anthropics / anthropics/knowledge-work-plugins
Add stability lifecycle frontmatter to skills (experimental/stable/deprecated)
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- Python
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Descrição
## Problem
Skills in this plugin have no stability indicator. An AI agent consuming skills cannot distinguish a well-tested default skill from a new/beta one, causing it to treat experimental workflows as production-ready. This leads to wasted tool calls on deprecated MCP servers or experimental features whose APIs change without notice.
## Proposed solution
Add a `stability:` frontmatter field to the SKILL.md schema with three values:
- `experimental` — actively developed, may change without notice
- `stable` — tested, production-ready, backwards-compatible
- `deprecated` — maintained for compatibility but no longer actively developed
Skill authors set `stability:` in frontmatter when submitting. The plugin's README or skill discovery surface can document the field so agents can filter or warn accordingly.
## Use case
An AI agent scanning a large plugin pool for production-grade workflows can filter on `stability: stable` and warn before invoking `experimental` skills or skip `deprecated` ones entirely. This prevents wasted tool calls and incorrect routing assumptions.
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