Skill Lifecycle Manager

Manage skill work as a lifecycle, not as isolated one-off edits. This skill is a thin router: it figures out which lifecycle stage matters now, runs only those stages, and hands off deep specialist work to the right sub-skill or helper instead of re-embedding every workflow inline.

The default design bias is:

• Use OpenAI-style structure discipline for authoring, packaging boundaries, and progressive disclosure
• Use Anthropic-style evaluation discipline for realistic test prompts, baseline comparisons when warranted, and separate trigger optimization
• Use repo-local conventions for skill.json, CHANGELOG.md, categories, generated catalogs, and validation commands

Outputs

Depending on the requested stage, produce one or more of: