"Formalizing AI workflows into reusable commands makes them more reliable and consistent than having to write a prompt every time we need help with something. When I run this command, it follows the same research steps in the same order, which means it's easier to find everything relevant and not forget about something.

The two-phase approach — research first, writing second — I think is also worth the extra step. Someone might want to just research a ticket and not write a draft. Also, this way the command works for other colleagues in the team who aren't tech writers.

MCP integrations are what make this a powerful tool. The ability to pull data from multiple internal tools in a single session is the foundation everything else is built on.

And as always: AI doesn't replace the tech writer's judgment. It replaces the tedious context-gathering so we can spend more time on the parts of the job that actually require expertise — deciding what customers need to know and how best to explain it.

If you want to try building something similar, OpenCode supports custom commands and agent skills. The setup takes a few minutes: define a command file with your research steps, create skill files with your docs framework conventions, and you have a reusable workflow. The OpenCode docs cover how to create commands and agent skills."

https://www.linkedin.com/pulse/building-ai-research-command-documentation-tickets-m%C3%A1rcio-florindo-mc4te/

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