KardSort (kardsort.com) platform help — a low-cost, indie card-sorting and tree-testing tool for UX and information-architecture (IA) research, sold via one-time payments, not subscriptions. It runs open/closed/hybrid card sorts (rich-media and participant-created cards) and task-based tree testing, with built-in analytics (similarity matrix, dendrogram, participant agreement, tree-test success/directness). Use when running a card sort or tree test on a tight budget, reading a similarity matrix or dendrogram, validating a site's navigation and findability, importing a sitemap into a tree test, linking a card sort to a tree test for IA validation, exporting results as Casolysis or SynCaps CSV, analyzing exports with the cardsort Python package, or choosing its Free, Starter, or Premium tier. Do NOT use for comparing card-sort tools across the market (use /sales-idea-validation), the MCP-native budget twin (use /sales-validatethat), or a deeper IA suite (use /sales-optimalworkshop).
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name
sales-kardsort
description
KardSort (kardsort.com) platform help — a low-cost, indie card-sorting and tree-testing tool for UX and information-architecture (IA) research, sold via one-time payments, not subscriptions. It runs open/closed/hybrid card sorts (rich-media and participant-created cards) and task-based tree testing, with built-in analytics (similarity matrix, dendrogram, participant agreement, tree-test success/directness). Use when running a card sort or tree test on a tight budget, reading a similarity matrix or dendrogram, validating a site's navigation and findability, importing a sitemap into a tree test, linking a card sort to a tree test for IA validation, exporting results as Casolysis or SynCaps CSV, analyzing exports with the cardsort Python package, or choosing its Free, Starter, or Premium tier. Do NOT use for comparing card-sort tools across the market (use /sales-idea-validation), the MCP-native budget twin (use /sales-validatethat), or a deeper IA suite (use /sales-optimalworkshop).
argument-hint
[describe what you need help with in KardSort]
license
MIT
version
1.0.1
tags
["sales","pre-launch","platform"]
KardSort Platform Help
KardSort (kardsort.com) is a simple, low-cost, indie card-sorting + tree-testing tool for UX teams —
the budget end of the information-architecture (IA) research cluster, built and maintained by a single
developer. It runs card sorting (open/closed/hybrid, with rich-media cards — images/audio/video/YouTube —
and participant-created cards, and letting a card go into more than one category) and task-based tree
testing (import a sitemap or text tree, or build one from a card sort), with built-in analytics for both:
a similarity matrix, hierarchical-clustering dendrogram, card ambiguity, participant agreement, and
split-half reliability on the card-sort side; success/directness, first-click correctness, path analysis,
node heatmaps, and Sankey navigation flows on the tree-test side. You can link a card sort to a tree test
for correlation analytics and IA-validation scoring, add pre/post questionnaires with power filters
(segment by answer), password-protect and schedule studies, and run in 7 languages.
Two things to say almost every time:
A clean card sort or tree test proves the structure works, not that anyone will pay. Read the similarity matrix / dendrogram / findability score as evidence the IA is right — if the user is still deciding whether to build, keep the findings but take the go/no-go to a real behavior test via /sales-idea-validation.
KardSort has NO public API, MCP server, webhooks, or Zapier. The only programmatic surface is export (CSV, Casolysis, SynCaps V3); a "pipe results into my CRM/warehouse" ask is a scripted export job, and the documented analysis path is the third-party cardsort Python package (github.com/katoss/cardsort) reading the Casolysis CSV. If a documented REST/webhook pipeline is required, 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 KardSort?
A) Pick/run a study — card sort (open/closed/hybrid) vs tree test, rich-media or participant-created cards
B) Read the analytics — similarity matrix, dendrogram, agreement; tree-test success/directness/path/Sankey
C) Link studies — connect a card sort to a tree test for correlation / IA-validation scoring
D) Get data out / automate — CSV / Casolysis / SynCaps V3 export and the cardsort Python package (no API)
E) Choose a plan — Free vs Starter vs Premium (all paid plans are one-time payments, not subscriptions)
Are you testing an IA you've already got, 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
Comparing card-sort/IA tools across the market, or the tool-agnostic validate-before-building method
/sales-idea-validation {question}
The MCP-native budget twin (same battery + an idea-validation engine + an MCP server)
/sales-validatethat {question}
The deep-IA reference-standard peer (benchmark card-sort/tree-test analysis, own panel)
A documented API / webhook-native research pipeline (KardSort has none)
/sales-userintuition {question}
Running a real behavior demand test (smoke-test page, waitlist, pre-sale) instead of an IA test
/sales-idea-validation or /sales-funnel{question}
When routing, give the exact command: "This is a {domain} question — run: /sales-idea-validation {original question}"
Otherwise, answer KardSort-specific questions using Step 3.
