Codebase Prompt Enhancer: How I Rewrite Coding Prompts to Fit the Repository Better

When I work with AI coding agents, I keep running into the same pattern: the original prompt is often not wrong, but it is still too broad. Because of that, the agent may read the wrong files, drift out of scope, or suggest something that sounds reasonable but does not really fit the actual repository.

That is the reason I built codebase-prompt-enhancer. The idea is simple: before rewriting a coding prompt, inspect the relevant codebase first. Once the real repository context is clear, the prompt can be rewritten in a way that is more precise, more grounded, and easier for another coding agent to execute well.

Why I made this skill

At first, I noticed I was repeating the same workflow over and over. A user would give a short coding prompt, then I would:

• inspect the repo
• identify the real files involved
• figure out the local architecture pattern
• narrow the scope
• rewrite the prompt so it was actually usable

After doing that many times, it felt more natural to turn the process into a dedicated skill instead of repeating it manually.

What Codebase Prompt Enhancer does

The workflow is intentionally straightforward:

1. read the original prompt
2. extract the goal, scope, constraints, and expected output
3. inspect only the relevant parts of the repository
4. infer the repository facts that matter
5. rewrite the prompt in a more execution-ready form

What matters most to me is that the skill does not change the user objective. If the user asks for a specific fix or improvement, the rewritten prompt should stay aligned with that exact request.

The problem it solves

In practice, vague coding prompts often lead to issues like:

• editing the wrong files
• broadening scope unintentionally
• replacing patterns that should have been preserved
• missing nearby dependencies or contracts
• returning generic advice instead of something directly actionable

A lot of the time, people assume this is mostly a model problem. My experience is that it is often a prompt-to-repository alignment problem.

The main idea behind the skill

One principle I keep coming back to is:

A better coding prompt is not necessarily a longer prompt.
A better coding prompt is usually a prompt that is more aware of the repository.

That is also why the SKILL.md stays narrow:

• inspect only what is relevant
• prefer real paths, modules, and contracts
• avoid inventing new requirements
• keep the final output short and reusable

The output format stays limited to:

• Intent Summary
• Codebase Context
• Improved Prompt

That makes it practical instead of overly formal.

Why generic prompts make agents drift

A prompt like this:

Help me improve the blog for mobile.

is understandable, but there is still too much the agent has to guess:

• where the blog is implemented
• whether the issue is on the list page or detail page
• which router architecture is being used
• where the data comes from
• which patterns should be preserved

The more the agent has to guess, the more likely it is to move away from what the user actually wanted.

What a better prompt looks like

The same request can become something like:

Improve the blog responsiveness in `src/app/components/blog/blog-list-page.tsx` and `src/app/components/blog/blog-post-page.tsx`.
Keep the current App Router structure and preserve `/blog` and `/blog/[slug]`.
Do not redesign the visual direction. Focus on spacing, overflow handling, image sizing, and typography on smaller screens.
Run a production build after the changes to verify the result.

The objective does not change. But the improved prompt is:

• more specific
• more repository-aware
• easier to execute
• easier to review afterward

That difference is exactly what I want this skill to create.

How the workflow works in practice

1. Read the original prompt carefully

The first step is always to identify:

• goal
• scope
• constraints
• expected output

Even short prompts usually contain enough intent to work with, as long as that intent is extracted carefully.

2. Inspect only relevant code

I do not want the skill to scan the whole repository unless that is actually necessary. It should focus on:

• entry points
• routes
• main components
• related utilities
• config or tests when they matter

This matters because over-reading unrelated code can make the rewritten prompt unnecessarily broad.

3. Infer concrete repository context

Once the relevant code is inspected, the skill extracts the parts that should appear in the rewritten prompt:

• exact file paths
• functions or classes involved
• architectural patterns to preserve
• nearby dependencies
• likely risks

That is the step that turns a generic prompt into a repository-aware one.

4. Rewrite the prompt in an execution-ready form

The final prompt should be usable immediately. That means:

• minimal ambiguity
• no unnecessary theory
• enough repository context for another agent to begin work

This is also why I kept the output format short instead of turning it into a full report.

When I find this skill most useful

In my experience, codebase-prompt-enhancer is especially useful for:

• feature implementation
• debugging
• repository-specific refactoring
• code review prompts
• codebase analysis prompts

It becomes even more useful when:

• the repo is large enough that the model can easily guess wrong
• the original user prompt is brief
• the next step is to hand the improved prompt to another coding agent

When it is probably unnecessary

I do not think this skill is needed for everything. It is much less useful for:

• purely theoretical questions
• very small single-file tasks with obvious context
• requests that do not need prompt rewriting at all

Its real strength is in repository-bound tasks, not general explanations.

Rules I intentionally kept in the skill

When I wrote the SKILL.md, I wanted to block a few common AI habits.

Do not change the user objective

Models often try to “improve” the task itself. I wanted this skill to avoid that.

Do not invent new requirements

If the repository does not justify them, the rewritten prompt should not introduce them.

Do not broaden scope

Just because related code exists does not mean all of it belongs in the new prompt.

Prefer repository facts over generic advice

A prompt that names the actual files, components, and constraints is usually much more useful than something like “please follow best practices”.

What I value most about it

The biggest benefit is not that the prompt sounds more polished. The real benefit is that:

• the agent stays closer to the intended task
• fewer correction rounds are needed
• review becomes easier
• collaboration between the user and the agent becomes clearer

If you often lose time fixing the first response because the prompt was too broad, a repository-aware rewrite step can make a noticeable difference.

A short checklist I use for coding prompts

Before handing a coding prompt to an AI agent, I usually ask:

• does the prompt point to the right files or routes?
• does it preserve the original objective?
• does it mention the important constraints?
• is it accidentally broadening scope?
• is it specific enough for another agent to execute directly?

If several of those answers are still “not really”, that is usually where codebase-prompt-enhancer becomes useful.

Conclusion

To me, codebase-prompt-enhancer is not about making prompts sound more sophisticated. It is a practical way to move a prompt from correct but vague to correct and much more usable inside the real repository.

For modern AI coding workflows, that is often the difference that matters most.