When Not to Use Kodebase

I built Kodebase because I believe in executable documentation—specs that compile into working software. But I'd be lying if I said it was right for every project.

Some projects don't need structured planning. Some contexts are better served by just coding. Some teams work better with different approaches.

This is an honest assessment of when Kodebase helps—and when it doesn't.

When Kodebase Shines

Let's start with the sweet spot. The methodology delivers the most value when:

You're a non-technical founder who wants to ship.

This is the core use case. You describe what you want in plain language. Scout asks clarifying questions and validates your business logic. Sherpa generates the structured artifacts. AI agents implement the code. You evaluate outcomes—"does it do what I asked?"—without ever touching YAML or reviewing code.

The alternative is hiring developers, managing sprints, and hoping the thing they build matches what you imagined. Kodebase lets you stay in control of the what while the system handles the how.

You're building something complex.

Multi-module systems. Features that touch many parts of the codebase. Projects where a single developer can't hold the entire architecture in their head.

Scout's validation prevents you from building vague features. The artifact structure prevents the AI from making locally reasonable decisions that create global inconsistency. For simple projects, this structure isn't necessary.

You're working over weeks or months, not hours.

Context decay is a function of time. If you're building something in an afternoon, there's nothing to decay. The AI can hold everything in a single context window.

But if you're coming back to a codebase after a week, the specs and artifacts become invaluable. They're the institutional memory that survives between sessions—and they're versioned alongside your code.

You need to maintain quality at speed.

If you just need something that works, AI can spit out code without structure. But if you need something that keeps working—that's testable, maintainable, and won't create nightmares in six months—the upfront investment in clear specs pays off.

Scout won't let you ship garbage. Validation steps and acceptance criteria are built into the artifacts. The methodology enforces quality, not just suggests it.

Multiple people (or agents) are contributing.

The artifact structure becomes a coordination mechanism. Everyone knows what's being built, what the acceptance criteria are, and how their work connects to others.

For true solo development with no handoffs ever, this coordination value is reduced—but even solo developers benefit from the enforced clarity.

When Kodebase Is Overkill

Here's where the methodology adds more friction than value:

Weekend hacks and experiments.

You're exploring an idea. You don't know if it'll work. You might throw away everything by Sunday night.

Don't use Kodebase. Just code. The point of a hack is to move fast and learn. Even with Scout generating specs automatically, the planning process slows you down for benefits you'll never collect.

I prototype without Kodebase all the time. When an experiment proves viable and needs to become real software, then I add structure.

One-off scripts and utilities.

A script that runs once, a utility that does one thing, a tool you'll use for a month and forget about.

Just write it. Ask Claude to write it. No specs needed.

Learning a new technology.

When you're learning, you need to make mistakes fast. You need to try things, break them, understand why they broke.

Structured planning imposes order on a process that benefits from chaos. Learn first. Structure later.

Truly simple projects.

A landing page. A static site. A CRUD app with three screens.

If you can hold the entire project in your head and describe it in one sentence, you don't need Scout to validate your business logic or Sherpa to guide implementation. The methodology is designed for complexity; simple projects are better served by simple tools.

The Gray Zone

Some situations could go either way:

Early-stage startups (pre-product-market-fit).

You're iterating fast. Requirements change weekly. What you build today might be thrown away tomorrow.

Arguments for Kodebase: Scout forces clarity. You can't describe a vague feature and have it pass validation. This prevents wasted cycles on poorly-conceived ideas—you'll know if something doesn't make sense before you build it.

Arguments against: If you're pivoting every two weeks, even lightweight planning might feel like overhead.

My take: Kodebase actually shines here. The problem with early-stage iteration isn't building—it's building the wrong thing. Scout's validation catches bad ideas early. And when you pivot, the specs document why you pivoted.

Agencies and client work.

You're building for someone else. Requirements come from outside. Timelines are fixed.

Arguments for Kodebase: Scout can translate vague client wishes into clear specs. You share the plain-language summary with clients for approval. Acceptance criteria are negotiated upfront, preventing scope creep.

Arguments against: Some clients genuinely don't know what they want until they see it. No amount of validation will help.

My take: Kodebase works well for clients who can answer questions. Scout asks clarifying questions naturally—that's the validation process. If a client can't engage with "What happens when X fails?" they probably can't engage with any planning process.

Open source projects.

Arguments for: Contributors need context. The .kodebase/docs/ folder becomes contributor documentation. Specs explain why things are built the way they are.

Arguments against: Open source thrives on low barriers. If contributing requires understanding the Kodebase methodology, you might lose casual contributors.

My take: Use Kodebase for core development. Keep the contribution process simple—don't require contributors to use Scout or understand artifacts. Let the maintainers handle the methodology; let contributors just submit PRs.

The Anti-Patterns

Some signs you're using Kodebase wrong:

Specifying every trivial change.

Not every change needs to go through Scout. Bug fixes often don't need formal specs. Style changes don't. Documentation updates don't.

The methodology is for features and significant changes. For a typo fix, just fix the typo.

Ignoring Scout's questions.

Scout asks clarifying questions for a reason. If you're rushing through validation—answering "yes" to everything without thinking—you're defeating the purpose.

The validation is the value. If you skip it, you're just adding overhead without getting the benefit.

Specs that never update.

The spec says one thing. The code does another. Nobody updated the docs when requirements changed.

Stale specs are worse than no specs—they actively mislead. If you're not maintaining them, stop using them. (Monk will eventually help with this, flagging when specs drift from reality.)

Using Kodebase to avoid building.

Planning is seductive. It feels like progress. Some people hide in specs to avoid the scary work of actually shipping.

If you've been "refining the spec" for three weeks and haven't shipped anything, something's wrong. Scout should validate in one session, not twenty.

The Honest Assessment

Kodebase adds value when:

• You want to describe features in plain language and have them built correctly
• Projects are complex enough to benefit from validated planning
• Timelines are long enough for context to decay
• Quality enforcement matters—you can't ship without meeting criteria
• Multiple contributors or AI agents need coordination

Kodebase is overkill when:

• Projects are simple enough to hold in your head
• You're experimenting or prototyping
• You're learning a new technology
• It's a one-off script you'll run once and forget

I use Kodebase for Kodebase. I don't use it for Saturday experiments or one-off scripts. The methodology is a tool. Tools are for specific jobs.

Know when to use it. Know when to put it down.

The Real Question

Before adopting any methodology, ask: What problem am I solving?

If your problem is "I have an idea but no technical skills to build it," Kodebase helps. Scout validates your thinking; Sherpa guides the AI to build it.

If your problem is "AI keeps forgetting my conventions and producing inconsistent code," Kodebase helps. The specs become the AI's persistent memory.

If your problem is "I need to test an idea quickly," Kodebase probably doesn't help. Just build the thing.

If your problem is "I hate the chaos of my current process," Kodebase might help—but so might better habits, clearer thinking, or just slowing down.

Methodology isn't magic. It's leverage. And leverage only matters if you're lifting something heavy.