Is it compatible with any programming language?

Yes, Context Hunter focuses on structural logic, naming patterns, and file relationships that apply across all programming languages and frameworks.

Why should I use Context Hunter before a refactor?

It identifies how data flows and how errors are handled in similar modules, preventing regressions and maintaining the architectural integrity of the system.

Can it detect specific naming conventions?

Yes, it analyzes patterns in database schemas, variable naming (such as camelCase vs snake_case), and file structures to match the project's style.

What does the Context Hunter skill do?

It prompts Claude to proactively investigate your existing codebase for patterns, conventions, and utilities before writing new code to ensure every change remains idiomatic.

How does this improve AI-generated code?

By forcing the AI to 'look before it leaps,' the generated code becomes indistinguishable from existing code, reducing the need for manual cleanup and style corrections.

Context Hunter

Name: Context Hunter
Author: MrLesk

byMrLesk

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Herramientas para Desarrolladores

Analyzes existing codebase patterns and unwritten conventions to ensure new code remains consistent and idiomatic.

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Context Hunter is a specialized skill designed to help Claude perform deep architectural discovery before implementing changes. It guides the AI to investigate analogous features, trace data flow, and identify implicit naming or schema conventions within a repository. By prioritizing pattern matching and utility discovery, it ensures that new features, bug fixes, or refactors are indistinguishable from the existing codebase, reducing technical debt and maintaining high standards of code quality. This skill is essential for maintaining architectural integrity in large, complex, or legacy projects where conventions may not be explicitly documented.

Características Principales

Pattern Discovery: Automatically searches for analogous features and existing implementations.
Data Flow Tracing: Maps how data moves through the system to ensure consistent handling and validation.
Consistency Guardrails: Flags conflicts between new requirements and established codebase patterns.
Utility Audit: Prevents code duplication by locating existing helpers and reusable utilities.
16 GitHub stars
Convention Detection: Identifies implicit naming, schema, and testing rules within the repo.

Casos de Uso

Implementing new features that must strictly adhere to existing design patterns and abstractions.
Refactoring legacy code while preserving specific error handling and database tracking behaviors.
Onboarding to a new codebase where architectural conventions are not yet fully documented.

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Características Principales

Pattern Discovery: Automatically searches for analogous features and existing implementations.
Data Flow Tracing: Maps how data moves through the system to ensure consistent handling and validation.
Consistency Guardrails: Flags conflicts between new requirements and established codebase patterns.
Utility Audit: Prevents code duplication by locating existing helpers and reusable utilities.
16 GitHub stars
Convention Detection: Identifies implicit naming, schema, and testing rules within the repo.

Casos de Uso

Implementing new features that must strictly adhere to existing design patterns and abstractions.
Refactoring legacy code while preserving specific error handling and database tracking behaviors.
Onboarding to a new codebase where architectural conventions are not yet fully documented.