moskills: giving coding agents the structure they lack
Agents drift when left alone. moskills is a small set of skills that add structure at the exact moments they tend to go wrong.
01 / the-problem: AI agents drift without structure
Coding agents are fast, and that is the problem. They make wrong assumptions, overcomplicate code, touch files they should not, and report work as done before it is verified. The speed that helps you ship also accelerates the mess.
The deeper issue is alignment. Before the agent writes a single line, there is rarely a shared understanding of what done looks like. No agreed goal. No criteria to judge whether the output will meet your expectations. No validation step at the end. The agent fills that gap with its own assumptions, and those assumptions are invisible to you until something breaks.
Give an agent a vague task and it will confidently build the wrong thing. Then it will tell you it is done.
We all kept hitting the same failure modes across projects. I stopped fixing them one chat at a time and packaged the fixes. The result is moskills: a small set of skills that give an agent structure at the exact moments it tends to go off the rails.
Vague request -> no shared criteria -> agent guesses -> builds wrong thing -> no validation -> you find out late
The most common failure in software is not bad code. It is misalignment between what you meant and what got built.
02 / what-it-is: set of skills, one install
moskills is a set of small, focused skills. Each one covers a specific moment in the work where agents usually slip.
-
align-intent: agree on goal, inputs, outputs, and non-goals before any code -
shared-language: a glossary so humans and the agent use the same words -
system-map: map modules and the safest change boundary before editing -
tdd: a red, green, refactor loop for behavior changes -
diagnose: reproduce and find root cause before fixing -
checkpoint: record real progress, Git deltas, and next steps -
gatekeeper: check the result matches what was asked before saying done -
compress-input: switch the agent to short, direct communication -
memorize: remembers lessons and decisions between sessions -
handoff: summarize what happened so another agent can take over
The skills are small on purpose. Heavy frameworks take over your process and make their own bugs hard to fix. Small skills stay easy to read, adapt, and compose.
03 / how-it-triggers: The agent picks the skill, not you
The interesting part is not the slash commands. It is that you rarely need them. Every skill carries a short description with a Use when: trigger line.
---
name: diagnose
description: "Use when: debugging bugs, failing tests, broken builds, regressions, performance issues, or unexpected behavior."
---
The agent reads those descriptions and matches them against what you are asking for. Say "the build is broken" and the diagnose skill kicks in on its own. The slash command is still there when you want to be explicit, but most of the time you do not need it.
A precise trigger line is worth more than a clever command name. The description is what makes a skill discoverable.
04 / install: One line, central updates
moskills installs as a Claude Code plugin, so the skills are available in every project and update in one place.
/plugin marketplace add Mouad1/moskills
/plugin install moskills@moskills
Update everywhere later with a single command:
/plugin update moskills@moskills
No files are copied into each project, and one update reaches all of them. There is also a copy script for people who want the files committed directly into a single repo.
05 / takeaway: Speed needs structure
Agents are not going to slow down, so the answer is structure, not caution. Align before coding, keep a shared language, map before you cut, check before you say done. moskills puts those habits in place at the moments that matter, then gets out of the way.
If you build with coding agents, try it on your next feature and watch where the agent would have guessed. That gap is exactly what these skills close.
These skills are not invented from scratch. They are the result of experimentation across real projects and feedback from people in the community who work with agents every day. What is here reflects what actually worked, not what looked good in theory.
The pack is open source and free. Find the code at github.com/Mouad1/moskills. More articles on AI and how to work better with it are at belghitis.com.