How to Use SKILL.md, AGENTS.md, and CLAUDE.md with Memside
By Sravanth Thota
If you use AI for coding, you may already have instruction files in your projects. Files like `SKILL.md`, `AGENTS.md`, and `CLAUDE.md` tell an AI assistant how to work with a repo, what rules to follow, and what mistakes to avoid.
These files are useful, but they usually live in one place. Memside lets you save them as reusable AI Skills inside your memory hub, so the same instructions can be used across different AI tools without pasting the full file into every chat.
What these files are for
`SKILL.md`, `AGENTS.md`, and `CLAUDE.md` are plain Markdown files that describe how an AI should behave when working on a project or task. They may include rules like reading files before editing, making small changes, preserving existing behavior, running tests, avoiding secrets, and not committing unless asked. In a coding project, that kind of guidance can prevent a lot of unnecessary cleanup later. When the rules are written clearly, the AI has a better chance of following the same working style each time.
Why saving them in Memside helps
Keeping these files inside a repo is useful, and you should continue doing that if your tools read them there. The problem is that your work does not always happen inside that repo.
You may switch from one AI app to another, work from another device, or ask for help before the repo is open. You may also have personal instructions, like your review style or writing tone, that do not belong in every project folder. When you save these files as AI Skills in Memside, they become portable. You can keep the original file, reuse it from connected AI tools, and choose when the AI should read a shorter version. That makes them useful for AI continuity across chats, devices, and coding assistants.
Setting up an AI Skill in Memside
The setup is simple. Open Memside, go to the AI Skills area, and create a new skill. Paste the contents of your `SKILL.md`, `AGENTS.md`, `CLAUDE.md`, or another instruction file.
Give the skill a clear name and a short description. Practical names work best, such as Backend Safety Rules, Blog Writing Style, or Release Notes Checklist. After saving, Memside keeps the original source. That matters because the original file is your full instruction record, even if you later use a shorter version for day-to-day AI work.
If you have Memside connected to your AI tool, you can skip the manual steps. Share the file contents in the chat and ask the AI to save it as a skill in Memside. The AI will handle the setup on your behalf, including naming it and preserving the original source.
Compact, detailed, and original versions
Long instruction files can be useful, but the AI does not need to read the full file for every small task. Repeated long rules take up context that could have been used for the actual work. Memside lets you keep the original source while also using optimized versions, and the three useful choices are compact, detailed, and original.
Compact is best when the AI needs the main rules but not every detail. It works well for small fixes, quick reviews, common writing tasks, and repeated instructions the AI already understands. Use it when you want speed and lower token use.
Detailed is better when the AI needs more context, including workflow order, edge cases, examples, and extra safety instructions that may be missing from the compact version. Use it for code reviews, larger changes, security-sensitive work, or any task where missing a rule could cause rework.
Original is the full file you imported or wrote. Use it when exact wording matters, or when the instruction file is strict enough that summarizing may remove something important. This is the right choice for detailed `AGENTS.md` files, compliance instructions, or a full `SKILL.md` that contains examples the AI should not miss.
How this saves tokens
Every AI chat has a context limit, and every instruction you send uses part of that context. If the AI reads a long instruction file for every small task, you spend context on repeated setup instead of the actual work. Using compact and detailed versions reduces that waste. The AI still gets the guidance it needs, but it does not have to process the full file every time. Across many chats and AI workflows, that adds up to real token savings and keeps context transfer lighter.
This is also helpful when switching between AI tools. Instead of pasting the same Markdown file into ChatGPT, Claude, Codex, Cursor, or another assistant, you can keep the skill in Memside and let your connected tools load it when needed.
When the file is not in the repo
Sometimes the most useful instruction is not inside the current repo. You may be reviewing an idea, writing documentation, helping with a project from a different machine, or working before the codebase is open.
Memside helps here because your skill lives outside the repo. You can still ask the AI to use your saved code review skill before reviewing a change, or use your saved writing skill before drafting an article. This makes instruction files useful beyond one folder and beyond one project, which is the practical side of AI continuity in day-to-day work.
A simple workflow
Start with one instruction file. Do not import every rule file you have on the first day, and do not spend time organizing before you have used any of them.
A simple workflow looks like this:
Pick one useful file, such as `AGENTS.md`.
Save it as an AI Skill in Memside.
Keep the original source preserved.
Review the compact and detailed versions.
Use compact for normal tasks.
Use detailed or original when the task needs more care.
After using it a few times, improve the skill. Remove unclear rules, add missing safety checks, and keep the wording easy for AI to follow.
Reusable instructions, less repeated setup
The main benefit is that your instructions become reusable. You do not need to hunt for the file, paste it into a chat, or rewrite the same rules in every tool. Memside does not replace your repo files. You can still keep `SKILL.md`, `AGENTS.md`, and `CLAUDE.md` where they belong in your projects. It simply gives you a portable copy and optimized versions that are easier to use wherever your AI work happens.