Idea refine
Turn your ai agent into senior engineer..The result is fast code that fails slowly. AI Agent Skills solves this by giving agents the same disciplined workflows senior engineers use
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Sharpen a vague idea into a buildable, scoped proposal
SKILL.md
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Overview
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.
When to Use
- Before writing a spec for something you've only talked about
- When a request feels vague or underspecified
- When you're unsure if you're solving the right problem
- Before a technical design discussion
Process
Step 1: State the problem, not the solution
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."
Step 2: Identify who has the problem
Name the specific user persona or system component affected. Vague problems have vague solutions.
Step 3: Measure the current pain
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.
Step 4: List candidate solutions
Write 3 different ways to solve the problem at different points on the effort/impact curve. This prevents anchoring on the first idea.
Step 5: Score and select
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.
Step 6: Define the out-of-scope boundary
Explicitly state what this proposal does NOT include. Scope creep starts here if you don't.
Step 7: State the success metric
One measurable outcome that proves the problem is solved. Not "users like it" — "search result relevance score improves by 15% on the benchmark dataset."
Anti-Rationalizations
"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.
Verification Requirements
- Problem statement is one sentence and problem-focused (not solution-focused)
- Affected user or system is named
- At least 2 candidate solutions were considered
- Out-of-scope items are explicitly listed
- Success metric is measurable