value-proposition-designer
Skill3 starsChanged 4 months agoMITView on GitHub
Design a validated Value Proposition Canvas that maps customer jobs, pains, and gains to product pain relievers and gain creators, with an explicit fit assessment. Use when working on product discovery, PRDs, user stories, OKRs, or backlog planning.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- imohammadfaraz/claude-skills-for-product-managertheir own version, first seen Nov 1, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- imohammadfaraz/claude-skills-for-product-manager★ 19same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository59 other files it gives its agents.
- agent-memory-designerSkill
- ai-cost-optimizerSkill
- ai-evaluation-builderSkill
- ai-latency-optimizerSkill
- ai-solution-architectSkill
- ai-workflow-builderSkill
- context-engineeringSkill
- human-in-the-loop-designerSkill
- multi-agent-plannerSkill
- prompt-engineerSkill
- prompt-optimizerSkill
- prompt-reviewerSkill
- adr-generatorSkill
- api-designerSkill
- architecture-reviewSkill
- component-diagram-builderSkill
- container-diagram-builderSkill
- data-flow-analyzerSkill
- integration-plannerSkill
- observability-plannerSkill
- scalability-advisorSkill
- solution-architectureSkill
- system-context-builderSkill
- experiment-designerSkill
- go-to-market-plannerSkill
- pricing-strategy-advisorSkill
- mcp-best-practicesSkill
- mcp-client-generatorSkill
- mcp-security-reviewSkill
- mcp-server-generatorSkill
- mcp-tool-generatorSkill
- acceptance-criteria-generatorSkill
- analytics-instrumentation-plannerSkill
- competitive-analysisSkill
- epic-generatorSkill
- feature-prioritizationSkill
- idea-validatorSkill
- north-star-metric-advisorSkill
- okr-builderSkill
- persona-generatorSkill
- prd-generatorSkill
- problem-statement-builderSkill
- product-discovery-assistantSkill
- product-vision-builderSkill
- sprint-plannerSkill
- story-mappingSkill
- user-story-generatorSkill
- chunking-strategy-advisorSkill
- embedding-strategy-advisorSkill
- hybrid-search-advisorSkill
- knowledge-ingestion-plannerSkill
- rag-architecture-designerSkill
- retriever-optimizerSkill
- ai-governance-advisorSkill
- ai-threat-modelingSkill
- guardrails-builderSkill
- owasp-llm-reviewerSkill
- prompt-injection-detectorSkill
- skill-nameSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
If segment is vague, stop and request persona detail or run persona-generator.
- Distinctive: line 136
Start from jobs, not features.
- Distinctive: line 137
Use customer language from interviews, not internal jargon.
- Distinctive: line 140
Treat canvas as living artifact.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Value Proposition Designer
Purpose
Design a validated Value Proposition Canvas that maps customer jobs, pains, and gains to product pain relievers and gain creators, with an explicit fit assessment.
Input: Persona, problem statement, product concept (optional), competitive context (optional)
Output: Value Proposition Canvas document with fit score and prioritized value hypotheses
Examples: See examples.md for worked input/output.
Workflow
Step 1: Define customer segment
Confirm:
- Primary persona or segment (one canvas per segment)
- Context of use (when/where the product is considered)
- Current alternatives (manual workarounds, competitors, status quo)
If segment is vague, stop and request persona detail or run persona-generator.
Step 2: Build customer profile
Capture three blocks from the customer's perspective:
Jobs — functional, social, emotional tasks they try to accomplish:
| Type | Job | Importance (H/M/L) |
Pains — obstacles, risks, frustrations before/during/after jobs:
| Pain | Severity (H/M/L) | Frequency |
Gains — desired outcomes, benefits, aspirations:
| Gain | Relevance (H/M/L) | Expected vs unexpected |
Prioritize top 3 jobs, top 3 pains, top 3 gains.
Step 3: Build value map
Map product offering to customer profile:
Products & services — what the offering includes (capabilities, not marketing copy).
Pain relievers — how each relieves specific pains:
| Pain relieved | How | Evidence |
Gain creators — how each creates specific gains:
| Gain created | How | Evidence |
Each pain reliever and gain creator must link to at least one customer profile item.
Step 4: Assess fit
Evaluate Problem-Solution Fit and Product-Market Fit signals:
| Dimension | Score (1–5) | Rationale |
| Job relevance | ||
| Pain coverage | ||
| Gain delivery | ||
| Differentiation vs alternatives |
Flag gaps: pains without relievers, gains without creators, relievers without linked pains.
Step 5: Prioritize value hypotheses
Rank top 3 value hypotheses to validate:
| Hypothesis | Customer belief | How to test | Success signal |
Step 6: Validate
Run Validation checklist.
Decision Rules
| Condition | Action |
| No persona or segment defined | Stop; request persona or run persona-generator |
| Multiple segments in one canvas | Split into separate canvases per segment |
| Pain reliever not linked to a pain | Remove or map to specific pain |
| Gain creator describes feature, not outcome | Rewrite as customer benefit |
| All pains marked low severity | Re-interview segment or narrow scope |
| Value map mirrors competitor 1:1 | Articulate differentiation in gain creators |
Validation
- One primary customer segment identified
- Customer profile has jobs, pains, gains with priorities
- Value map has products/services, pain relievers, gain creators
- Every reliever/creator links to a profile item
- Fit assessment completed with scores and gaps noted
- Top 3 value hypotheses with test methods defined
- Differentiation vs alternatives stated explicitly
Anti-patterns
- Feature list as value map — listing capabilities without linking to pains/gains.
- Generic pains — "bad UX", "too expensive" without segment-specific context.
- Orphan relievers — pain relievers that don't address ranked pains.
- One canvas for all segments — enterprise and SMB mixed in one profile.
- Assumed fit — high fit scores without evidence or test plan.
Best Practices
- Start from jobs, not features; jobs survive product pivots.
- Use customer language from interviews, not internal jargon.
- Rank pains by severity × frequency, not gut feel alone.
- Compare value map to top 2 alternatives explicitly.
- Treat canvas as living artifact; update after each discovery sprint.
Output Structure
# Value Proposition Canvas: [Segment] — [Product]
## Customer Profile
### Jobs (top 3)
| Job | Type | Importance |
|-----|------|------------|
### Pains (top 3)
| Pain | Severity | Frequency |
|------|----------|-----------|
### Gains (top 3)
| Gain | Relevance | Notes |
|------|-----------|-------|
## Value Map
| Pain | Reliever | How |
|------|----------|-----|
| Gain | Creator | How |
|------|---------|-----|
## Fit Assessment
| Dimension | Score | Gaps |
|-----------|-------|------|
**Overall fit:** [Problem-Solution / PMF signal stage]
## Value Hypotheses & Differentiation
| # | Hypothesis | Test | Success signal |
|---|------------|------|----------------|
[vs alternative 1, alternative 2]Next Skills
| Outcome | Recommended Skill |
| Fit validated, need vision | product/product-vision-builder |
| Prioritize features from value map | product/feature-prioritization |
| Test assumptions before build | product/idea-validator |
| Write requirements for MVP | product/prd-generator |
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

