What is the PROMPTCONVERTER methodology?

It is a structured approach used by this skill to convert loose requirements into function-like syntax, eliminating the ambiguity often found in complex natural language coding tasks.

Where are the feature patterns stored?

Patterns are saved locally in the .claude/pseudo-code-prompting/patterns.md file, allowing the skill to learn from and replicate successful implementations over time.

How does this enhance the /feature-dev command?

It acts as a pre-processor that turns vague natural language into precise pseudo-code parameters, making subsequent search, architecture, and implementation phases significantly more accurate.

Can I use this for non-feature-dev tasks?

While specifically optimized for the /feature-dev workflow, the pseudo-code structuring logic can be applied to any complex coding request to improve model performance and clarity.

Does this skill help with code reviews?

Yes. By creating explicit parameters at the start of the workflow, it provides a clear, testable checklist for the review phase to ensure every requirement was implemented correctly.

Pseudo-Code Feature Architect

Name: Pseudo-Code Feature Architect
Author: EladAriel

byEladAriel

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Productivity & Workflow

Structures natural language requirements into unambiguous pseudo-code to streamline the Claude Code feature development workflow.

About

This skill optimizes the native Claude Code /feature-dev workflow by applying the PROMPTCONVERTER methodology to translate user requests into structured, function-like pseudo-code. By defining explicit parameters and constraints upfront, it eliminates ambiguity, sharpens exploration searches, and ensures architecture and implementation phases map directly to initial requirements. It includes a persistent memory system to learn and reuse patterns for common features like OAuth, caching, and API integrations, leading to faster phase transitions and higher-quality code reviews through verifiable acceptance criteria.

Key Features

Standardized review checkpoints derived from pseudo-code parameters
Phase-specific optimization from discovery through exploration and implementation
2 GitHub stars
Automated conversion of requirements into structured pseudo-code directives
Persistent memory integration for learning and reusing domain-specific feature patterns
Parameter-driven clarification to identify and resolve missing requirements early

Use Cases

Standardizing feature implementation across projects using a shared patterns library
Translating broad user requests into technical specifications for the /feature-dev command
Accelerating the exploration phase with precise, parameter-based search targets

About

Key Features

Standardized review checkpoints derived from pseudo-code parameters
Phase-specific optimization from discovery through exploration and implementation
2 GitHub stars
Automated conversion of requirements into structured pseudo-code directives
Persistent memory integration for learning and reusing domain-specific feature patterns
Parameter-driven clarification to identify and resolve missing requirements early

Use Cases

Standardizing feature implementation across projects using a shared patterns library
Translating broad user requests into technical specifications for the /feature-dev command
Accelerating the exploration phase with precise, parameter-based search targets