sales-skills/sales-validatethat

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:

1. 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 CSV export (no REST API)
   • F) Choose — ValidateThat vs Optimal Workshop / UXtweak / Lyssna, or Free vs Starter vs Pro
2. 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 structure works (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).