With these sorts of tasks models really seem to suffer from not knowing what packages or conventions have been deprecated. This is really obvious with an immature ecosystem like nix.

This is where custom setups will start to shine.

https://github.com/upstash/context7 - Pull version specific package documentation.

https://github.com/utensils/mcp-nixos - Similar to above but for nix (including version specific queries) with more sources.

https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking - Break down problems into multiple steps instead of trying to solve it all at once. Helps isolate important information per step so “the bigger picture” of the entire prompt doesn’t pollute the results. Sort of simulates reasoning. Instead of finding the best match for all keywords, it breaks the queries down to find the best matches per step and then assembles the final response.

https://github.com/CaviraOSS/OpenMemory - Long conversations tend to suffer as the working memory (context) fills up so it compresses and details are lost. With this (and many other similar tools) you can have it remember and recall things with or without a human in the loop to validate what’s stored. Great for complex planning or recalling of details. I essentially have a loop setup with global instructions to periodically emit reinforced codified instructions to a file (e.g., AGENTS.md) with human review. Combined with sequential thinking it will identify contradictions and prompt me to resolve any ambiguity.

The quality of the output is like going from 80% to damn near 100% as your knowledge base grows from external memory and codified instructions in files. I’m still lazy sometimes and will use something like Kagi assistant for a quick question or web search, but they have a pretty good baseline setup with sequential thinking in their online tooling.