LLM Coding Rules

    1

    Enforce surgical edits and prevent LLM over-engineering with a strict behavioral coding contract.

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    LLM Coding Rules

    Example session with this skill installed

    Follow the LLM Coding Rules: Add a 'status' field to the User struct in models.go, default to 'pending'.

    • Read your context and instructions
    • Compiled the llm coding rules

    I will add a 'status' field to the User struct.

    Assumptions:

    1. The 'status' field should be a string to allow for future states (active, suspended).
    2. Existing JSON tags should be followed for consistency.

    Success Criteria:

    • models.go compiles successfully.
    • The User struct contains Status string json:"status"``.
    • New User instances default to "pending" in the constructor/factory function if one exists.

    Code Update

    // internal/models/user.go

package models

type User struct {
	ID        int64     `json:"id"`
	Email     string    `json:"email"`
	Password  string    `json:"-"`
+	Status    string    `json:"status"`
	CreatedAt time.Time `json:"created_at"`
}

func NewUser(email, password string) *User {
	return &User{
		Email:    email,
		Password: password,
+		Status:   "pending",
	}
}

    Changes:

    • Modified internal/models/user.go: Added Status field to User struct and initialized it in NewUser.

    Trade-off:
    Using a string instead of an iota/enum increases memory slightly but avoids manual string mapping for JSON responses.

    Next steps:

    1. Run go build ./internal/models/... to verify compilation.
    2. Update the database schema migration to include the new column.

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    What you get

    Minimize code churn by forcing surgical line-by-line edits.Eliminate speculative generality and unrequested abstractions.Verify changes against explicit success criteria before completion.Ensure trade-offs are surfaced before the model alters interfaces.

    About this skill

    The problem

    LLMs often over-engineer simple fixes, rewriting entire files and introducing breaking changes or unnecessary boilerplate just to update a single line of logic.

    What it does

    • Enforces surgical edits to minimize code churn and accidental side effects.
    • Forces the model to state assumptions and trade-offs before modifying files.
    • Requires the definition of verifiable success criteria (tests or diffs) prior to execution.
    • Prevents speculative generality by blocking unrequested abstractions and helpers.

    Why this beats prompting it yourself

    Standard prompts fail to prevent the "helpful" LLM from refactoring working code or adding unasked-for features. These rules act as a strict behavioral contract that keeps the model focused on the specific task, reducing review time and preventing architectural drift.

    Use cases

    • Quick bug fixes where you need to preserve existing file formatting.
    • Feature additions in large, sensitive codebases where side effects are costly.
    • Collaborative coding sessions where you need predictable, verifiable model outputs.
    • Refactoring tasks that require explicit trade-off analysis before implementation.

    Known limitations

    Requires an agent with file-reading capabilities. Not suitable for tasks where a complete architectural overhaul is the primary goal.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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      Ask your agent to use it

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