sql-patterns

sql-patterns is a skill for Claude Code from Methasit-Pun/data_engineer_claude_skills. It costs 137 tokens per session (1,565 once invoked), scanned A, original, no licence file.

A collection of SQL patterns for analytical work, such as examining large datasets and calculating results across groups of rows.

In plain words
What is it for?
It helps write or review window functions, common table expressions, partitioning strategies, and queries for BigQuery, Snowflake, Redshift, or DuckDB.
Why use it?
It helps avoid common query mistakes and improve queries that use advanced structures or need optimization.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the data-engineer-skills plugin — 28 skills shipped together

Good fit It helps write or review window functions, common table expressions, partitioning strategies, and queries for BigQuery, Snowflake, Redshift, or DuckDB.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/methasit-pun/data_engineer_claude_skills/sql-patterns
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Methasit-Pun/data_engineer_claude_skills --skill sql-patterns
Clone the repo
git clone --depth 1 https://github.com/Methasit-Pun/data_engineer_claude_skills

Made for: Claude Code.

Or install data-engineer-skills, the plugin that ships this one along with the rest of its 28 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for sql-patterns

README.md
[![agentmods](https://agentmods.dev/badge/skills/methasit-pun/data_engineer_claude_skills/sql-patterns/github.svg)](https://agentmods.dev/skills/methasit-pun/data_engineer_claude_skills/sql-patterns)
Your own site
<a href="https://agentmods.dev/skills/methasit-pun/data_engineer_claude_skills/sql-patterns"><img src="https://agentmods.dev/badge/skills/methasit-pun/data_engineer_claude_skills/sql-patterns/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for sql-patterns

Your own site · 80×15
<a href="https://agentmods.dev/skills/methasit-pun/data_engineer_claude_skills/sql-patterns"><img src="https://agentmods.dev/badge/skills/methasit-pun/data_engineer_claude_skills/sql-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 137 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,565 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00137 $0.01565
Opus 5.5 $0.00055 $0.00626
Sonnet 5.5 $0.00027 $0.00313
Haiku 4.5 $0.00014 $0.00156

Measured 1mo ago against content hash 1d608040e872, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sql-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

03-modeling/sql-patterns/SKILL.md · 187 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 187 lines · 137 tokens per session scan A 1d608040e872

Subscribe to this mod's changes

sql-patterns is a skill published in the GitHub repository Methasit-Pun/data_engineer_claude_skills (1 stars, last pushed 3mo ago), with no licence file. It adds 137 tokens to every session and 1,565 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

snowflake-dbt-model-refactoring

Refactor a bloated dbt model on Snowflake safely and produce a quantified before/after report that explains which qualities improved and why — not just raw deltas. Extends the standard refactor flow with (1) downstream impact analysis via dbt-analyze, (2) row-count + column-hash parity validation via data-parity, (3)…

AltimateAI/data-engineering-skills · 195 tokens

optimizing-databricks-sql

Analyze DBSQL queries, including SQL embedded in notebooks (spark.sql(...), %sql cells), for anti-patterns, lint issues, and performance problems, using Databricks-specific dialect and platform knowledge (Delta, Photon, Unity Catalog) layered on top of altimate-code's generic SQL engine. Use when a user asks to…

AltimateAI/data-engineering-skills · 98 tokens

developing-incremental-models

Develops and troubleshoots dbt incremental models. Use when working with incremental materialization for: (1) Creating new incremental models (choosing strategy, uniquekey, partition) (2) Task mentions "incremental", "append", "merge", "upsert", or "late arriving data" (3) Troubleshooting incremental failures (merge…

AltimateAI/data-engineering-skills · 112 tokens

creating-dbt-models

Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns…

AltimateAI/data-engineering-skills · 122 tokens

optimizing-query-by-id

Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake queryid (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance…

AltimateAI/data-engineering-skills · 121 tokens

migrating-sql-to-dbt

Converts legacy SQL to modular dbt models. Use when migrating SQL to dbt for: (1) Converting stored procedures, views, or raw SQL files to dbt models (2) Task mentions "migrate", "convert", "legacy SQL", "transform to dbt", or "modernize" (3) Breaking monolithic queries into modular layers (discovers project…

AltimateAI/data-engineering-skills · 121 tokens