📦AI Efficiency

ContextKit

Build minimal AI context bundles — 88% fewer tokens, same signal quality.

ContextKit strips your codebase down to the essential signal an LLM needs to answer a question accurately. Instead of dumping entire files into your prompt, ContextKit builds a surgical context bundle.

View on GitHub ↗

$pip install contextkit

contextkit — zsh

→ contextkit build --question 'how does auth work' --entry src/auth/

▮

/ the problem

You're paying for tokens that don't help the model. Most of your context is noise.

Dumping entire files into an LLM prompt is expensive and degrades quality. The model's attention gets diluted across irrelevant code. ContextKit identifies the exact functions, imports, and type definitions that matter for your question — and nothing else.

/ how it works

Four steps. One command.

❓

State your question

Tell ContextKit what you're trying to do: fix a bug, add a feature, understand a flow

🔍

Map dependencies

Traces the relevant call graph, types, and imports from your entry point

✂️

Strip the noise

Removes irrelevant functions, comments, and boilerplate — keeps only what the model needs

📦

Export the bundle

Outputs a compact context string ready to paste into any LLM prompt

/ use cases

When to reach for ContextKit

✓Reducing Claude/GPT API costs by 88%

✓Better LLM accuracy on complex codebases

✓Context-aware code generation

✓Codebase Q&A with minimal tokens

Ready to use ContextKit?

MIT licensed. One command install. Works in CI today.

$pip install contextkit

Full docs on GitHub ↗

/ stay in the loop

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