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idea-refine

Sharpen a vague idea into a buildable, scoped proposal

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Quellinformationen

Repository
vignesh2027/AI-AGENT-SKILLS
Letzte Quellaktivität
13. Mai 2026 um 19:03
Erkannte Sprache von SKILL.md
Englisch
Sterne
1
Forks
0

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Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
idea-refine
description
Sharpen a vague idea into a buildable, scoped proposal
difficulty
junior
domains
["general"]
## 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
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