ValidateThat (validatethat.io) platform help — a low-cost, self-serve UX-research and idea-validation micro-tool: an idea-validation engine (fast market verdict + competitor read + research plan) plus card sorting, tree testing, first-click/prototype tests, surveys, and interviews, with studies embeddable on your own site, Prolific recruitment, a Figma plugin, and — rare for this class — an MCP server to run research from Claude or Cursor. Use when running a card sort or tree test on a budget, validating a product idea before building, embedding a study on your website, recruiting testers via Prolific, reading a similarity matrix or dendrogram, choosing between its Free, Starter, and Pro tiers, setting up its MCP server, or comparing it with Optimal Workshop, UXtweak, or Lyssna as a cheaper option. Do NOT use for the tool-agnostic validate-before-building method or comparing research tools across the market (use /sales-idea-validation), or an API/webhook-native research pipeline (use /sales-userintuition).
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name
sales-validatethat
description
ValidateThat (validatethat.io) platform help — a low-cost, self-serve UX-research and idea-validation micro-tool: an idea-validation engine (fast market verdict + competitor read + research plan) plus card sorting, tree testing, first-click/prototype tests, surveys, and interviews, with studies embeddable on your own site, Prolific recruitment, a Figma plugin, and — rare for this class — an MCP server to run research from Claude or Cursor. Use when running a card sort or tree test on a budget, validating a product idea before building, embedding a study on your website, recruiting testers via Prolific, reading a similarity matrix or dendrogram, choosing between its Free, Starter, and Pro tiers, setting up its MCP server, or comparing it with Optimal Workshop, UXtweak, or Lyssna as a cheaper option. Do NOT use for the tool-agnostic validate-before-building method or comparing research tools across the market (use /sales-idea-validation), or an API/webhook-native research pipeline (use /sales-userintuition).
argument-hint
[describe what you need help with in ValidateThat]
license
MIT
version
1.0.2
tags
["sales","pre-launch","platform"]
ValidateThat Platform Help
ValidateThat (validatethat.io) is a low-cost, self-serve UX-research + idea-validation micro-tool —
the budget, indie answer to Optimal Workshop / UXtweak. It bundles an Idea-Validation Engine (idea →
market verdict + competitor breakdown + prioritized research plan) with the full battery: card sorting
(open/closed/hybrid → similarity matrix / dendrogram), tree testing, first-click / prototype tests,
surveys, and interviews — plus studies you embed on your own site (one-line snippet),
Prolific recruitment, and a Figma plugin (frame → card sort). Its one genuinely differentiating
surface in this cluster: an MCP server ("run UX research from a chat" in Claude / Cursor) — every peer
here (Optimal Workshop, UXtweak, Lyssna, Useberry) is UI-only with no automation surface at all.
Two things to say almost every time:
A clean card sort or tree test proves the structure works, not that anyone will pay — and the Idea-Validation Engine's verdict is an AI opinion, not demand. If the user is still deciding whether to build, keep the findings/research plan but take the go/no-go to a real behavior test via /sales-idea-validation.
ValidateThat has NO public REST API or webhooks. The only programmatic surface is the MCP server (plus the Figma plugin and embed snippet); a "pipe results into my CRM/warehouse" ask is MCP-from-an-agent or manual CSV export (Starter+), not REST — for a documented pipeline, route to /sales-userintuition.
Step 1 — Gather context
If references/learnings.md exists, read it first for accumulated platform knowledge.
Ask only what you can't infer:
What do you want from ValidateThat?
A) Validate an idea — the Idea-Validation Engine (market verdict + competitor read + research plan)
B) Pick/run a study — card sort vs tree test vs first-click/prototype vs survey vs interview
C) Embed a study on my site — the one-line snippet
D) Recruit participants — via Prolific ($40 free credit) vs sharing a link to your own users
E) Automate — the MCP server (Claude/Cursor) or (no REST API)
CSV export
F) Choose — ValidateThat vs Optimal Workshop / UXtweak / Lyssna, or Free vs Starter vs Pro
Are you testing something you've already built/designed, or still deciding whether to build? The
second is an idea-validation question — flag it in Step 2.
