agent-factory

I've been digging through skill implementations and came across your agent-factory—the recursive task delegation pattern is clever, though I'm curious how you're handling agent state synchronization at scale since that's where most systems start to fracture.

Links:

• GitHub
• Your agentic skill in the SkillzWave marketplace

The TL;DR

You're at 76/100, solidly in C territory—which is a good foundation but leaving meaningful points on the table. This is based on Anthropic's best practices for skill structure and usability. Your strongest area is Utility (16/20) where you're actually solving a real problem with structured approaches. The weak spots are Progressive Disclosure Architecture (20/30) and Writing Style (7/10), which are fixable.

What's Working Well

• Solid utility and problem-solving: You're addressing a genuine gap (custom agent creation) with structured workflows and concrete agent types (simple, agentic, supervisor). That's 16/20 points well-earned.
• Metadata and conventions: Name format is correct, YAML frontmatter is valid, and you maintain consistent terminology throughout (kebab-case, YAML structure, MCP integration patterns).
• Comprehensive examples: You've got extensive examples showing different agent configurations—that earned you bonus points for being grep-friendly and exemplary in scope.

The Big One: File Structure is Defeating Progressive Disclosure

Here's the problem: Your SKILL.md is 269 lines and your template (AGENTS_FACTORY_PROMPT.md) is 1,124 lines. That's massive. Progressive Disclosure means users should get the concept quickly from SKILL.md (~80 lines), then drill deeper into references. Right now, they're climbing a mountain to understand what your skill does.

The fix: ...