Off course! I mainly contribute to tensorflow/docs-l10n 10 and made some contribution to other repos (tensorflow/docs 12, tensorflow/tfx 13, and tensorflow/tensorboard 11) on almost same workflow. Followings are my workflow.
Find an issue

Run example notebooks in Colab (I mostly use them for my learning)
Find unexpected errors
(tensorflow/docs-l10n only) Find untranslated notebooks or outdated notebooks
Search issues of the repo to check the issue is already reported or not
Try to find the cause of the issue to understand it enough to describe

Report an issue

Check README.md and CONTRIBUTING.md to find the way to report the issue
Take a look at issue templates to find the most suitable one (each repo can have its own rules and templates)
Fill the issue template

Fix the issue

Fork the repo.
Set up my local development environment following instructions in README.md, CONTRIBUTING.md, setup.py, and other resources (if required, I mostly use Colab because )
Fix the issue and push it into my cloned repo.
Clone it into my local environment.
Apply formatter and linter using nbfmt and nblint following this document 6

Push fix commits to my cloned repo

Make a pull request

Make a pull request and if I created the issue, link the PR to the issue
Pray to pass the CI  (and fix my PR if needed)
Pray to merge it 


I think this is a generic workflow to contribute to OSS hosted on GitHub. I guess, for tensorflow/docs-l10n, this PR-based workflow will soon change into gitlocalize based workflow.