Finding Expensive Queries

Organization

AltimateAI

finding-expensive-queries

Finds and ranks expensive Snowflake queries by cost, time, or data scanned. Use when: (1) User asks to find slow, expensive, or problematic queries (2) Task mentions "query history", "top queries", "most expensive", or "slowest queries" (3) Analyzing warehouse costs or identifying optimization candidates (4) Finding queries that scan the most data or have the most spillage Returns ranked list of queries with metrics and optimization recommendations.

Overview

Publisher	AltimateAI
Repository	`data-engineering-skills`
Skill name	`finding-expensive-queries`
Stars	133
Forks	12
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 AltimateAI on GitHub. Read the source before you install it.

Installation

Install the Finding Expensive Queries AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/snowflake/finding-expensive-queries
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/snowflake/finding-expensive-queries

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/AltimateAI/data-engineering-skills.git /tmp/data-engineering-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-skills/skills/snowflake/finding-expensive-queries .claude/skills/finding-expensive-queries

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Expensive Queries 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 Finding Expensive Queries 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 Finding Expensive Queries 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.

Finding Expensive Queries

Query history → Rank by metric → Identify patterns → Recommend optimizations

Workflow

1. Ask What to Optimize For

Before querying, clarify:

Time period? (last day, week, month)
Metric? (execution time, bytes scanned, cost, spillage)
Warehouse? (specific or all)
User? (specific or all)

2. Find Expensive Queries by Cost

Use QUERY_ATTRIBUTION_HISTORY for credit/cost analysis:

sql
SELECT
    query_id,
    warehouse_name,
    user_name,
    credits_attributed_compute,
    start_time,
    end_time,
    query_tag
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_ATTRIBUTION_HISTORY
WHERE start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP())
ORDER BY credits_attributed_compute DESC
LIMIT 20;

3. Get Performance Stats for Specific Queries

Use QUERY_HISTORY for detailed performance metrics (run separately, not joined):

sql
SELECT
    query_id,
    query_text,
    total_elapsed_time/1000 as seconds,
    bytes_scanned/1e9 as gb_scanned,
    bytes_spilled_to_local_storage/1e9 as gb_spilled_local,
    bytes_spilled_to_remote_storage/1e9 as gb_spilled_remote,
    partitions_scanned,
    partitions_total
FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY
WHERE query_id IN ('<query_id_1>', '<query_id_2>', ...)
  AND start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP());

4. Identify Patterns

Look for:

High credits_attributed_compute queries
Same query_hash repeated (caching opportunity)
partitions_scanned = partitions_total (no pruning)
High gb_spilled (memory pressure)

5. Return Results

Provide:

Ranked list of expensive queries with key metrics
Common patterns identified
Top 3-5 optimization recommendations
Specific queries to investigate further

Common Filters

sql
-- Time range (required)
WHERE start_time >= DATEADD('days', -7, CURRENT_TIMESTAMP())

-- By warehouse
AND warehouse_name = 'ANALYTICS_WH'

-- By user
AND user_name = 'ETL_USER'

-- Only queries over cost threshold
AND credits_attributed_compute > 0.01

-- Only queries over time threshold
AND total_elapsed_time > 60000  -- over 1 minute

Frequently asked questions

What does the Finding Expensive Queries AI skill do?

Why use Finding Expensive Queries on TypingMind?

Because you install it once and use it with any model. Finding Expensive Queries 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 Finding Expensive Queries in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AltimateAI/data-engineering-skills/tree/main/skills/snowflake/finding-expensive-queries. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finding Expensive Queries?

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 Finding Expensive Queries?

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

Is the Finding Expensive Queries AI skill free?

Yes. It is published on GitHub by AltimateAI under the MIT 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

Huggingface Hub

NousResearch

OrganizationPopular

HuggingFace hf CLI: search/download/upload models, datasets.

Pytorch Lightning

Clean training loops with built-in distributed support.

Troubleshoot

Investigate unexpected behavior in the current Copilot agent session by analyzing its event log. Use when the user asks why something happened, why a request was slow, why a tool was or was not used,…

Pdf

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart,…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Finding Expensive Queries

Workflow

1. Ask What to Optimize For

2. Find Expensive Queries by Cost

3. Get Performance Stats for Specific Queries

4. Identify Patterns

5. Return Results

Common Filters

Frequently asked questions

Related AI Skills

Set up your own AI workspace now