If you already think in Skills, you already understand Decorators. One gives your agents reusable expertise. The other gives your prompts reusable shape. Decorators are a deterministic way to level up prompts — you spend time on intent, not on syntax.
A decorator is a packaged prompt instruction. A hook expands it into the careful version before the model ever sees your prompt. Here’s what that actually looks like.
You type:
::Antipatterns(domain=database, severity=critical, format=examples)
Review my MongoDB schema design.
The model receives:
Identify common antipatterns and mistakes in the specified domain. Focus on how to recognize these issues and provide guidance on how to avoid or fix them. Focus on database antipatterns such as poor schema design, inefficient queries, and data integrity issues. Focus only on the most severe antipatterns that pose immediate risks or significant technical debt. Provide concrete examples of each antipattern with code or design snippets that illustrate the problem.
Review my MongoDB schema design.
One line in. A five-sentence specification out — domain narrowed, severity filtered, output format dictated. The model now performs a fundamentally different review than it would for the bare prompt. You wrote the sigil once. The engine assembles the rest, the same way, every time.
Same pattern as Skills, with one important difference. Skills are loaded into context and the model decides whether to use them — they’re advisory. Decorators run in a UserPromptSubmit hook before the model ever sees your prompt — they’re deterministic. Use one, get its shaping, every time, no exceptions.
Turn on auto-decorate and you don’t even pick.
Install:
claude plugin marketplace add synaptiai/synapti-marketplace
claude plugin install prompt-decorators
A decorator is a packaged prompt instruction. A hook expands it into the careful version before the model ever sees your prompt. Here’s what that actually looks like. You type: ::Antipatterns(domain=database, severity=critical, format=examples) Review my MongoDB schema design.
The model receives: Identify common antipatterns and mistakes in the specified domain. Focus on how to recognize these issues and provide guidance on how to avoid or fix them. Focus on database antipatterns such as poor schema design, inefficient queries, and data integrity issues. Focus only on the most severe antipatterns that pose immediate risks or significant technical debt. Provide concrete examples of each antipattern with code or design snippets that illustrate the problem. Review my MongoDB schema design.
One line in. A five-sentence specification out — domain narrowed, severity filtered, output format dictated. The model now performs a fundamentally different review than it would for the bare prompt. You wrote the sigil once. The engine assembles the rest, the same way, every time.
Same pattern as Skills, with one important difference. Skills are loaded into context and the model decides whether to use them — they’re advisory. Decorators run in a UserPromptSubmit hook before the model ever sees your prompt — they’re deterministic. Use one, get its shaping, every time, no exceptions.
Turn on auto-decorate and you don’t even pick.
Install: claude plugin marketplace add synaptiai/synapti-marketplace claude plugin install prompt-decorators
Plugin marketplace: https://github.com/synaptiai/synapti-marketplace See all available decorators: https://synaptiai.github.io/prompt-decorators/api/decorators...