So I naturally felt the need to (tell Claude to) build a MCP for this accounting API, and now I ask it to do accounting tasks, and then it just does them. It's really ducking sweet.
Another thing I did was, after a particularly grueling accounting month close out, I've told Claude to extract the general tasks that we accomplished, and build a skill that does it at the end of the month, and now it's like having a junior accountant in at my disposal - it just DOES the things a professional would charge me thousands for.
So both custom project MCPs and skills are super useful in my experience.
Claude and an mcp and skill is plain to me. Writing your own agent connecting to LLMs to try to be better than Claude code, using Ralph loops and so on is the rabbit hole.
(I'm genuinely asking)
skills that teach the agent how to pipe data, build requests, trace them through a system and datasources, then update code based on those results are a step function improvement in development.
ai has fundamentally changed how productive i am working on a 10m line codebase, and i'd guess less than 5% of that is due to code gen thats intended to go to prod. Nearly all of it is the ability to rapidly build tools and toolchains to test and verify what i'm doing.
What sort of skills are you referring to?
Skills are crazy useful to tell Claude how to debug your particular project, especially when you have a library of useful scripts for doing so.
1. I have many and sometimes contradictory workflows: exploration, prototyping, bug fixing debugging, feature work, pr management, etc. When I'm prototyping, I want reward hacking, I don't care about tests or lint's, and it's the exact opposite when I manage prs.
2. I see hard to explain and quantify problems with over configuration. The quality goes down, it loses track faster, it gets caught in loops. This is totally anecdotal, but I've seen it across a number of projects. My hypothesis is that is related to attention, specifically since these get added to the system prompt, they pull the distribution by constantly being attended to.
3. The models keep getting better. Similar to 2, sometime model gains are canceled out by previously necessary instructions. I hear the anthropic folks clear their claude.md every 30 days or so to alleviate this.
The reality is that if you actually know what you want, and can communicate it well (where the productivity app can be helpful), then you can do a lot with AI.
My experience is that most people don't actually know what they want. Or they don't understand what goes into what they want. Asking for a plan is a shortcut to gaining that understanding.
So, to really create something new that I care about, LLMs don't help much.
They are still useful for plenty of other tasks.
Working on an unspecified codebase of unknown size using unconfigured tooling with unstated goals found that less configuration worked better than more.
But for some projects there will be things Claude doesn’t know about, or things that you repeatedly want done a specific way and don’t want to type it in every prompt.