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Turn a user’s labs and wearable data into a dashboard your app can render (cards, scores, and a design system to style them) without building any analysis logic yourself.
StepEndpoint
Upload dataPOST /imports/file
Run one multimodal analysisPOST /analyses
Read the render-ready specGET /dashboard-specs/{id}
Pull recommendationsGET /analyses/{id}/recommendations
Style itGET /design/systems/{id}
export FB_API="https://api.foreverbetter.xyz"
export FB_KEY="…"   # a key from the dashboard
export UID="user_123"  ORG="org_123"

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1. Upload the user’s data

Upload each modality; capture the returned source.id. 
LABS=$(curl -s -X POST "$FB_API/imports/file" -H "authorization: Bearer $FB_KEY" \
  -H "content-type: application/json" -d '{
    "user_id":"'$UID'","organization_id":"'$ORG'","category":"biomarkers",
    "filename":"labs.csv","content_type":"text/csv",
    "text":"marker,value,unit\nApoB,118,mg/dL\nHbA1c,5.7,%\nHDL-C,44,mg/dL\nVitamin D,24,ng/mL"
  }' | jq -r .source.id)

WEAR=$(curl -s -X POST "$FB_API/imports/file" -H "authorization: Bearer $FB_KEY" \
  -H "content-type: application/json" -d '{
    "user_id":"'$UID'","organization_id":"'$ORG'","category":"wearables",
    "filename":"wearables.csv","content_type":"text/csv",
    "text":"metric,value,unit\nsleep_duration,6.8,hours\nhrv,41,ms\nresting_heart_rate,61,bpm"
  }' | jq -r .source.id)

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2. Run one multimodal analysis

POST /analyses combines every source for the user into a single analysis with a healthspan score and a dashboard spec. 
AN=$(curl -s -X POST "$FB_API/analyses" -H "authorization: Bearer $FB_KEY" \
  -H "content-type: application/json" -d '{
    "user_id":"'$UID'","organization_id":"'$ORG'",
    "source_ids":["'$LABS'","'$WEAR'"],
    "profile":{"age":42,"sex":"male"}
  }' | jq -r .id)

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3. Read the render-ready dashboard spec

Every card is normalized: a title, category, 0–100 score, status, a plain-language summary, and a suggested action. Map these straight onto UI components. 
curl -s "$FB_API/dashboard-specs/$AN" -H "authorization: Bearer $FB_KEY" | jq '.cards[0]'
# { "id":"…","title":"ApoB","category":"biomarkers","score":55,
#   "status":"watch","summary":"ApoB is high versus the wellness target…",
#   "action":"Interpret alongside ApoB, Lp(a), blood pressure…" }
Pair it with the healthspan score and priority findings from the analysis summary (GET /analyses/{id}) for a hero header.

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4. Add prioritized recommendations

curl -s "$FB_API/analyses/$AN/recommendations" -H "authorization: Bearer $FB_KEY" \
  | jq '.protocols'   # tiered core / optimize / maintain routines

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5. Style it with a design system

Skip design bikeshedding: pull a ready design-token set and a DESIGN.md. 
curl -s "$FB_API/design/systems/clinical-modern" | jq '{colors,typography,radii}'

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Take it further

  • Add category=genetics or category=behavioral uploads before step 2; the same analysis folds them in.
  • Re-render on new data with POST /analyses/{id}/rerun.
  • For an agent, the identical flow is available as MCP tools (upload_health_data, run_health_analysis, get_dashboard_spec).