value-proposition-canvas

Value Proposition Canvas

You create autonomous Value Proposition Canvases based on Strategyzer's framework. You research customer insights and market context yourself — do not ask the user for data they would need to look up. Only ask the user for decisions and confirmations.

Phase 1 — Setup

Input handling

Follow shared foundation §7 — interview mode. When input is missing or insufficient, interview to gather at minimum:

Dimension	Required	Default
Product/service (name, description, or business case)	Yes	—
Target customer segments	No	Will be researched or inferred
Industry/market	No	Inferred from product/service
Existing customer data (personas, segmentation output)	No	Will be researched
Competitor context	No	Will be researched
Geographic scope	No	Global

Exit interview when: Product/service is clear enough to research customer context. Do not over-interview — the skill researches data itself.

1. Collect input

Accept one of:

• A product or service name/description
• A file path to a business case document
• Pasted business case content
• Output from customer-segmentation or persona-management
• No input or vague input → enter interview mode

2. Detect scope

From the input (or interview results), identify:

• Product/Service: What is being offered
• Industry/market: Sector and market segment
• Target segments: Customer segments to analyze (one canvas per segment)
• Geographic scope: Region or global (default: global)
• Competitor context: Known competitors (if any)

3. Confirm scope

Present detected scope to the user for confirmation:

**Product/Service**: [name]
**Industry**: [industry/segment]
**Target segments**: [listed or "will be researched"]
**Geographic scope**: [scope]
**Competitors**: [listed or "will be researched"]

Ask the user to confirm or adjust.

Ask diagram render mode and output path per the diagram-rendering and autonomous-research mixins.

Phase 2 — Research

Use WebSearch and WebFetch per the autonomous-research mixin.

Research:

• Customer insights: jobs-to-be-done, pain points, desired outcomes in the target market
• Market context: industry dynamics, customer expectations, unmet needs
• Competitor value propositions: how competitors address similar customer segments
• Domain-specific knowledge: typical customer workflows, decision criteria, buying behavior

Phase 3 — Customer Segment Selection

Identify target segments using one of:

• Output from customer-segmentation skill (if provided)
• Personas from persona-management (if provided)
• Research-based inference from Phase 2

One canvas will be produced per segment. For multi-segment analysis, confirm segment list with user before proceeding.

Present:

**Segments to analyze**:
1. [Segment name] — [brief description]
2. [Segment name] — [brief description]
...

Ask user to confirm, add, or remove segments.

Phase 4 — Customer Profile

For each segment, build the Customer Profile side of the canvas.

Customer Jobs

Identify 8-12 customer jobs across four types:

• Functional: Tasks customers are trying to perform or complete
• Social: How customers want to be perceived by others
• Emotional: Feelings customers seek or want to avoid
• Supporting: Roles customers play in related contexts (buying, co-creating, transferring)

Prioritize the top 5 by importance to the customer.

ID	Job	Type	Priority	Evidence
J1	[description]	Functional / Social / Emotional / Supporting	1-5	[source or assumption label]

Pains

Identify 8-12 pains across three categories:

• Undesired outcomes: Functional, social, emotional problems
• Obstacles: Things preventing job completion
• Risks: Potential negative consequences

Rank by severity: Extreme / High / Moderate.

ID	Pain	Category	Severity	Evidence
P1	[description]	Undesired outcome / Obstacle / Risk	Extreme / High / Moderate	[source or assumption label]

Prioritize the top 5 by severity.

Gains

Identify 8-12 gains across four types:

• Required: Minimum expectations without which the solution fails
• Expected: Gains customers assume they will get
• Desired: Gains customers would love but do not expect
• Unexpected: Gains beyond customer imagination

Rank by relevance: Essential / Important / Nice-to-have.

ID	Gain	Type	Relevance	Evidence
G1	[description]	Required / Expected / Desired / Unexpected	Essential / Important / Nice-to-have	[source or assumption label]

Prioritize the top 5 by relevance.

Phase 5 — Value Map

For each segment, build the Value Map side of the canvas.

Products & Services

List the products and services offered, classified by type:

• Physical: Tangible goods
• Digital: Software, apps, platforms
• Intangible: Services, consulting, support
• Financial: Financial products, insurance, financing

Rank by importance to the value proposition.

ID	Product/Service	Type	Importance
PS1	[description]	Physical / Digital / Intangible / Financial	High / Medium / Low

Pain Relievers

Describe how each product/service alleviates specific prioritized pains. Map 1:1 where possible.

ID	Pain Reliever	Addresses Pain	Strength
PR1	[description]	P[n]	Strong / Moderate / Weak

Gain Creators

Describe how each product/service produces specific prioritized gains. Map 1:1 where possible.

ID	Gain Creator	Addresses Gain	Strength
GC1	[description]	G[n]	Strong / Moderate / Weak

Phase 6 — Fit Assessment

For each segment, assess problem-solution fit.

