I also have always admired the old unix tools after learning how to use them. When working with big data files, it is super fast to get going by using cat, grep, sed, awk, cut and wc. As an alternative to using some editor and manually doing searches over data, the commands bring huge performance gain.
I do software development and have to also fill some tedious forms now and then. I have a good experience of these three things to boost my productivity:
Shell scripts and Python based automations are good to perform repetitive tasks. Python 3 is everywhere and easily installable, which makes it a good base for writing automations.
Cron helps ensuring the automations are executed on time. This is nice for some backup activities.
Automator (on OSX) is a good tool to connect key events to your automations.
"Say" (the shell command to perform text-to-speech synthesis) is nice way to stay up-to-date on the executions.
I also utilize macro capabilities of editors when working with larger data files.
The trend is that the existing tools are continuously being made more and more powerful which also reduces the need for extra scripting on top of them.
Finally, I'd like to mention that the company I work for, Softagram, https://softagram.com is also offering a tool to boost productivity when working with larger or more risky code changes. The tool automates dependency/risk analysis and offers the output in nicely integrated visual report right to the comment field of pull request in GitHub, GitLab, etc. systems.
I'll share some background here to make it more approachable. (as the founder and the first developer of the product)
Visualization of the changes is based on a webhook triggered code analysis, comparison and visualization pipeline. The idea is to make pull request more reviewable by showing the context of the change in just few seconds. Also any new dependencies (that might screw your current design) are shown. It supports multiple repositories making it suitable for large projects.
With last one year we have had major transformation to better integrate Softagram into pull requests (of Azure, Github and Bitbucket) and merge requests (of GitLab). This is to make it easily utilized within the current developer workflows.
The major challenges is to make pull request code change visualization very understandable while keeping it also sufficiently detailed to have it useful. There is an additional detailed dependency visualization that will pop up below the main visualization if your code changes are larger.
We would really love to get feedback on if it helps you and if it provides the report fast enough, and generally if this approach is something that you like or not.
Run the code through in Softagram analyzer and start digging into the structures using the visual browsing capabilities in Softagram Desktop app. There is free trial at softagram.com where you can do it easily if you happen to have your codes in some Git repo in cloud services such as Bitbucket or GitHub... Shameless self promotion this is however, as I work for the company. But that approach I have also been personally using more than 10 years: static dependency analysis coupled with excellent visual dependency browsing. I think some expensive version of Visual Studio has also similar stuff available.