Turning Repetitive Instructions in Lovable into Reusable Skills

AI agents are still general-purpose, so every time you use Lovable you have to re-explain how to work and what the rules are. To reduce this, the Skills format proposed by Anthropic late last year has spread, and Lovable and OpenAI have both adopted it. The core idea is to reuse instructions and context written once, whenever they're needed.

In Lovable, workspace / project knowledge is a background rule that's always on, while Skills are task-specific playbooks that are called up only when a particular task comes in. Knowledge handles common standards, while Skills handle recurring workflows, complementing each other.

A Skill is basically a folder, containing a SKILL.md and supporting files. The --- section in SKILL.md is frontmatter, metadata that holds the name and description. The actual selection criterion is the description alone, and if the body gets long, details are moved out into supporting files and linked. In the example, deeper materials like colors.md, components-reference.md, voice-examples.md, and cta-library.md are kept separate.

Skills aren't scripts but documentation that Lovable reads and follows. That's why they should be portable to other tools, readable by humans, and easy to edit. Multiple Skills can be attached to a single task at once, and they can also be invoked manually with /skill-name.

Three examples are given.

• design-system: enforces rules for colors, spacing, typography, and component reuse.
• fresh-eyes-review: gives blunt feedback from a first-time visitor's perspective.
• landing-page-copy: fixes the tone, structure, and CTA rules for landing pages.

Ultimately, what matters is building up finely split Skills by purpose rather than one large all-purpose Skill.