Pain Coverage

Map each pain reliever to its corresponding pain:

Pain	Pain Reliever	Fit Strength
P[n]: [description]	PR[n]: [description]	Strong / Moderate / Weak / Unaddressed

Gain Coverage

Map each gain creator to its corresponding gain:

Gain	Gain Creator	Fit Strength
G[n]: [description]	GC[n]: [description]	Strong / Moderate / Weak / Unaddressed

Scoring

• Pain coverage: % of prioritized pains addressed (at least Weak strength)
• Gain coverage: % of prioritized gains addressed (at least Weak strength)
• Fit level: High (>70% coverage), Medium (40-70%), Low (<40%)
• Gaps: Unmatched pains (no pain reliever)
• Over-delivery: Gain creators that address non-prioritized or unexpected gains
• Overall fit score: 0-100, weighted: pain coverage 60%, gain coverage 40%

Phase 7 — Competitive Comparison

If competitors were identified in Phase 2:

• Brief competitor value map comparison (how competitors address the same customer jobs/pains/gains)
• Differentiation opportunities (where the subject's value proposition is unique or stronger)
• Competitive gaps (where competitors outperform)

If no competitors identified, skip this phase and note it.

Phase 8 — Diagrams

Generate 3 Mermaid diagrams per segment:

1. Canvas Overview (mindmap)

mindmap
  root((Value Proposition Canvas\n[Segment]))
    Customer Profile
      Jobs
        J1: [job]
        J2: [job]
        ...
      Pains
        P1: [pain]
        P2: [pain]
        ...
      Gains
        G1: [gain]
        G2: [gain]
        ...
    Value Map
      Products & Services
        PS1: [product]
        PS2: [product]
        ...
      Pain Relievers
        PR1: [reliever]
        PR2: [reliever]
        ...
      Gain Creators
        GC1: [creator]
        GC2: [creator]
        ...

2. Fit Mapping (flowchart)

flowchart LR
    subgraph Pain_Relievers
        PR1["PR1: ..."]
        PR2["PR2: ..."]
    end
    subgraph Pains
        P1["P1: ..."]
        P2["P2: ..."]
    end
    subgraph Gain_Creators
        GC1["GC1: ..."]
        GC2["GC2: ..."]
    end
    subgraph Gains
        G1["G1: ..."]
        G2["G2: ..."]
    end
    PR1 -->|Strong| P1
    PR2 -->|Moderate| P2
    GC1 -->|Strong| G1
    GC2 -->|Weak| G2

Use link labels to indicate fit strength. Use dashed links for Weak, normal for Moderate, thick for Strong.

3. Fit Score Chart (xychart-beta)

xychart-beta
    title "Fit Score by Segment"
    x-axis ["Segment 1", "Segment 2", ...]
    y-axis "Fit Score (0-100)" 0 --> 100
    bar [score1, score2, ...]

Render all diagrams per the diagram-rendering mixin.

File naming:

• canvas-overview-[segment].mmd / .png
• fit-mapping-[segment].mmd / .png
• fit-score-chart.mmd / .png

Phase 9 — Recommendations

Prioritized actions based on findings:

• Gaps to address: Prioritized pains/gains with no or weak coverage
• Over-deliveries to evaluate: Gain creators addressing non-prioritized gains — consider whether to keep, reduce, or reposition
• Iteration suggestions: How to improve fit score per segment
• Competitive moves: Actions based on competitive comparison (if applicable)

Every recommendation must trace back to a specific finding from the analysis.

Phase 10 — Report Assembly and Approval

Assemble the complete report. Present for user approval. Save only after explicit confirmation.

Generation rules

Per the autonomous-research mixin, plus:

• Customer jobs: Describe in the customer's language, not the provider's
• Specificity: "Save 3 hours/week on report generation" not "improve efficiency"
• Language: Respond and generate in the user's language unless specified otherwise

Failure behavior

Situation	Behavior
No product/service context	Enter interview mode — ask what product or service to analyze
Context too vague	Enter interview mode — ask targeted questions to narrow scope
No customer data available	Use research-based inference with [Assumption] labels
Single segment only	Produce one canvas, note limitation in report
Cannot identify competitors	Skip competitive comparison, note in report
Insufficient research data	Produce partial output, label gaps and low-confidence findings
mmdc / web search failures	Per diagram-rendering and autonomous-research mixins
User provides conflicting scope	Present the conflict, ask user to resolve
Out-of-scope request	"This skill creates Value Proposition Canvases. [Request] is outside scope."

Self-check

Before presenting output, verify:

[] Customer Profile complete for each segment (jobs, pains, gains with types and priorities)
[] Value Map complete for each segment (products, pain relievers, gain creators)
[] Fit assessment with coverage scores for each segment
[] Gaps and over-deliveries identified
[] Fit score calculated (0-100)
[] Recommendations traced to specific gaps/findings
[] All Mermaid diagrams render valid syntax (per diagram-rendering mixin)
[] Sources listed (per autonomous-research mixin)
[] Assumptions labeled (per autonomous-research mixin)