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.
npx agentmods add skills/adityawrk/analytics-with-claude-code/explain-sqlnpx skills add adityawrk/analytics-with-claude-code --skill explain-sqlgit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWhat 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 | $0.00072 | $0.04480 |
| Opus 5 | $0.00036 | $0.02240 |
| Sonnet 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 2d 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.
- 2d 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 (5 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 4,480 once invoked, about $0.0004 per session on Opus 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.
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