LEANSpark (leanspark.ai) platform help — an AI startup-validation system by Ash Maurya, creator of the Lean Canvas, built on Lean Canvas + Continuous-Innovation PDCA cycles, not a one-shot score: a free Lean Canvas builder stress-tested across seven dimensions, persistent memory, a Contradiction Detection engine that flags where plan and evidence stop agreeing, and two-week Plan-Do-Check-Act cycles ending in a Persevere / Pivot / Pause call. Exposes an OAuth MCP server (leanspark.ai/mcp) that reads your validated canvas, positioning, and voice profile into Claude Code or Cursor. Use when building or stress-testing a Lean Canvas, acting on a flagged plan-vs-evidence contradiction, deciding persevere/pivot/pause, setting up the MCP server, weighing free tier vs a paid credits plan, or asking whether it has a REST API. Credit-metered; no REST API. Do NOT use for comparing idea validators or the validate-before-building method (use /sales-idea-validation), or the smoke-test landing page (use /sales-funnel).
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
LEANSpark (leanspark.ai) platform help — an AI startup-validation system by Ash Maurya, creator of the Lean Canvas, built on Lean Canvas + Continuous-Innovation PDCA cycles, not a one-shot score: a free Lean Canvas builder stress-tested across seven dimensions, persistent memory, a Contradiction Detection engine that flags where plan and evidence stop agreeing, and two-week Plan-Do-Check-Act cycles ending in a Persevere / Pivot / Pause call. Exposes an OAuth MCP server (leanspark.ai/mcp) that reads your validated canvas, positioning, and voice profile into Claude Code or Cursor. Use when building or stress-testing a Lean Canvas, acting on a flagged plan-vs-evidence contradiction, deciding persevere/pivot/pause, setting up the MCP server, weighing free tier vs a paid credits plan, or asking whether it has a REST API. Credit-metered; no REST API. Do NOT use for comparing idea validators or the validate-before-building method (use /sales-idea-validation), or the smoke-test landing page (use /sales-funnel).
argument-hint
[describe what you need help with in LEANSpark]
license
MIT
version
1.0.0
tags
["sales","idea-validation","platform"]
LEANSpark Platform Help
LEANSpark (leanspark.ai) is "the decision layer for founders" — an AI startup-validation system
by Ash Maurya (creator of the Lean Canvas, author of Running Lean), built on his Lean Canvas +
Continuous-Innovation framework. Unlike the one-shot "score my idea" validators, it keeps persistent
memory of your model, interviews, and experiments, runs two-week Plan-Do-Check-Act cycles over
~90 days, and has a Contradiction Detection engine that flags where your plan and your evidence stop
agreeing — ending each sprint in a Persevere / Pivot / Pause decision. It also exposes an OAuth
MCP server so Claude Code / Cursor can read your validated model.
Step 1 — Gather context
If references/learnings.md exists, read it first for accumulated platform knowledge.
Ask only what you can't infer from the user's prompt:
Where are you? (a) building/stress-testing a Lean Canvas, (b) staring at a seven-dimension result
or a flagged contradiction and unsure what to do, (c) mid-cycle and deciding persevere/pivot/pause,
(d) wiring the MCP server into Claude Code / Cursor, (e) weighing free tier vs a paid credits plan,
(f) asking whether it has a REST API / how to get data out.
What's the idea, in one sentence? (helps interpret the canvas and the seven-dimension stress test).
What evidence have you got so far — a hunch, customer interviews, an experiment result, a landing
page, a waitlist, or someone offered to pay?
Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.
Step 2 — Route or answer directly
LEANSpark is one validation system. Much of the thinking a founder needs is tool-agnostic — route it:
The user's real question
Route to
Should I trust this? How do I actually validate? The evidence ladder, comparing validators
/sales-idea-validation
Build the smoke-test / fake-door landing page the demand test runs on
/sales-funnel
Grow a pre-launch waitlist and capture signups
/sales-audience-growth
Run the ~10 customer interviews that feed the evidence side
/sales-idea-validation
Reddit-based real-demand signal to cross-check the model
/sales-trendseeker
When routing, give the exact command: "This is a validation-method question — run: /sales-idea-validation {the user's original question}"
Answer directly only for LEANSpark-specific mechanics (the 12-block canvas, the seven dimensions,
the contradiction engine, PDCA cycles, persevere/pivot/pause, credits & modes, the MCP server).
