Structure user skill ideas into Aperio Skills with YAML frontmatter.
npx skills add https://github.com/BaiGanio/aperio --skill skill-creator-baiganio
Please help me install this Agent Skill. Skill: skill-creator Source: https://github.com/BaiGanio/aperio/tree/main/skills/skill-creator Command: npx skills add https://github.com/BaiGanio/aperio --skill skill-creator-baiganio
This Skill helps users create, draft, test, and evolve Aperio skills—guiding them from idea through evaluation to a polished skill description and packaging.
Provide a new skill concept, and I'll guide you through drafting SKILL.md, setting up evals, and iterating to a polished skill.
High-intent search queries and answers about installing and using this skill.
To create a new skill from scratch, provide a skill concept to scope it into a fully structured file with YAML frontmatter. The process drafts the SKILL.md body, configures evaluation loops, and iterates based on feedback to produce a polished skill.
Structuring a SKILL.md file for automation requires including YAML frontmatter with name, description, metadata, keywords, category, and load. The body must provide actionable instructions, defining the workflow, evaluation plan, and tooling required for iteration.
Skill iteration works by configuring evaluation loops that test the drafted SKILL.md against specific prompts. You iterate based on evaluation feedback to refine the trigger description, optimize tooling, and ensure the skill performs correctly across test cycles.
Yes, YAML is required to build a distributable skill file because it formats the essential frontmatter metadata. Packaging validates readiness by ensuring the YAML configuration and SKILL.md body are correctly structured before final distribution.
You can use MCP tools to test and optimize skill workflows by defining them within the SKILL.md tooling section. The skill creation process explicitly describes the tooling required to create, test, and optimize the workflow across multiple iterations.
Limitations when drafting evaluation prompts depend on the scope defined in the initial skill concept. If the scope is too broad, the evaluation loops may fail to provide actionable feedback, requiring you to narrow the workflow and tooling before iterating.
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