I've found really hammering it with *important*, all caps, "NEVER", etc finally made it start using the tidewave MCP for elixir development well. It felt really heavy handed but it worked.

For an idea of how heavy handed it was, this is my claude.md (with some explanatory text before): https://gist.github.com/bontaq/77b56d90b30e29c84c53c86d7fe05...

This is just how the attention mechanism works.

(search for effective context problem for more info. e.g. https://arxiv.org/abs/2509.21361)

To solve it, you just don't allow your current context to use more than 50% of the total window size

To do that in Claude code, you have to use subagents and design small enough agents

Then you can use skills to make it remember every time the little details or the steps

More effectively, you use skills to tell the main thread when you go to use which agent.

If you don't understand anything I said, try to restate the important things to the model periodically, and keep your tasks small.

Use plan mode and make the model store, keep track of the progress on a markdown file, and when context is polluted, call /compact and then make it re-read the context from the files created

You can prompt it as simply as:

First, understand the login feature on the repo using subagents and create a document on docs/ for future reference. Then, understand the task at hand and create an implementation plan. <task> blah blah </task>

Also, using XML tags makes the attention remember easily

Are agents still the way to go or have skills supplanted them? I don't really understand when you'd use one or the other

It's better if you think that the only thing that is really there is a context window.

They can add complex concepts and tools on top, but all that is is a different way to put things in the context window. Even the chat history on the web... You are not sending a message every time... It's not really a chat... the model is writing what it predicts will come next, like autocompleting a Word document that is written in a chat-like format.

So agents are like you, opening a new window and having the chat there, so you don't pollute the current window with all the tokens needed to process that question, and to use only the output here.

This is important bc of the effective context window problem. Models are more accurate the smaller the context is.

Hence, MCP tools are problematic. If you have registered many of them, the rules for using each one are added to your context, even if you don't use them.

Having a very extensive Claude.md file is also problematic.

You can use skills to instruct the model on which agents to use when requesting a specific thing. Antrophic says they have trained the model to discover on its own when to read the skill and follow the instructions you picture there, which can include Python scripts to run.

So yeah, agents help the model save context window for your current problem, skills help the model follow your instructions better, and instructions can include agent calling, and MCP is crap, you'd better ask the model to generate code to make that call

Oh, there are also slash commands. I don't really use them... if someone has a success story for them, I would love to know about it.

They're completely orthogonal features.

Skills are just reusable prompts in a convenient package.

Subagents get their own pristine context window to go off and perform some task. They can also run skills and do lots of context-heavy work and report back some small sliver of it to the main agent as a report.

Skills are more than just reusable prompts, since they can be packaged alongside with runnable Python or Node scripts that the model can use to achieve what it needs.

Not just Python and Node. Package anything you want with them, that's what makes them convenient.

It seems to me that skills are the same as projects on the web interface, but with executable files