Skip-ahead: if the prompt already names the study type or the question is specific, go to Step 3.
Step 2 — Route or answer directly
If the user's question is about…
Route to
The tool-agnostic validate-before-building method, or comparing research/idea tools across the whole market
/sales-idea-validation {question}
A documented API / webhook-native research or interview pipeline (ValidateThat has none)
/sales-userintuition {question}
Running a real behavior demand test (smoke-test page, waitlist, pre-sale) instead of an IA/usability test
/sales-idea-validation or /sales-funnel{question}
The deep-IA reference-standard peer (benchmark card-sort/tree-test analysis)
/sales-optimalworkshop {question}
The broad self-serve all-in-one usability peer (own panel, session recording)
/sales-uxtweak {question}
Recruiting participants at scale via the Prolific panel itself (API/webhooks/CLI)
/sales-prolific {question}
When routing, give the exact command: "This is a {domain} question — run: /sales-idea-validation {original question}"
Otherwise, answer ValidateThat-specific questions using Step 3.
Step 3 — ValidateThat platform reference
Read references/platform-guide.md for the full reference — the module/automation-surface table (what's
MCP-accessible, embed-accessible, or UI-only), best-effort pricing and plan gates (which research methods are
Pro-gated, the free-tier 3-study limit, Prolific recruitment cost), the Idea → Study → Participant → Response
data model with JSON shapes, the MCP-server setup + Figma-plugin + embed-snippet recipes, and the no-REST-API
data-out playbook. ValidateThat has no public REST API, so there is no API-reference file.
Answer using only the relevant section — don't dump the full reference.
Step 4 — Actionable guidance
Say the caveat: an IA/usability result — and the AI verdict — is not demand. Make explicit that a card
sort / tree test proves a structureworks (findability, category agreement) and the Idea-Validation
Engine's verdict is an AI opinion; neither proves strangers will pay. If they're still deciding
whether to build, keep the findings but take the go/no-go from a real behavior test (smoke test, pre-sale)
via /sales-idea-validation.
Flag it as MCP-only when any automation/export comes up. ValidateThat has no public REST API or
webhooks (verify — it may change). The programmatic surface is the MCP server (run/list studies, pull
stats from Claude/Cursor), the Figma plugin (frame → card sort), and the on-site embed snippet;
bulk data-out is CSV export (Starter+). "Sync results to HubSpot/Snowflake on a schedule" is MCP-from-an-agent
or a scripted CSV job, not REST — if a documented pipeline is required, route to /sales-userintuition.
Match the tool to the question. Recommend by job: card sort = how users group/label content (open =
discover, closed = validate → similarity matrix / dendrogram); tree test = can users find things in
your nav (findability, no visuals); first-click / prototype test = where users click first; survey /
interview = attitudes and why (Pro-gated). Design the tree with a card sort, then validate it with a tree test.
Steer recruitment by budget. Recruiting via Prolific is billed per response (~$3.50, best-effort;
a $40 free credit seeds every account) — for a handful of testers, sharing a study link to your own
users is free. Present the Prolific rate and the free credit as best-effort.
Warn about the plan gates before they hit them.Free = 3 studies (analytics limited to the first ~3
respondents); Starter unlocks unlimited card sorts / tree tests / first-click + full analytics + AI
insights + CSV export + hide-branding; surveys, interviews, and competitor analysis are Pro+. Present all
pricing as best-effort and point to validatethat.io/pricing.
Set participant counts realistically. Quantitative IA (card sort / tree test) needs ~30+ for a stable
similarity matrix / findability score; qualitative (interviews) surfaces most themes with a handful. Say it's
a rule-of-thumb, not a guarantee.
If you discover a gotcha or tip not in references/learnings.md, append it there with today's date.
Gotchas
Best-effort from research (2026-07) — pricing, the Pro-gated method split, the Prolific per-response rate and
free credit, and the exact MCP-server config move; verify at validatethat.io.
