Philosophy — Feature Construction

Define data schemas before building minimal viable features.

Updated Apr 30, 2026
One-click install
npx skills add https://github.com/bytetalent/docs --skill philosophy-feature-construction
Or copy as Structured Prompt for Agent
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Skill: Philosophy — Feature Construction
Source: https://github.com/bytetalent/docs/tree/main/skills/meta/philosophy-feature-construction
Command: npx skills add https://github.com/bytetalent/docs --skill philosophy-feature-construction

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill provides core principles for building features when standard patterns do not apply, focusing on data-driven and iterative development.

Core Features & Use Cases

  • Data-First Approach: Encourages defining data shape before UI or routes.
  • Smallest Working Slice: Focuses on creating a minimal viable feature that meets essential requirements.
  • Error Handling Strategy: Defines when and how to implement error handling.
  • Testing Strategy: Outlines when to write tests and the importance of typechecking and linting.

Quick Start

Apply the philosophy of smallest working slices and data-first development to your new feature by starting with defining the data schema and implementing the core functionality.

Frequently Asked Questions about Philosophy — Feature Construction

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a feature when there are no existing patterns to follow?

Feature construction without existing patterns requires a data-first approach, where you define the data shape and schema before building UI or routes to establish a clear foundation.

What is the smallest working slice method for iterative feature development?

The smallest working slice method focuses iterative feature development on creating a minimal viable feature that meets essential requirements first, avoiding over-engineering before validating core functionality.

When should I implement error handling and testing during schema-driven API design?

In schema-driven API design, implement error handling and write tests after defining the data schema and building the smallest working slice, prioritizing typechecking and linting earlier.

Does data-driven development work for novel features across different tech stacks?

Data-driven development applies to novel features across various stacks because it prioritizes defining data schemas and iterative slices over stack-specific patterns, ensuring broad applicability.

What is the best way to start building a feature with no established patterns?

The best way to start feature construction without patterns is defining the data schema first, then implementing the core functionality using an iterative, minimal viable feature approach.