skill.
  • Do not invent custom health-check SQL. Use tool outputs as the source of truth.

Workflow (MANDATORY)

  1. Determine whether the user asks for status only, or root cause ("why", "root cause", "reason", "caused by", "explain").
  2. For RCA questions, pick one supported canonical symptom key based on user wording, explicit target details, and, when needed, status findings.
  3. Explain from tool output only: top candidates, support score, evidence lists, gaps, and prioritized actions.

Severity Thresholds (Guidance)

  • CRITICAL: replication lag > 300s, disk usage > 90%
  • WARNING: replication lag > 60s, disk usage > 80%
  • OK: metrics within normal ranges

Do not hardcode parts thresholds in responses. Use the thresholds and severities returned by collect_cluster_status.

Output Format (MANDATORY)

Use one of these two formats:

A) Status-only question

  1. Summary table: Always print a table title line exactly before the table: ### Summary.

    StatusNodes with IssuesChecks RunTimestamp
    🟢 OK / 🟠 WARNING / 🔴 CRITICALNcategoriesISO8601
  2. Findings by category: Always print a table title line exactly before the table: ### Findings by Category. Use a markdown table (not bullets) with one row per category. Required columns:

    CategoryStatusKey MetricsTop Outlier / ScopeNotes
    parts / errors / replication / ...🟢 OK / 🟠 WARNING / 🔴 CRITICALconcise metric values with thresholdsnode/table if present, else -one short phrase

    Table rules:

    • Include all categories returned by collect_cluster_status in stable order.
    • Status must include both emoji and text (for example 🟠 WARNING), never emoji-only.
    • Markdown table cells do not reliably support line breaks in this UI. Do not try to render multi-line bullets in a cell.
    • In Key Metrics, put the 1-2 most important metrics only (single-line, semicolon-separated if needed).
    • Put additional metrics in Notes as compact key/value items (single-line).
    • Put numeric values first (for example max_parts_per_table=533 (>500)), avoid prose-heavy sentences.
    • Always wrap database/table identifiers in backticks (for example `db.table` or `db`) in all table cells.
    • If category has sub-findings (for example top errors), keep them in Notes as compact comma-separated items.
    • If no outlier exists, set Top Outlier / Scope to -.
  3. Recommendations (max 3 items; each item = title + why + concrete SQL/command if needed).

B) RCA question ("why", "cause", "reason", "explain")

Use compact structure only:

  1. RCA Verdict: one sentence, max 30 words.
  2. Top Candidates: markdown table with max 3 rows: cause | support_score | evidence. In evidence, render up to 3 evidence_for items prefixed with ✓ and up to 2 evidence_against items prefixed with ✗, separated by <br/>. When excluded_candidates is non-empty, include at least one excluded reason as a ✗ item for the most relevant row. Evidence fidelity rules:
    • Use only candidate.evidence_for and candidate.evidence_against from collect_rca_evidence for that row.
    • Do not pull extra lines from top-level observations, other candidates, or status output into the evidence cell.
    • Do not restate raw metrics unless they already appear inside candidate.evidence_for or candidate.evidence_against.
    • Preserve the candidate/tool counts: if helpful, you may mention indicators_matched/indicators_checked, but never imply more matched checks than the tool returned.
  3. Possible Actions: max 3 numbered items, sorted by impact. Formatting rule: print the line 3. **Possible Actions**, then a blank line, then an indented nested numbered list using exactly 1., 2., 3.. Do not continue the outer top-level numbering for action items.
  4. Gaps / Next Checks: max 2 bullets. Formatting rule: print the line 4. **Gaps / Next Checks**, then a blank line, then indented bullets using exactly -.

RCA brevity limits:

  • Keep total RCA response under 220 words (excluding SQL command blocks).
  • Do not add long background/theory paragraphs.
  • Use direct statements and numeric evidence.

Critical Rules

  • ALWAYS call collect_cluster_status before giving any opinion on current health.
  • Use status_analysis_mode="windowed" when user asks for a bounded time window or historical context.
  • For RCA questions, MUST call collect_rca_evidence. collect_cluster_status is optional unless current health context is needed.
  • Do NOT state root causes without RCA evidence output.
  • If gaps[] is non-empty, explicitly state what evidence is missing.
  • If all candidates have support_score < 0.3, state that the RCA is inconclusive and use candidate next_checks plus gaps to explain what to inspect next.
  • If best candidate is weak (0.30-0.39), present it as a possibility with caveats and emphasize candidate next_checks.
  • Never fabricate or merge evidence lines across candidates. Candidate rows must be traceable directly to that candidate's evidence_for and evidence_against.
  • If collect_rca_evidence.related_symptoms is non-empty, include a line Related symptoms: and list them.
  • When follow-up questions omit time range, reuse the most recent explicit time window/range from prior turns.
  • Never assume schema or table names; use only what tools return.
  • Do not invent custom health-check SQL; use tool outputs as source of truth.
  • Be concise and focus on remediation, not theory.

Frequently asked questions

What does the Diagnose Clickhouse Clusters AI skill do?

Diagnose ClickHouse cluster health and provide concrete remediation.

Why use Diagnose Clickhouse Clusters on TypingMind?

Because you install it once and use it with any model. Diagnose Clickhouse Clusters 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 Diagnose Clickhouse Clusters in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FrankChen021/datastoria/tree/master/resources/skills/diagnose-clickhouse-clusters. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Diagnose Clickhouse Clusters?

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 Diagnose Clickhouse Clusters?

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

Is the Diagnose Clickhouse Clusters AI skill free?

It is published on GitHub by FrankChen021. Check the repository for licensing terms. 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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