Borrowing it
Nothing to install: this file belongs to adityawrk/analytics-with-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/adityawrk/analytics-with-claude-code/main/.claude/skills/explain-sql/SKILL.mdgit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWrote 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.
[](https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/explain-sql)<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/explain-sql"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/explain-sql/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.
<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/explain-sql"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/explain-sql.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.04480 |
| Opus 5.5 | $0.00029 | $0.01792 |
| Sonnet 5.5 | $0.00014 | $0.00896 |
| Haiku 4.5 | $0.00007 | $0.00448 |
Grade A, and why
explain-sql 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Explained
You are a senior analytics engineer and SQL expert. When given a SQL query, you will produce a comprehensive, human-readable explanation that makes the query understandable to anyone on the team — from junior analysts to principal engineers. Follow every step below.
Step 0: Acquire the Query
Determine the SQL query to explain from one of these sources (in priority order):
- Inline paste: The user pastes a query directly after invoking this skill.
- File path: The user provides a path like
models/marts/fct_orders.sqlorqueries/revenue.sql. Read the file contents. - dbt model reference: The user provides a dbt model name like
fct_ordersorstg_payments. Search for the corresponding.sqlfile undermodels/directories using Glob patterns like**/fct_orders.sql. - Interactive: If none of the above, ask the user to provide the query.
If the file is a dbt model (contains {{ ref( or {{ source( or Jinja templating), note this and handle the dbt-specific analysis in Step 7.
Step 1: Dialect Detection
Auto-detect the SQL dialect from syntax clues. Check for these markers:
| Dialect | Identifying Syntax |
|---|---|
| PostgreSQL | ::type casts, ILIKE, LATERAL, GENERATE_SERIES, RETURNING, ON CONFLICT |
| MySQL | backtick identifiers, LIMIT x, y syntax, IFNULL, GROUP_CONCAT, AUTO_INCREMENT |
| BigQuery | UNNEST, STRUCT, ARRAY_AGG, SAFE_DIVIDE, backtick project.dataset.table, EXCEPT(), DATE_DIFF(..., ..., DAY) |
| Snowflake | FLATTEN, LATERAL FLATTEN, TRY_CAST, OBJECT_CONSTRUCT, QUALIFY, $$ blocks, MATCH_RECOGNIZE |
| DuckDB | EXCLUDE, REPLACE, COLUMNS(*), read_parquet(), read_csv_auto(), PIVOT/UNPIVOT inline |
| SQL Server | TOP N, CROSS APPLY, OUTER APPLY, NOLOCK, @@ROWCOUNT, ISNULL(), + for string concat |
| Redshift | DISTKEY, SORTKEY, DISTSTYLE, UNLOAD, COPY, GETDATE() |
| Standard SQL | None of the above markers detected |
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.
- 2mo ago First seen · 390 lines · 72 tokens per session scan A f2c96bcf2070
explain-sql is a skill published in the GitHub repository adityawrk/analytics-with-claude-code (6 stars, last pushed 8mo ago), licensed MIT. It adds 72 tokens to every session and 4,480 once invoked, about $0.0003 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.
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)…
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…
mssql-to-snowflake-dbt-migration
Convert MS SQL Server source code (stored procs, views, T-SQL scripts) into dbt models targeting Snowflake, with build-first-then-parity, isolated-schema deployment, and a migration dashboard. Use when: (1) User says "migrate this T-SQL to dbt on Snowflake", "convert these stored procs end-to-end", "move from SQL…
end-to-end-snowflake-pipeline
Build an end-to-end Snowflake pipeline — ticket → dbt models + orchestration DAG → PR → self-review → ticket comment. Use when: (1) User says "build a pipeline for X end-to-end", "create an end-to-end flow", "set up the pipeline per JIRA-123" (2) Scope spans dbt models AND orchestration AND a PR — not just one of them…
snowflake-query-optimization
End-to-end Snowflake query optimization — fetch, rewrite, prove parity, benchmark, report. Use when: (1) User wants an end-to-end flow (fetch + rewrite + parity + benchmark + report), not just a rewrite (2) Input is a .sql file path in the repo (optionally :L10-L80 line range), OR a Snowflake queryid (UUID) (3) User…
dbt-model-enhancement
Scoped enhancement of ONE existing dbt model from a ticket, with a validated PR as the deliverable. Use when: (1) User says "update per JIRA-123", "add column X to this model", "enhance based on this ticket" (2) Task is a scoped change to a specific existing model — one model, one ticket, one PR (3) User expects dbt…