How does it improve prompt performance?

It applies a specific decision framework to select the best prompting techniques (like Chain of Thought or Few-Shot) while removing 'bloat'—content that doesn't actually change the AI's behavior.

What artifacts can this skill generate?

It can generate Claude Code skills (.claude/skills/), commands (.claude/commands/), subagents (.claude/agents/), and specialized reference documents.

Can I use it to audit my existing prompts?

Yes, the 'analyze' trigger allows the skill to evaluate your current prompt files and provide specific, reasoned suggestions for improvement.

Why is bloat removal important for Claude Code?

Reducing bloat lowers token costs, decreases latency, and prevents the model from being distracted by irrelevant instructions, leading to more accurate outputs.

Prompt Architect

Name: Prompt Architect
Author: bcasci

bybcasci

Developer Tools

Generates, analyzes, and optimizes high-performance prompts for Claude Code skills, commands, and subagents.

About

The Prompt Architect skill provides a rigorous framework for developing and refining AI instructions within the Claude Code ecosystem. By utilizing specialized artifact guides and a curated techniques catalog, it enables developers to create precise, bloat-free prompts for skills, commands, subagents, and reference documents. It excels at identifying unnecessary content that doesn't influence model behavior, ensuring that your custom Claude extensions are both cost-effective and highly reliable.

Key Features

Template-based generation for .claude artifacts
Bloat reduction to minimize token usage
Standardized formatting for skills and subagents
Deep analysis of prompt effectiveness and logic
Decision framework for selecting prompting techniques
0 GitHub stars

Use Cases

Optimizing reference documentation for better context retrieval
Refining subagent instructions to improve complex task execution
Scaffolding a new Claude Code skill with proper frontmatter and triggers

About

Key Features

Template-based generation for .claude artifacts
Bloat reduction to minimize token usage
Standardized formatting for skills and subagents
Deep analysis of prompt effectiveness and logic
Decision framework for selecting prompting techniques
0 GitHub stars

Use Cases

Optimizing reference documentation for better context retrieval
Refining subagent instructions to improve complex task execution
Scaffolding a new Claude Code skill with proper frontmatter and triggers