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idea-refine
Sharpen a vague idea into a buildable, scoped proposal
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Sharpen a vague idea into a buildable, scoped proposal
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | idea-refine |
| description | Sharpen a vague idea into a buildable, scoped proposal |
| difficulty | junior |
| domains | ["general"] |
Most ideas arrive as fuzzy intuitions. This skill converts them into crisp, buildable proposals with clear scope, constraints, and success criteria — before anyone writes a spec or a line of code.
Write one sentence describing the problem being solved. Not the feature — the problem. Example: "Users can't find past orders because search only covers the last 30 days."
Name the specific user persona or system component affected. Vague problems have vague solutions.
Quantify where possible: "affects 20% of active users," "adds 3 minutes to the workflow," "causes 12 support tickets/week." If you can't measure it, question whether it's a real problem.
Write 3 different ways to solve the problem at different points on the effort/impact curve. This prevents anchoring on the first idea.
For each solution: estimate effort (S/M/L), impact (low/medium/high), and risk (low/medium/high). Select the option with the best ratio for the current context.
Explicitly state what this proposal does NOT include. Scope creep starts here if you don't.
One measurable outcome that proves the problem is solved. Not "users like it" — "search result relevance score improves by 15% on the benchmark dataset."
"We know what we want to build — let's just build it" The thing you want to build is a solution. Before committing to a solution, confirm you've correctly understood the problem.
"We don't have data on this yet" Absence of data is a finding. Document your assumptions and validate them in the first iteration.
Build UIs that work for all users including keyboard navigation, screen readers, and WCAG 2.2
Design multi-agent systems with robust tool interfaces, state management, and failure handling
Build ML systems with disciplined training, evaluation, deployment, and safety practices
Design APIs that are stable, ergonomic, and evolvable
Design systems at the right scale with explicit trade-off documentation
Design services that are reliable, observable, secure, and maintainable