Map the decision space,
before choosing a path.

The problem

AI often answers too quickly. When you ask what to build, write, sell, publish, prioritize, or choose, the first answer may sound reasonable while hiding the actual decision space. The useful step is often not generation. It is recovering the options, constraints, tradeoffs, and paths that were implicit in the conversation.

The skill

Mora reads the current context and maps coherent candidate paths. Each path includes what to foreground, what to de-emphasize, who it serves, what behavior follows, and what must be true for the path to work.

What it does

1. 1. Recovers the context

   It extracts options, constraints, assets, tensions, audience, goals, and unresolved questions from the conversation.

2. 2. Builds candidate paths

   It avoids loose categories and creates actionable paths with clear implications.

3. 3. Names what to test next

   If a path depends on human reactions, it identifies the artifact and audience that should be tested next.

4. 4. Keeps the decision grounded

   It does not write polished copy by default. It organizes the user's own context and possible choices.

Example

The output should make the possible paths visible. It should not pretend that one answer is obvious before the decision space has been mapped.

Install

Paste this into Claude Code, Codex, Cursor, Hermes Agent, or another terminal-capable AI agent.

npx skills add ryuzo-k/yomira

Separate product

Mora maps options. Yomira is a separate paid product for testing how people may react to a message, offer, page, product idea, or other artifact.