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idea-evaluation

by solvaholic

Design thinking workflow system powered by GitHub Copilot agents

1🍴 0📅 2026年1月1日
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SKILL.md


name: idea-evaluation description: Assess solution ideas on impact and feasibility to prioritize prototyping. Use during Ideate phase when deciding which ideas to pursue.

Idea Evaluation

Overview

Systematically assess solution ideas to prioritize which ones to pursue through prototyping.

When to Use

  • During Ideate phase after generating multiple ideas
  • When deciding which ideas to prototype
  • When stakeholders or leadership ask for prioritization
  • Before committing resources to development

How to Apply

1. Grade on Two Dimensions

IMPACT — How much value would this create?

  • High: Solves major pain point, affects many users, strategic value
  • Medium: Improves experience, helps some users, supports goals
  • Low: Nice improvement, limited scope, minor benefit

FEASIBILITY — How realistic is this to build/implement?

  • High: Clear path, existing tech, reasonable timeline/resources
  • Medium: Some unknowns, might need new tech, moderate complexity
  • Low: Major technical challenges, unclear if possible, high cost

2. Plot on Impact/Feasibility Matrix

       FEASIBILITY
       Low    Med    High
    ┌─────────────────────┐
H   │       │  B  │  A   │
I   │       │     │      │
M   ├───────┼─────┼──────┤
P   │       │  C  │  B   │
A   │       │     │      │
C   ├───────┼─────┼──────┤
T   │   D   │  D  │  C   │
    └─────────────────────┘

A tier: High impact, High feasibility → Prioritize B tier: High impact, Med feasibility OR Med impact, High feasibility → Consider C tier: Med impact, Med feasibility OR Low impact, High feasibility → Maybe later D tier: Low feasibility or Low impact → Park or discard

3. Consider Additional Factors

Beyond impact/feasibility:

  • Stakeholder priority: What do users care about most?
  • Strategic fit: Aligns with business goals?
  • Dependencies: Blocks or enables other ideas?
  • Learning value: Will prototyping teach us something important?
  • Risk: What happens if we're wrong?

4. Make Recommendations

For each tier:

A tier: "Prototype immediately. This addresses [key insight] with clear path forward."

B tier: "Strong candidate. Needs [feasibility spike / user validation / resource check] before committing."

C tier: "Interesting but not priority. Revisit if A/B tier ideas fail or after launch."

D tier: "Park for now. [Technical barriers / unclear value / other ideas are stronger]."

5. Document in currentstate.json

{
  "id": "idea1",
  "title": "Offline data capture",
  "description": "Allow field techs to log data without connection, sync when back online",
  "impact": "high",
  "feasibility": "high",
  "status": "ideated",
  "idea_doc_link": "ideas/offline_capture.md"
}

Evaluation Example

Idea: "AI-powered predictive maintenance alerts"

  • Impact: High — Prevents costly downtime, major pain point
  • Feasibility: Low — Need ML expertise, training data, unclear accuracy
  • Grade: B tier
  • Recommendation: Validate predictive value with simple rule-based alerts first, then explore AI if rules prove useful

Idea: "Quick-capture field form"

  • Impact: High — Addresses #1 user frustration with current 10-step process
  • Feasibility: High — Straightforward UI work, existing tech
  • Grade: A tier
  • Recommendation: Prototype immediately, test with 5 field techs

Tips

  • Involve the whole DesignTeam
  • Get stakeholder input on impact
  • Validate feasibility with technical experts
  • Don't over-engineer low-hanging fruit
  • High impact + Low feasibility may need research spike first
  • Re-evaluate as you learn more
  • Document reasoning, not just grades

スコア

総合スコア

60/100

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