IA/usability success — and the AI verdict — ≠ demand. ValidateThat proves a structure/design works and
the engine gives an AI opinion; the build-or-not go/no-go still belongs to a real behavior test. Route that
to /sales-idea-validation.
No REST API, no webhooks. The only automation surfaces are the MCP server, the Figma plugin, and
the on-site embed snippet; bulk data-out is CSV export (Starter+). Don't design a live REST/webhook
pipeline around it — route pipeline needs to /sales-userintuition.
The exact MCP config is not in public docs. ValidateThat's site confirms the MCP server (Claude Desktop /
claude.ai / Claude Code) but the marketing pages are JS-rendered; don't invent an endpoint URL, auth method,
or tool names — have the user copy the connect string from validatethat.io's integrations/settings page and
discover the tool list at runtime.
Surveys and interviews are Pro-gated. The Free and Starter tiers cover card sort / tree test / first-click
only — a user who needs surveys, interviews, or competitor analysis has to be on Pro+. Check the plan before
promising a method.
Free caps studies AND analytics. Free is 3 studies with analytics limited to roughly the first 3
respondents (responses beyond that are collected but not analyzed) — it's a real evaluation limit, not just a
study count. Starter unlocks full analytics.
Related skills
/sales-kardsort — The closest budget twin: the same indie card-sort/tree-test battery at an even lower,
one-time price (no subscription) with rich-media / participant-created cards — but with no MCP server,
Figma plugin, embed, or idea-validation engine (export-only: CSV / Casolysis / SynCaps + the cardsort Python
package). Route here when one-time pricing or rich cards matter more than automation. Install: npx skills add sales-skills/sales --skill sales-kardsort -a claude-code
/sales-provenbyusers — Another budget twin: a cheap full-battery IA/usability suite (card sort/tree test + first-click/five-second/preference/surveys) with one-time pricing and CSV export, but no MCP, no idea-validation engine, and no Figma/embed — recruit-your-own-participants only. Route here when method breadth matters more than an automation surface. Install: npx skills add sales-skills/sales --skill sales-provenbyusers -a claude-code
/sales-optimalworkshop — The IA "reference standard" peer: deep, specialist card sorting (OptimalSort) +
tree testing (Treejack) + first-click (Chalkmark) with benchmark analysis and an own panel — route here when the
job is pure information architecture and rigor matters more than price, or to compare; note it's UI-first
(no API/MCP) and higher-entry-priced. Install: npx skills add sales-skills/sales --skill sales-optimalworkshop -a claude-code
/sales-uxtweak — The broad self-serve all-in-one usability peer (card sort + tree test + first-click /
five-second / preference + prototype/website tests + session recording + surveys) with its own 130-country
panel — route here when you want breadth and a built-in panel; also UI-only/no API. Install: npx skills add sales-skills/sales --skill sales-uxtweak -a claude-code
/sales-lyssna — The ex-UsabilityHub usability + IA twin with a larger panel and a usable free plan plus
AI follow-ups/summaries — route here to compare, or when free-tier generosity / panel size matters. Install: npx skills add sales-skills/sales --skill sales-lyssna -a claude-code
/sales-idea-validation — The tool-agnostic validate-before-building method + the full research/usability/validator
landscape (use this to decide build-or-not; a passing IA test or an AI verdict is not demand). Install: npx skills add sales-skills/sales --skill sales-idea-validation -a claude-code
/sales-userintuition — The API/webhook/MCP-native real-human interview peer — the pick when you need a documented programmatic research pipeline ValidateThat can't offer. Install: npx skills add sales-skills/sales --skill sales-userintuition -a claude-code
/sales-prolific — The developer-native recruit panel ValidateThat recruits through — route here to drive recruitment at scale via a real REST API + webhooks + CLI. Install: npx skills add sales-skills/sales --skill sales-prolific -a claude-code
/sales-funnel — Build the smoke-test / fake-door landing page that turns an IA/design hypothesis or an AI verdict into a real demand test. Install: npx skills add sales-skills/sales --skill sales-funnel -a claude-code
/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: "I need to fix my app's navigation but I'm on a tiny budget — what do I run?"