Step 3 — LEANSpark platform reference
Read references/platform-guide.md for the full reference — the 12-block canvas and seven-dimension
stress test, the persistent-memory / contradiction-detection / PDCA loop, credits & the Reactive /
Attentive / Proactive modes, pricing/plan gates, the MCP server (setup, scopes, the one write-back tool),
and how to get data out. Read references/leanspark-api-reference.md for the MCP surface verbatim.
Answer using only the relevant section — don't dump the whole guide.
Step 4 — Actionable guidance
The canvas, seven-dimension score, and contradiction flags are coaching over your inputs — not demand.
LEANSpark's edge over pure-LLM validators is that it's built around running real experiments and
interviews and tracking that evidence over 90 days — but a well-organized canvas with no real customer
evidence fed in is still just a plan. Keep the structure and the flagged contradictions; take the
go/no-go from a real behavior test (smoke test / pre-sale). Route that to /sales-funnel + /sales-audience-growth.
The Contradiction Detection engine only checks things you told it — it can't see that your evidence base is imaginary.
It flags where your plan and your logged evidence disagree (e.g. positioning vs. interview feedback),
which is genuinely useful — but "no contradictions" means your inputs are internally consistent, not
that strangers want it. Treat a flagged contradiction as your next thing to go resolve with real customers.
Feed it real evidence, then let it compound. Its whole value proposition is persistent memory —
log actual interviews and experiment results, not guesses. Tell the user the system is only as good as
the evidence they put in; thin or invented inputs yield confident but empty coaching.
Read persevere/pivot/pause as a cycle verdict, not a final grade. Each two-week PDCA cycle ends in
Persevere (evidence supports the current model — keep testing), Pivot (a core assumption failed — change
the model), or Pause (stop/park). It's a checkpoint against pre-set success criteria, so anchor it on
whether the experiment hit its threshold, not on how the AI "feels" about the idea.
Present all pricing as best-effort, and explain credits + modes. Plans are credit-metered (Free
~25 credits/mo, paid tiers up to ~1,600) and pricing may have changed — point to leanspark.ai/pricing.
The "mode" scales with tier: Reactive (you drive) → Attentive (proactive nudges) → Proactive
(daily briefings, autonomous campaigns, background watchers). The free Lean Canvas builder is usable
without a card.
The MCP server is the only programmatic surface — there's no REST API or webhooks. To use LEANSpark
data in Claude Code: claude mcp add --transport http leanspark https://leanspark.ai/mcp, then authorize
via OAuth in the browser and pick which idea to expose. It's read-mostly (validated canvas,
positioning, competitive evidence, voice profile) plus one write-back tool to save deliverables into
your active experiment; each connection is scoped to a single idea and revocable in Settings → MCP.
Don't confuse it with the free five-minute canvas generators or one-shot scorers. LEANSpark is
deliberately a longitudinal 90-day processbuilt by Ash Maurya, the creator of the Lean Canvas
(persistent memory + two-week PDCA cycles + contradiction detection), not a 60-second verdict — and it's
credit-metered (always present its pricing as best-effort and point to leanspark.ai/pricing). When a
user weighs it against a fast/free canvas generator, lead with that longitudinal-vs-one-shot contrast and
the creator-of-the-Lean-Canvas authenticity, note the free Lean Canvas builder needs no card, and point
them to /sales-idea-validation for the full comparison.
If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.
Gotchas
Best-effort from research (2026-07) — the AI-validator tool set churns fast; verify tiers, the modes, credits, and the MCP scopes at leanspark.ai.
A tidy canvas is not validated demand. Even from Ash Maurya, the creator of the Lean Canvas, LEANSpark
coaches and tracks your evidence — it doesn't manufacture demand. Follow every green light with a real-behavior test.
Contradiction detection is bounded by your inputs. It surfaces conflicts between what you logged;
it can't tell you that the whole evidence base is thin or imagined. Garbage in, confident coaching out.
It's a 90-day commitment, not an instant score. The value is the persistent memory + PDCA cadence
over weeks; a founder wanting a one-shot number will find the deliberate pace a mismatch.
Credit-metered with tiered "modes." Free is ~25 credits/mo in Reactive mode; proactive/autonomous
behavior (daily briefings, background watchers) is top-tier only. Confirm current credits/prices at leanspark.ai/pricing.
No REST API or webhooks — MCP only. The automation surface is the OAuth MCP server (read-mostly +
one write-back tool), scoped to one idea. Don't plan a REST integration; there isn't one.
