Value proposition canvas
Agent-based startup coaching skills for Claude Code — every claim labeled FACT/ASSUMPTION/UNKNOWN. Zero-hallucination protocol, 10 skills, one master orchestrator.
npx -y skills add ajstars1/startup-coach --skill value-proposition-canvasAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Value Proposition Canvas (Osterwalder, Pigneur, Bernarda, Smith — Strategyzer) — map customer jobs, pains, and gains against your pain relievers, gain creators, and products/services, then test for fit. Use when shaping what to build, writing positioning/messaging, deciding an MVP, or when a founder can list features but not the customer outcome they serve.
SKILL.md
3.7 KB, as published. Nobody here has run it
Value Proposition Canvas (VPC)
Credits: Alexander Osterwalder, Yves Pigneur, Greg Bernarda, Alan Smith — Value Proposition Design (strategyzer.com; the canvas and first chapter are free there). Ranking discipline and hypothesis framing — Steve Blank.
The canvas has two sides. Fit is the claim; evidence is the test.
Right side — Customer Profile (about THEM, drafted from evidence)
- Customer jobs — functional, social, and emotional jobs they're trying to get done (import from
/jtbdif run). Their words, their situation, not your solution. - Pains — bad outcomes, obstacles, and risks around those jobs: what costs too much time/money, what feels risky, what keeps failing.
- Gains — outcomes and benefits they want: required, expected, desired, and unexpected gains.
Rank pains and gains by intensity — with customers where possible. AI/founder rankings are placeholders: label them [ASSUMPTION — rank-test in interviews]. Interview evidence gets quoted; everything else is labeled.
Left side — Value Map (about YOU, drafted as hypotheses)
- Products & services — what you offer (the list, not the pitch).
- Pain relievers — exactly which top-ranked pains each element eliminates or reduces. Not all pains — the ones that matter.
- Gain creators — exactly which top-ranked gains each element produces.
Fit — the honest test
Fit exists when your top pain relievers and gain creators map to the customer's top-ranked pains and gains — as ranked by customers, not by you. Three levels: on paper (problem-solution fit, all assumption), in market (customers respond — early evidence), at scale (they pay repeatedly — product-market fit). State plainly which level the canvas is at.
Common failure modes to flag:
- Feature-first fit: value map written first, customer profile reverse-engineered to justify it. (Tell: every feature magically maps to a "top" pain.)
- Pain inflation: mild inconveniences promoted to burning pains without evidence. If a launch or landing page already got silence, say what that does/doesn't imply.
- Unranked everything: 15 pains all "important" = nothing ranked = nothing testable.
MVP linkage
The MVP is the smallest thing that tests the riskiest fit claim — usually top pain ↔ top pain reliever. A concierge/manual version of the service is often stronger evidence than software (real transaction, zero build). Specify: the claim under test, the MVP form, the pass/fail signal, and what you deliberately did NOT build.
Output
Both sides of the canvas in markdown, every entry labeled [FACT — quote/source] / [ASSUMPTION] / [UNKNOWN], pains/gains ranked with ranking provenance stated, the fit claim + current fit level, the MVP proposal, and a "Validate next" list routed to /customer-discovery. Get the printable canvas from Strategyzer for workshops.
Ledger discipline (v2): reference Belief Ledger IDs ([A4], [F7]) for every claim; propose new/changed items in a closing "Ledger delta" section rather than restating claims — restated claims drift.
Fill-in templates: TEMPLATES.md in this skill's folder — copy the structures into your project's discovery/ and fill with labels.