awesome-readme VS sqlfluff

About user-statistician

The user-statistician GitHub Action can generate an SVG with a detailed summary of your activity on GitHub. It is mentioned in the tools section of the awesome README awesome list. The SVG it generates includes general information about you (e.g., year you joined, number of followers, number you are following, most starred repository, etc), information about your repositories (e.g., numbers of stars and forks, etc), information about your contributions (e.g., numbers of commits, issues, PRs, etc), and the distribution of languages within your public repositories.

If you really want a stellar README.md take a look at some of the examples in awesome-readme for inspiration!

Awesome README - A collection of high-quality READMEs from a variety of projects, organized by topic. https://github.com/matiassingers/awesome-readme

Provide a succinct and comprehensive README: readers of your personal project will always start with the README to know where to begin. The goal of the README is to provide the reader an understanding of the business problem you are trying to solve, how your solution goes about solving it (solution architecture diagram), and how to get started and run your code. There are plenty of great README examples here: https://github.com/matiassingers/awesome-readme

About user-statistician

Recently, the user-statistician GitHub Action was added to the tools section of Awesome README, which is an Awesome List that includes a curated collection of examples of Awesome READMEs from open source projects, as well as tools enabling creating Awesome READMEs. The Awesome README list is a great place to go if you are looking for ideas for how to improve the READMEs of your open source projects. The Awesome README list covers READMEs more generally, but the tools section includes a few tools focused on Profile READMEs, in addition to many tools for project READMEs more generally. The user-statistician GitHub Action is in the Tools Section.

Repo : https://github.com/sqlfluff/sqlfluff

I'm surprised sqlfluff hasn't been mentioned yet. Perhaps not a comprehensive list, but it's worked for everything I've thrown at it. There's an ANSI keyword list [0], and then dialect-specific lists for everything from DB2 [1] to Snowflake [2].

[0]: https://github.com/sqlfluff/sqlfluff/blob/main/src/sqlfluff/...

It has tons of annoying quirks, but I couldn't imagine running a DBT project without it: https://github.com/sqlfluff/sqlfluff

Move SQL queries to sql files and read from files (Use sqlfluff to lint the code https://github.com/sqlfluff/sqlfluff)

SQLFluff is a good CLI tool for this and includes support for jinja and dbt. I don't think there's a VSCode plugin for it yet.

This linter can really enforce some best practices https://github.com/sqlfluff/sqlfluff

A list of best practices:

BigQuery SQL and SQLFluff

There's a SQL parser/linter called SQLFluff that my team uses for our CI/CD. I've made a few pull requests to fix the parser for the particular SQL dialect we used, and my college compiler classes definitely helped.