Step 3 — KardSort platform reference
Read references/platform-guide.md for the full reference — the module/automation-surface table (what's
export-accessible vs UI-only), best-effort pricing and the one-time-payment plan gates (study/participant/card
caps, data-retention windows), the Study → Card → Category → Participant data model with JSON shapes, the export
formats (CSV / Casolysis / SynCaps V3) and the cardsort Python package recipe, and the no-API
data-out playbook. For the export column schema the cardsort package expects, read
references/kardsort-api-reference.md — KardSort has no REST API, so that file documents the export/analysis
surface instead.
Answer using only the relevant section — don't dump the full reference.
Step 4 — Actionable guidance
In every response, say the caveat: an IA/usability result is not demand. State plainly — even when the
user only asked how to fix navigation — that a card sort or tree test proves a structureworks (category
agreement, findability) but does not prove strangers will pay. Keep the findings; for any build-or-not
question, take the go/no-go to a real behavior test (smoke test, pre-sale) via /sales-idea-validation.
Flag it as export-only when any automation/API/pipeline comes up. KardSort has no public REST API, MCP
server, webhooks, or Zapier (verify — it may change). The only data-out is export — CSV (cards &
categories, with remarks), Casolysis (recommended), SynCaps V3 (.txt), questionnaire responses,
participant info. The documented analysis path is the third-party cardsort Python package reading the
Casolysis CSV — one row per participant × card placement, columns
card_id, card_label, category_id, category_label, user_id (pip install cardsort → create_dendrogram /
get_cluster_labels). "Sync results to HubSpot/Snowflake on a schedule" is a scripted export job, not REST —
if a documented pipeline is required, route to /sales-userintuition.
State that paid plans are one-time payments, not subscriptions. Starter and Premium are one-time
purchases (Starter = one month of access, no auto-renewal; Premium = a year, no auto-renewal) — say this
explicitly so the user doesn't expect a recurring plan, and present every price as best-effort pointing to
kardsort.com/pricing.
Match the tool to the question, and name the outputs to read. Recommend by job: card sort = how users
group/label content (open = discover, closed = validate; hybrid = both) — whenever you recommend a card
sort, tell the user to read its results via the similarity matrix and the dendrogram (plus participant
agreement); tree test = can users find things in your nav (findability, no visuals → success/directness/
first-click/Sankey path). Design the tree with a card sort, then validate it with a tree test — and link the
two for correlation / IA-validation scoring.
Warn about the plan caps and data-retention window before they hit them. Free is 1 published study / 10
participants / 10 cards / 20 tree nodes / 7-day retention; Starter and Premium raise these (up to ~1000
participants / 1000 cards / 500 nodes) and extend retention to 30 / 90 days. Present all limits as
best-effort and flag that results are purged after the retention window — export before it lapses.
Set participant counts realistically. A stable similarity matrix / split-half-reliable card sort wants
~30+ participants; a findability tree test similar. Say it's a rule-of-thumb, not a guarantee, and note the
per-plan participant cap may force a paid tier for a properly-powered study.
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 one-time-payment model, the per-plan caps, the data-retention
windows, and the export-format list; verify at kardsort.com.
IA/usability success ≠ demand. A clean card sort or tree test proves the structure works, not that anyone
will pay. Route the build-or-not go/no-go to /sales-idea-validation.
No REST API, no MCP, no webhooks, no Zapier. The only automation surface is export (CSV / Casolysis /
SynCaps V3); the documented analysis path is the third-party cardsort Python package on the Casolysis CSV.
Don't design a live REST/webhook pipeline around it — route pipeline needs to /sales-userintuition.
Paid plans are one-time payments, not subscriptions. Starter (~one month of access) and Premium (~one year)
are one-time purchases with no auto-renewal — set expectations accordingly; access lapses rather than renews.
Free tier is a real evaluation cap, not just a trial.1 published study, 10 participants, 10 cards, 20
tree nodes, 2 questions, 7-day retention — too small for a powered study. A 30-participant card sort needs a paid tier.
Results are purged after the retention window (7 / 30 / 90 days by plan). Export (Casolysis/CSV/SynCaps)
before the window lapses or the data is gone — there's no API to pull it back later.
A stale listicle may say KardSort has "no similarity matrix/dendrogram." That's outdated — the current
product ships 15+ card-sort KPIs and 20+ tree-test KPIs; trust kardsort.com over old comparison articles.
Payment is Stripe/PayPal only. If those aren't supported in the user's country, the maker offers manual
alternatives on request (contact via kardsort.com) — don't assume a blocked checkout means no access.
