moyu-fr
by uuczmoyu-fr is a code-editing guardrail skill for small, surgical changes. It helps agents avoid overengineering, keep diffs minimal, and focus on the exact edit requested. Use moyu-fr when you want a simple patch, not a rewrite, and when tight scope control matters more than expansion.
This skill scores 67/100, which is acceptable for directory listing. It gives users a clear anti-overengineering trigger and enough operational intent to be useful, but the repo lacks supporting assets and deeper workflow guidance, so adopters should expect a narrow, opinionated behavior rather than a broadly documented utility.
- Explicit auto-trigger rules for overengineering patterns make it easy for an agent to know when to apply the skill.
- Substantial SKILL.md content with headings, examples, and repo/file references suggests real operational guidance rather than a placeholder.
- The focus is narrow and actionable: minimize unnecessary changes, abstractions, dependencies, and test/doc scaffolding.
- No scripts, references, resources, or install command are provided, so users have little implementation support beyond the prompt text.
- The description is terse and heavily policy-based, so it may fit only code-editing situations where restraint is the primary goal.
Overview of moyu-fr skill
moyu-fr is a code-editing guardrail skill for agents that tend to overbuild. It is designed for users who want the smallest correct change, especially when a request can be solved by editing only the asked-for lines instead of rewriting files, adding abstractions, or “improving” the code beyond scope.
What moyu-fr is for
The moyu-fr skill is best for patch-style work: surgical fixes, narrow refactors, one-off bug repairs, and responses to users who explicitly ask for “simple,” “minimal,” or “only change X.” It helps keep an agent aligned with the real job-to-be-done: make the requested change and stop.
Why this skill is different
Unlike a generic prompt, moyu-fr encodes explicit anti-overengineering rules. It discourages new layers, extra dependencies, defensive code for impossible cases, and unrelated documentation or tests. That makes it useful when output quality is measured by restraint, not breadth.
Best-fit users and misfit cases
Use moyu-fr for Code Editing when a repo or task is sensitive to diff size, architecture drift, or unnecessary cleanup. It is a weaker fit if you actually want redesign, feature expansion, test creation, or a broad codebase modernization, because the skill is optimized to minimize change.
How to Use moyu-fr skill
Install it and confirm the activation path
Use the repository’s skill install flow for moyu-fr, then confirm the agent can detect the skill from your editing request. The moyu-fr install decision is strongest when your workflow already includes strict scope control and you want the skill to act automatically on overengineering signals.
Give it a narrow edit brief
The best moyu-fr usage pattern is a precise request with boundaries. Good inputs name the target file, the exact behavior change, and what must not change.
Example of a strong brief:
- “Edit
src/auth.tsto fix the null check invalidateSession; do not change error handling or add helpers.” - “Update only the regex in
README.mdexample; keep the rest of the snippet unchanged.”
Weak inputs are broad:
- “Improve the auth flow”
- “Clean up this module”
- “Make it more robust”
Those prompts invite expansion, which the skill is specifically trying to prevent.
Read the right files first
For a practical moyu-fr guide, start with SKILL.md, then inspect any linked repo notes or task-specific files the repository mentions. In this skill’s current shape, the main signal is the skill body itself, so the most useful reading path is the instructions, the repo tree, and the immediate files you plan to edit. If you cannot state the exact target and the minimum acceptable diff, the prompt is still too vague.
Use it as a restraint filter, not a rewrite engine
The skill works best when you already know the desired outcome and want to block excess. It should shape the agent’s edits toward:
- minimal line changes
- no new abstractions unless requested
- no extra docs, tests, or comments unless asked
- no file rewrites when a patch is enough
moyu-fr skill FAQ
Is moyu-fr only for Code Editing?
Yes, the strongest moyu-fr fit is code editing with tight scope control. It is less useful for open-ended generation, architecture planning, or tasks where extra scaffolding is expected.
How is it different from a normal prompt?
A normal prompt can say “keep it simple,” but moyu-fr turns that preference into a stronger operating rule set. That matters when the agent is tempted to solve adjacent problems, rewrite surrounding code, or add polish that was never requested.
Is moyu-fr beginner-friendly?
Yes, if the user can describe a single concrete change. It is easiest to use when you can name the file, the symptom, and the exact boundary of the edit. Beginners struggle mostly when the request is too broad, not because the skill itself is hard.
When should I not use moyu-fr?
Do not use it when you actually need refactoring, test coverage, new abstractions, or system-hardening work. In those cases, the skill’s restraint may fight the real goal and produce an output that is technically minimal but incomplete.
How to Improve moyu-fr skill
Give tighter constraints up front
The best way to improve moyu-fr is to state what must stay unchanged. Include file names, target lines, and exclusions such as “no new functions,” “no extra comments,” or “do not touch formatting outside this block.” These constraints reduce guesswork and make the skill’s minimal-edit behavior more reliable.
Ask for the smallest acceptable diff
If you want a better moyu-fr usage result, describe success in diff terms:
- “Change only one branch condition.”
- “Patch the existing function instead of creating a helper.”
- “Preserve the current public API.”
This tells the agent what “done” looks like and prevents scope creep.
Watch for common failure modes
The main failure mode is accidental expansion: extra validation, extra explanation, or cleanup that feels helpful but changes more than requested. Another common issue is under-editing when the request truly requires multiple small changes; in that case, state the full set of required edits explicitly so the skill does not stop too early.
Iterate with narrower follow-ups
If the first result is still too broad, tighten the next instruction instead of restating the same request. For example: “Keep the same logic and only adjust this condition” is more actionable than “make it smaller.” For moyu-fr, better inputs usually mean fewer degrees of freedom, not more context.