Sql anti pattern linter

Skill sisodiabhumca/agent-skills/skills/sql-anti-pattern-linter

Production-Ready Agent Skills : product analytics, growth experiments, CRM, research synthesis, postmortems, data contracts, SaaS spend, compliance, architecture maps, and LLM eval and many more.

Install

npx -y skills add sisodiabhumca/agent-skills --skill sql-anti-pattern-linter

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Vendor-neutral skill to lint SQL text for common anti-patterns and output actionable findings.

SKILL.md

1.1 KB, 267 tokens by cl100k_base, as published. Nobody here has run it

When to invoke

You want a lightweight, fast static check on SQL before review.
You suspect performance issues from patterns like SELECT * or non-sargable filters.

Inputs needed

--sql_file: Path to a .sql file.
--out_json: Path to write findings as JSON.

Workflow

Read SQL text.
Run heuristic rule checks (regex-based):
- SELECT *
- WHERE clause using functions on indexed-looking columns (e.g., LOWER(col)=...)
- Leading wildcard LIKE '%foo'
- OR chains on same column
- Implicit joins via comma in FROM a, b
- NOT IN (subquery) null-trap risk
- Missing WHERE on UPDATE/DELETE
Emit findings with rule id, severity, message, and line number when detectable.

Output format

JSON with:

file
findings[]: {rule_id, severity, message, line}

Guardrails

Not a full SQL parser; results are best-effort.
Do not auto-rewrite queries without human review.

Reference code

sql_anti_pattern_linter.py

What ships with it: 2 files

3.7 KB alongside SKILL.md, 1 of them executable

README.md280 B
sql_anti_pattern_linter.pyruns3.4 KB

Gives 0 of the 12 instructions most quality gates skills give in 267 tokens

Counted across 1,195 of the 2,094 authors here whose files we hold, read 2026-08-07

Read the output and check the exit codein 54 of 1195, across 14 files
Verify requirements using a line-by-line checklistin 53 of 1195, across 12 files
Identify the verification command proving the claimin 51 of 1195, across 12 files
Run the full verification commandin 50 of 1195, across 11 files
Verify output confirms the claimin 49 of 1195, across 12 files
Check version control diff after agent delegationin 46 of 1195, across 6 files
State claim with evidencein 44 of 1195, across 4 files
Run the test suitein 33 of 1195, across 26 files
Keep state in memory by defaultin 27 of 1195, across 6 files
Make prototype runnable with one commandin 26 of 1195, across 5 files
Produce a verification reportin 25 of 1195, across 14 files
Detect the package manager from lockfilesin 24 of 1195, across 5 files

Said here and by no other author read

read sql text
run heuristic rule checks
emit findings with rule id
emit findings with severity
emit findings with message
write findings as json

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.