34 journeys × 3 profiles. Same destination, different vehicle, different route. The ForEveryMind SDK doesn't paint the same web three colours — it ships three different webs that share manufacturer DNA.
anchor journey · 60s read
full detail →User opens a table, edits a single cell, and saves. The journey covers the most-touched cell-level slot (cell-suffix) plus the post-save surfaces (save-confirmation / main-postlude).
Connector
Battleship
sea route
“Bring the team into the workflow.”
HSP flood + Connector → social context is the cheapest channel. The activity feed entry carries provenance the cell can attach ("by Aisha, 2m"), so the cell-suffix slot fills additively without layout reflow.
Try Monica's route →Explorer
Tractor
field route
“Show me what I don't already know.”
ASD prediction-error + ADHD focus + DYS GLUT-drain — the journey compensates by chunking to 4 rows, freezing motion to 0ms, and arming an advance-notice strip 3s before any destructive write.
Try Nate's route →Analyst
Airplane
air route
“Numbers first. Then we talk.”
Analyst + DA-from-data — the journey adds dense numerics (KpiBand, Sparkline, SaveLedger) so the existing reward loop keeps firing through the edit. CORT +19 bumps confirmation_dialogs to 'always' (see profile packet) which DestructiveGuard reads.
Try Sara's route →why this matters
“One of us needs a tractor, one needs a battleship and another needs an airplane. They are utterly different. But they should all share the brand book DNA.”
“It's not just the look — it's the way we use it. The user journeys will be different routes. Some take the field, the air or the sea to get to the same destination, each in an entirely different vehicle.”
full atlas · 5min browse
User initiates column creation via the table editor toolbar. The journey covers the column-name input surface (typed-name for Sara, literal-language for Nate), the advance-notice strip for destructive-adjacent DDL operations (Nate), and post-save feedback including KPI impact (Sara) and team context (Monica).
User navigates to the Database Triggers surface, creates a trigger on a table, defines its action, and saves. The journey covers trigger creation with destructive-write patterns and impact analysis across three cognitive models.
User initiates creation of a foreign key relationship in the database tables view. The journey covers the FK dialog (table/column selection), relationship visualization (Nate's SchemaMap), impact analysis (Sara's KpiBand), and team context (Monica).
User navigates to the Database Functions surface, creates a custom Postgres function, reviews impacted tables, and saves. The journey covers function SQL editing, schema validation, and confirmation workflows across three cognitive models.
User initiates index creation via the database indexes surface. The journey covers index-type selection, column selection, and post-creation feedback including performance impact (Sara's query-plan reveal, Nate's micro-progress).
User selects a column and initiates deletion. The journey covers the confirmation modal (untouchable by brand policy), plus the pre-confirmation advance-notice (Nate's prediction-error mitigation) and post-deletion feedback surfaces (Monica's team context, Sara's schema-change ledger).
User selects a row or table and initiates deletion. The journey covers the destructive-op confirmation modal (untouchable by brand policy), plus the pre-confirmation advance-notice (Nate) and post-confirmation feedback surfaces (Monica's activity context, Sara's row count ledger).
User opens a table, edits a single cell, and saves. The journey covers the most-touched cell-level slot (cell-suffix) plus the post-save surfaces (save-confirmation / main-postlude).
User initiates table export to CSV via the table toolbar or context menu. The journey covers column/filter selection, export format options, and post-export feedback including team-sharing (Monica), row-count snapshot (Sara), and pagination progress (Nate).
User finds a table and views its schema. Three entry points: Monica via team activity feed ("Aisha just added orders/v2"), Nate via SchemaMap visual discovery, Sara via KPI hub drilldown into table metrics.
User opens a table editor and inserts a new row via form or grid inline. Monica gets activity context on save. Nate gets advance-notice strip before destructive write + chunk-size 4 pagination. Sara gets save-ledger feedback.
User initiates inline renaming of a table via double-click or context menu. The journey covers the inline-edit input, confirmation, and post-rename feedback surfaces across the three cognitive profiles.
User applies a filter or search to a table and navigates paginated results. Nate gets DiscoveryCard idle filler + chunk-size 4. Sara monospace metrics + Sparkline. Monica gets activity context on discover.
User opens the schema diagram surface to explore database relationships visually. The journey covers the ERD view, table-hover details, and contextual overlays including team activity (Monica), KPI metrics (Sara), and schema-first navigation (Nate).
User runs a SELECT query and views tabular results. Monica via shared queries (team context), Nate via paginated chunk-size 4 with micro-progress, Sara via query-history with monospace numerics and Sparkline.
User writes a query and saves it as a named snippet for reuse. Monica via shared-with-team context, Sara via query-history pin, Nate via literal-name input with advance-notice.
User generates a shareable link for a query. Monica via people-picker (social context), Sara via terse confirmation with email validation, Nate via explicit advance-notice warning that team will see this.
User clicks between multiple open SQL query tabs. Nate via zero-motion with literal tab labels, Sara via monospace timestamps, Monica via by-who-touched sorting.
User runs EXPLAIN ANALYZE on a query and views the execution plan. Sara via monospace numerics + sparkline of row-counts, Nate via visual tree + chunk-size 4, Monica via discussion thread context.
User enters OAuth provider credentials and enables the provider. Sara via typed-name confirmation, Nate via chunk-size 4 + advance-notice, Monica via shared review context.
User navigates to the Auth > Users surface and inspects the list of authenticated users. The journey tests language overrides (tone: "team members" vs. "users"), column rendering deltas (monospace email, presence indicators), and pagination boundaries specific to this non-table surface.
User sends an invite to a new team member via email. Monica via people-first picker, Sara via terse confirm with email validation, Nate via literal-language interface.
User initiates creation of a row-level security (RLS) policy. The journey covers the policy-expression dialog, before/after row-count visualization, and post-creation feedback with security-aware profiles.
User navigates to the Storage > Files surface, selects a bucket, and browses files. The journey covers file-list rendering (icons, sizes, timestamps), preview behavior (thumbnails vs. alt text), and metadata column choices specific to each profile's cognitive needs.
User creates a new bucket for object storage. Nate via advance-notice (destructive-ish), Sara via typed-name confirmation, Monica via collaborative announcement.
User selects and uploads a file to a storage bucket. Sara via monospace bytes + sparkline of upload velocity, Nate via large preview + alt-text + chunk-size 4, Monica via shared-by + activity blurb.
User navigates to a function's secrets/environment configuration, adds or updates secrets, and verifies the changes. The journey covers secret creation, masking, and confirmation workflows across three cognitive models.
User navigates to the Edge Functions surface, creates or deploys a function, and verifies the deployment. The journey covers deployment entry points, code editor interactions, and post-deployment confirmation across three cognitive models.
User navigates to the Edge Functions surface, selects a function, and reads its execution logs. The journey covers log list rendering (timestamps, severity levels), pagination / density optimization, and time-series visual encoding across three cognitive models approaching async system observability.
User navigates to a function's version history, selects a previous version, and initiates a rollback. The journey covers destructive version management with advance notice and confirmation workflows optimized for three cognitive models.