User says: "Users can't find settings in my app. I want to test the menu structure but I can't afford Optimal Workshop."
Skill does: Recommends a tree test in ValidateThat (findability without visual design), optionally preceded
by an open card sort if the labels/categories are in doubt (read via the similarity matrix / dendrogram).
Notes the Free tier's 3-study / 3-respondent-analytics limit and that Starter (~$19/mo) unlocks full analytics
and unlimited tests, so a real 30-participant study needs Starter. Suggests sharing the link to your own users
(free) before paying Prolific. Adds that a clean tree test proves the nav works, not that the product sells.
Result: The user runs a Starter-tier tree test with the right participant count for a fraction of the peer price.
Example 2: "How do I run a ValidateThat card sort from Claude / automate this?" (developer/automation)
User says: "Can I spin up card sorts and pull results into my workflow without clicking around the dashboard?"
Skill does: States plainly that ValidateThat has no public REST API or webhooks — the automation surface is
its MCP server ("run UX research from a chat" in Claude Desktop / claude.ai / Claude Code — create studies, list
studies, pull stats), plus the Figma plugin and on-site embed snippet, with CSV export (Starter+) for
bulk data-out. Tells the user to copy the MCP connect string from validatethat.io's integrations page (the exact
endpoint/auth isn't in public docs — don't guess it) and discover the tool list at runtime. If they need a
documented REST + webhooks pipeline, routes to /sales-userintuition.
Result: The user wires up the MCP server in Claude instead of hunting for a REST API that doesn't exist.
Example 3: "Should I trust ValidateThat's 60-second idea verdict, and is Starter enough?"
User says: "I ran my SaaS idea through their validation engine and it says 'go' — do I start building? And which plan?"
Skill does: Cautions that the Idea-Validation Engine's verdict + competitor breakdown are an AI opinion, not
demand — keep the research plan it hands you, but earn the go/no-go with a real behavior test (smoke test,
pre-sale) via /sales-idea-validation. On plans (best-effort): Free = 3 studies, Starter (~$19/mo) = unlimited
card sorts/tree tests + full analytics, but surveys, interviews, and competitor analysis are Pro (~$49/mo), so if
the research plan calls for interviews the user needs Pro. Points to validatethat.io/pricing and flags every figure
as best-effort.
Result: The user treats the verdict as directional, runs the recommended studies, and picks the tier matching the methods.
Troubleshooting
"Which do I run — a card sort or a tree test?"
Symptom: The user isn't sure whether to test categories/labels or navigation findability.
Solution: Use a card sort when the question is how users group and name content (open = create/label to
discover a structure; closed = sort into your categories to validate them → read via similarity matrix /
dendrogram). Use a tree test when the structure exists and the question is whether users can find an item
(findability + first-click path, no visual design). Run the card sort to design the tree, then the tree test to
validate it — and remember a passing test proves the structure works, not that anyone will pay.
"The validation engine said 'go' — is my idea validated?"
Symptom: The user is treating the 60-second AI verdict as a green light to build.
Solution: No — the verdict, competitor breakdown, and market read are an LLM opinion (it can invent market
sizes and encourage almost any idea). Keep the prioritized research plan it produces (it's a good to-do list) and
the competitor map for positioning, but take the build-or-not go/no-go from a real behavior test (smoke-test page +
pre-sale) via /sales-idea-validation. A passing card sort or tree test you run next proves the design works — still
not demand.
"How do I get ValidateThat results into my stack / automate exports?"
Symptom: The user wants results flowing into a CRM, warehouse, or Slack automatically.
Solution: ValidateThat has no public REST API or webhooks. Programmatic surfaces are the MCP server
(drive studies + pull stats from Claude/Cursor), the Figma plugin, and the on-site embed snippet; bulk
data-out is CSV export (Starter+). Copy the MCP connect string from validatethat.io's integrations page (the
exact endpoint/auth isn't published — don't invent it), or glue a scheduled CSV export into your ETL. If an
automated documented pipeline is required, route to /sales-userintuition (REST API + HMAC webhooks + MCP).