Related skills
/sales-validatethat — The closest twin: the same budget card-sort/tree-test battery plus an Idea-Validation Engine, a Figma plugin, on-site embeds, and — unlike KardSort — an MCP server (run studies from Claude/Cursor). Route here when the user wants an automation surface or idea validation baked in; KardSort wins on one-time pricing and rich-media/participant-created cards. Install: npx skills add sales-skills/sales --skill sales-validatethat -a claude-code
/sales-optimalworkshop — The IA "reference standard" peer: deep card sorting (OptimalSort) + tree testing (Treejack) + first-click (Chalkmark) with benchmark analysis and an own panel — route here when the job is pure IA rigor and price matters less; also UI-first (no API). Install: npx skills add sales-skills/sales --skill sales-optimalworkshop -a claude-code
/sales-provenbyusers — Another budget twin: a cheaper full-battery suite (card sort/tree test + first-click/five-second/preference/surveys) with one-time pricing, but no rich-media/participant cards, no MCP, and no cardsort-package export path — just CSV. Route here when method breadth matters more than rich cards or a scripted export. Install: npx skills add sales-skills/sales --skill sales-provenbyusers -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 for 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 suite with a larger panel and a usable free plan —
route here to compare, or when panel size / free-tier generosity 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 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 research peer — the pick when you need a documented
programmatic research pipeline KardSort can't offer. Install: npx skills add sales-skills/sales --skill sales-userintuition -a claude-code
/sales-funnel — Build the smoke-test / fake-door landing page that turns an IA hypothesis 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 without paying Optimal Workshop's prices."
Skill does: Recommends an open card sort first if the labels/categories are in doubt (read via the
similarity matrix / dendrogram), then a tree test to validate findability — and to link the two for
IA-validation scoring. Notes the Free tier's 1-study / 10-participant / 7-day-retention cap means a real
30-participant study needs Starter or Premium — and that those are one-time payments, not subscriptions.
Adds that a clean tree test proves the nav works, not that the product sells.
Result: The user runs a properly-powered, linked card-sort → tree-test study on a paid one-time tier for a fraction of the peer price.
Example 2: "How do I get KardSort results into my analysis pipeline / automate this?" (developer/automation)
User says: "Can I pull card-sort results out programmatically and cluster them myself instead of clicking around?"
Skill does: States plainly that KardSort has no public REST API, MCP server, or webhooks — the only
data-out is export (CSV, Casolysis [recommended], SynCaps V3). Points to the documented path: export
the Casolysis CSV (columns card_id, card_label, category_id, category_label, user_id) and analyze it with
the third-party cardsort Python package (pip install cardsort → analysis.create_dendrogram(df) /
get_cluster_labels(df, cards)). For a scheduled warehouse sync, wrap that export+script in your own ETL; if a
documented REST + webhooks pipeline is a hard requirement, routes to /sales-userintuition.
Result: The user builds a scripted Casolysis-export → cardsort clustering job instead of hunting for an API that doesn't exist.
Example 3: "Is KardSort's free plan enough, and is it really a subscription?"
User says: "I want to try card sorting cheaply — what does the free plan cover and what am I paying monthly?"
Skill does: Explains Free = 1 published study, 10 participants, 10 cards, 20 tree nodes, 7-day retention —
fine for a trial, too small for a powered study. Clarifies that Starter and Premium are one-time payments, not
subscriptions (Starter ≈ one month of access, Premium ≈ one year, no auto-renewal), so nothing recurs.
Flags that results are purged after the retention window — export (Casolysis/CSV) before it lapses. Presents
every figure as best-effort and points to kardsort.com/pricing.
Result: The user picks the right one-time tier for a real study and exports results before retention lapses.
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; hybrid = both → read via the
similarity matrix / dendrogram + participant agreement). Use a tree test when the structure exists and the
question is whether users can find an item (success/directness/first-click, Sankey path, no visuals). Run the
card sort to design the tree, then the tree test to validate it — and link them for correlation/IA-validation
scoring. Remember: a passing test proves the structure works, not that anyone will pay.
"How do I get KardSort results out / automate exports?"
Symptom: The user wants results flowing into a CRM, warehouse, notebook, or Slack.
Solution: KardSort has no public REST API, MCP server, or webhooks. Data-out is export — CSV (cards &
categories, with remarks), Casolysis (recommended), SynCaps V3 (.txt), questionnaire responses, participant
info. The documented analysis path is the third-party cardsort Python package on the Casolysis CSV
(pip install cardsort). Glue a scheduled export into your own ETL, or drive the cardsort package from a script.
If an automated documented pipeline is required, route to /sales-userintuition (REST API + HMAC webhooks + MCP).
"My results disappeared / the study says the free limit is reached."
Symptom: Data is gone, or the user can't publish/collect more.
Solution: Two separate causes. (1) Data retention — results are purged after the plan window (Free 7
days, Starter 30, Premium 90); export (Casolysis/CSV/SynCaps) before it lapses, there's no API to recover it.
(2) Plan caps — Free allows 1 published study / 10 participants / 10 cards / 20 nodes; a bigger or second
study needs Starter or Premium (both one-time payments). Check which limit was hit before assuming a bug,
and export existing data first.
