OpportunitySpec Framework¶

OpportunitySpec is a merged 12-box framework from prism-roadmap that combines Jeff Patton's Opportunity Canvas (discovery-focused) with Marty Cagan's SVPG Opportunity Assessment (business case-focused).

When to Use¶

Use OpportunitySpec when evaluating feature-level opportunities within an existing product line. For new product lines, start with MRD instead.

Scope	Starting Document	Profile
New product line	MRD (Market Requirements)	`aws-product`
New feature on existing product	OpportunitySpec	`aws-feature`

The OpportunitySpec Flow¶

OpportunitySpec replaces MRD as the starting point for feature-level Working Backwards:

1. OPPORTUNITY DISCOVERY (human-authored)
   opportunity-spec.md
       ↓
2. WORKING BACKWARDS (synthesized, editable)
   press.md  →  faq.md  →  prd.md
   (vision)     (scope)    (requirements)
       ↓
3. STAKEHOLDER REVIEW (synthesized, editable)
   narrative-1p.md / narrative-6p.md
       ↓
4. USER EXPERIENCE (human-authored)
   uxd.md
       ↓
5. TECHNICAL SPECS (synthesized, editable)
   trd.md  →  tpd.md  →  ird.md
       ↓
6. RECONCILIATION
   spec.md

OpportunitySpec Structure¶

The 12-box canvas is organized in a 3×4 grid:

Row 1: Discovery¶

Box	Question	Source
1. Users & Problem	Who has the problem and what is it?	Patton + Cagan
2. Current Solutions	How do people solve this today?	Patton + Cagan
3. Solution Ideas	What are our solution concepts?	Patton

Row 2: Value¶

Box	Question	Source
4. User Value	What value does this provide to users?	Patton + Cagan
5. Business Value	What value does this provide to the business?	Patton + Cagan
6. Competitive Edge	Why are we best suited to pursue this?	Cagan

Row 3: Market¶

Box	Question	Source
7. Market & Timing	Who's the market and why now?	Cagan
8. Go-to-Market	How will we get this to users?	Patton + Cagan
9. Success Metrics	How will we measure success?	Patton + Cagan

Row 4: Validation¶

Box	Question	Source
10. Critical Requirements	What must be true for this to succeed?	Cagan
11. Risks & Assumptions	What are we betting on and what could go wrong?	Patton + Cagan
12. Recommendation	Given all the above, what's the recommendation?	Cagan + Patton

Using the aws-feature Profile¶

Initialize a Feature Project¶

multispec init mobile-app-feature --profile aws-feature

Draft the OpportunitySpec¶

multispec draft opportunity-spec -p mobile-app-feature

The template includes all 12 boxes with guidance for each:

## Row 1: Discovery

### Box 1: Users & Problem

*"Who has the problem and what problem are we solving?"*

**Primary Users:**
<!-- List primary user types affected by this problem -->

**Problem Statement:**
<!-- Clear, specific statement of the problem -->

**Evidence:**
<!-- What evidence supports this problem exists? -->

Evaluate the OpportunitySpec¶

multispec eval opportunity-spec -p mobile-app-feature

The rubric evaluates 5 categories with weighted scoring:

Category	Weight	Criteria
Discovery Clarity	20%	Problem definition, user identification, competitive analysis
Value Proposition	25%	User value, business value, differentiation
Market & Timing	20%	Market definition, timing rationale, success metrics
Validation Readiness	20%	Requirements, risks, recommendation quality
Document Quality	15%	Coherence, completeness, actionability

Thresholds:

PASS: ≥80%
PARTIAL: ≥60%
FAIL: <60%

Proceed to Working Backwards¶

Once the OpportunitySpec passes evaluation:

# Approve OpportunitySpec
multispec approve opportunity-spec -p mobile-app-feature

# Synthesize Press Release from OpportunitySpec
multispec synthesize press -p mobile-app-feature

# Continue with FAQ and PRD
multispec synthesize faq -p mobile-app-feature
multispec synthesize prd -p mobile-app-feature

Prioritization Frameworks¶

OpportunitySpec includes two prioritization frameworks from prism-roadmap to help evaluate and rank features.

RICE Scoring¶

RICE provides quantitative prioritization based on four factors:

Score = (Reach × Impact × Confidence) / Effort

Component	Description	Values
Reach	Users affected per time period	Integer (e.g., 1000 users/quarter)
Impact	Effect on each user	massive (3x), high (2x), medium (1x), low (0.5x), minimal (0.25x)
Confidence	Certainty in estimates	high (100%), medium (80%), low (50%)
Effort	Resources required	Float (person-months)

When to use RICE:

Comparing multiple features for roadmap prioritization
Quantitative justification for investment decisions
Effort and reach estimates are available

Example in OpportunitySpec:

Add RICE scoring to Box 12 (Recommendation) or as a summary section:

## Prioritization

### RICE Score

| Component | Value | Justification |
|-----------|-------|---------------|
| Reach | 1,000 users/quarter | Based on current active user count |
| Impact | High (2x) | Solves critical workflow blocker |
| Confidence | Medium (80%) | Customer interviews support this |
| Effort | 2 person-months | Engineering estimate |

**Score:** (1000 × 2.0 × 0.8) / 2 = **800**

Kano Model¶

Kano classifies features by customer satisfaction impact using paired questions:

Functional: "If the product HAS this feature, how do you feel?"
Dysfunctional: "If the product DOES NOT HAVE this feature, how do you feel?"

Category	Description	Priority
Must-Be	Basic expectation - absence causes dissatisfaction	5 (highest)
Performance	Linear satisfaction - more is better	4
Attractive	Delighter - unexpected positive surprise	3
Indifferent	No significant impact on satisfaction	1
Reverse	Unwanted - presence causes dissatisfaction	0

When to use Kano:

Understanding customer expectations vs. delighters
Deciding between "must-have" and "nice-to-have"
Product-market fit validation