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Sql Expert

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FrankChen021
sql-expert

Expert system for generating, validating, and optimizing ClickHouse SQL. Use this when the user needs data, queries, or analysis.

Overview

PublisherFrankChen021
Repositorydatastoria
Skill namesql-expert
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 Sql Expert 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/sql-expert
  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/sql-expert .claude/skills/sql-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sql Expert 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 Sql Expert 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 Sql Expert 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.

🚨 CRITICAL RULE: MANDATORY VALIDATION

You MUST call validate_sql(sql) for every new query you generate. Context Note: Historical validation steps are pruned to save tokens, but this does NOT excuse you from validating new queries in the current turn. Always validate before executing.

1. Schema Discovery & Context

  • Missing Schema: If you do not have the table schema, you MUST use get_tables and explore_schema first.
    • Optimization: If the user already mentioned exact field names, pass them in the columns argument of explore_schema instead of loading the full table schema.
  • Exact Identifier Rule: Treat exact identifier-like tokens from the user question as candidate columns on the first schema lookup. This especially applies to ClickHouse metric names such as ProfileEvent_*, CurrentMetric_*, and flattened event columns on system.* tables.
  • Missing Columns: If you don't see the expected column, retry explore_schema with a narrower columns list based on the user-mentioned identifier or the closest confirmed column names.
  • Schema Fidelity: Only use columns that are confirmed to exist in the table schema from explore_schema. Do not assume standard columns exist if they are not in the tool output.
  • User Context: If the user asks about "my data", use WHERE user = '<clickHouseUser>'.
  • System Tables: For queries on system.* tables (e.g., system.query_log, system.parts, system.merges), defer to the clickhouse-system-queries skill - it contains table-specific patterns, predicates, and resource metrics that this skill does not cover. For system.query_log, do not generate SQL until references/system-query-log.md has been loaded via skill_resource, and do not call search_query_log for chart/time-series requests.

2. Syntax Rules (The Grammar)

  • Tables: ALWAYS use fully qualified names (e.g., database.table).
  • Semicolons: NEVER include a trailing semicolon (;).
  • Enums: Use exact string literals for Enum columns.
  • Safety: ALWAYS use LIMIT for data exploration queries.

3. Optimization Rules (Best Practices)

  • Time filters: Always filter by the partition key (usually event_date or timestamp) first. Use bounded time windows (e.g., last 24h, 7 days) unless the user asks for all history.
  • Primary Keys (CRITICAL): ClickHouse indexes are sparse. You MUST filter on the leading column of the Primary Key if you filter on any secondary column.
    • Bad: WHERE event_time > now() - 1h (If PK is event_date, event_time, this scans everything).
    • Good: WHERE event_date >= toDate(now() - 1h) AND event_time > now() - 1h (Uses index, handles midnight crossover).
  • Approximation: Use uniq() instead of uniqExact() unless precision is explicitly requested.
  • Joins: Put the smaller table on the RIGHT. Use GLOBAL IN only for distributed queries.

4. Execution Workflow

  1. Generate: Create the SQL following the rules above.
  2. Validate (MANDATORY): Call validate_sql(sql).
    • If invalid: Read the error, fix the SQL, and retry (max 3 attempts).
  3. Decide Action:
    • Visualization: IF the user wants a chart, DO NOT execute. Pass the SQL to the visualization skill logic.
    • Data: IF the user wants answers (lists, counts), call execute_sql(sql).
    • Code Only: IF the user asks to "write SQL", just output the code block.

Frequently asked questions

What does the Sql Expert AI skill do?

Expert system for generating, validating, and optimizing ClickHouse SQL. Use this when the user needs data, queries, or analysis.

Why use Sql Expert on TypingMind?

Because you install it once and use it with any model. Sql Expert 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 Sql Expert in TypingMind?

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

Which AI models can use Sql Expert?

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 Sql Expert?

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

Is the Sql Expert 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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