Persevere/Pivot/Pause depends on pre-set success criteria. If you don't define what "success" for a
cycle looks like up front, the verdict drifts toward an AI opinion — set the threshold before the sprint.
Related skills
/sales-idea-validation — the tool-agnostic validate-before-building method and the full idea-validator comparison (start here to decide whether to trust any score or system)
/sales-funnel — build the smoke-test / fake-door landing page the real demand test runs on
/sales-audience-growth — grow a pre-launch waitlist and capture signups once you're testing demand
/sales-trendseeker — Reddit-based real-demand signal to cross-check LEANSpark's model
/sales-juma — the other tool in this cluster with a callable MCP server, if you're comparing MCP surfaces
/sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do -a claude-code
Examples
Example 1: "LEANSpark flagged a contradiction between my plan and my interviews — now what?"
User says: "The Contradiction Detection thing says my positioning conflicts with my customer feedback. What do I do?"
Skill does: Explains the engine compares what you logged (positioning vs. interview evidence) and that
the flag is your top thing to go resolve with real customers — not a verdict on the idea. Suggests re-running
the affected experiment with a sharper customer/problem, and reminds the user that "no contradiction" only
means internal consistency, not demand — the go/no-go still comes from a smoke test / pre-sale
(/sales-funnel + /sales-audience-growth).
Result: The founder treats the contradiction as a concrete next experiment instead of a pass/fail grade.
Example 2: "Is LEANSpark worth it over a free five-minute lean-canvas generator?"
User says: "I can get a lean canvas in five minutes elsewhere for free — why pay for LEANSpark?"
Skill does: Frames LEANSpark as a longitudinal system (persistent memory + two-week PDCA cycles +
contradiction detection over ~90 days from the Lean Canvas creator), not a one-shot canvas or score — so
its value only shows up if you actually run experiments and log evidence over weeks. Notes the free Lean
Canvas builder is usable without a card, explains credits/modes as best-effort, and points to
/sales-idea-validation to compare it against the fast/free scorers.
Result: The user picks based on whether they'll commit to the process, not the price tag.
Example 3 (automation): "Can I pull my LEANSpark model into Claude Code — is there an API?"
User says: "I want my validated canvas and positioning available in Claude Code / Cursor. Does LEANSpark have an API or webhook?"
Skill does: States there's no REST API or webhooks — the only programmatic surface is the OAuth
MCP server: claude mcp add --transport http leanspark https://leanspark.ai/mcp, then authorize in the
browser and select which idea to expose. Explains it's read-mostly (validated canvas, positioning,
competitive evidence, voice profile) plus one write-back tool to save a deliverable into the active
experiment, scoped to one idea and revocable in Settings → MCP — so you can generate on-brand copy grounded
in the validated model, but you can't script a full data export.
Result: The user connects the MCP server and reads their model into Claude Code instead of hunting for a REST endpoint.
Troubleshooting
The seven-dimension result / coaching feels generic
Symptom: The stress test or the AI's guidance reads bland and could apply to any idea.
Cause: LEANSpark coaches over the evidence you've logged; with only a one-line idea and no real
interviews or experiments in memory, it has nothing specific to reason from.
Solution: Feed it real evidence — log actual customer interviews and experiment results, answer the
canvas blocks concretely (especially early adopters and the problem), and let the persistent memory
compound over a cycle. Then cross-check the model against a real-demand signal (/sales-trendseeker) and
a smoke test.
"I connected the MCP server but can't get my full data out"
Symptom: The MCP tools read the model but there's no bulk export / no REST endpoint.
Cause: LEANSpark is UI + MCP only — the MCP server is read-mostly with a single write-back tool, and
there is no public REST API or webhooks.
Solution: Use the MCP reads for what they're for (grounding content/positioning in Claude Code/Cursor)
and the one write-back tool to save deliverables into your active experiment. For anything the MCP doesn't
expose, copy from the UI. Don't design a pipeline around a REST API that doesn't exist.
Ran out of credits / confused by Reactive vs Proactive
Symptom: The tool stops doing work, or the "mode" behaves differently than expected.
Cause: Plans are credit-metered and the mode is tier-gated — Free (~25 credits/mo) is Reactive
(you initiate); proactive nudges and autonomous/always-on behavior are higher tiers only.
Solution: Confirm current credits and tier behavior at leanspark.ai/pricing. Budget credits for real
work (logging evidence, running cycles) rather than idle chat, and upgrade only if you genuinely want the
Attentive/Proactive daily-coaching behavior — the free Lean Canvas builder needs no card.
