Prism Handle Audit API
Scrape Prism Handle Audit data with one API call. Answers three questions BEFORE you spend credits on a full pull. (1) Is this handle worth pulling? — a deterministic 0-100 composite quality score with five component sub-scores (presence, audience, engagement, activity, content_richness). (2) On which platforms? — a per-platform verdict and a `best_platforms` ranking, plus the native endpoints to hit next, so you can route the follow-up pull. (3) How much data is there? — a `surface` block projecting post + comment volume and the credit cost to pull it. Looks a single handle up across TikTok, Instagram, YouTube, and X in parallel (Threads/Bluesky/Truth Social can be added via `platforms`); for every platform the handle is found on it also samples recent posts to measure engagement and posting activity. A handle found on only some platforms is a successful, complete answer (the misses are the product); only an all-platform miss refunds. The call returns within a bounded latency budget: a slow upstream platform is skipped rather than stalling the whole audit, and the score is computed from the data available at request time (a skipped or profile-only platform is scored on the signals that did return and shown in `legs[]`, never silently dropped or penalized as a miss). Data completeness is first-class in the contract: `confidence` reports a deterministic high/medium/low tier with the reason, `evaluated_platforms`/`skipped_platforms` name exactly which platforms answered, `computed_at` timestamps the computation (cached responses can be up to 30 minutes old), and `score_version` pins the scoring-model semver so stored scores stay comparable across tuning releases. Scores are advisory heuristics estimated from public data at request time, not calibrated probabilities. Flat 5 credits for up to 4 platforms, +1 credit per extra platform. `legs[]` reports each leg's status, cost, and latency. See `prism/creator-vet` for the deeper 'should I sponsor' report.
Last updated July 2026
Try the Prism Handle Audit API
See real data before writing a single line
Searching 44 platforms in parallel
What can you do with the Handle Audit API?
The Handle Audit endpoint gives you structured Prism data with computed fields in a single request. No scraping infrastructure to build or maintain.
Example Request
curl -H "x-api-key: YOUR_API_KEY" \
"https://www.socialcrawl.dev/v1/prism/handle-audit?handle=mrbeast"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/prism/handle-audit",
params={
'handle': 'mrbeast',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/prism/handle-audit?handle=mrbeast",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters
| Parameter | Required | Description |
|---|---|---|
| handle | Yes | The handle to audit across every requested platform. Accepts a bare handle, a leading @, or a full profile URL (the platform is sniffed from the host). |
| platforms | No | CSV subset of tiktok,instagram,youtube,twitter,threads,bluesky,truthsocial (default the first four). Unknown platforms are ignored. Max 8. |
| sample | No | Recent posts sampled per found platform for the engagement + activity metrics (default 10, max 25). |
What does the Prism Handle Audit API return?
Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.
Example response
{
"success": true,
"platform": "instagram",
"endpoint": "/v1/instagram/engagement",
"data": {
"engagement_rate_percentages": 38.33,
"recent_posts": 12,
"followers": 87608035,
"comments": 528912,
"likes": 33049046,
"recent_posts_explanation": "Statistics based on the last 12 posts",
"id_user": "2278169415",
"username": "mrbeast",
"is_private": false,
"posts_details": [
{
"likes": 5636982,
"comments": 69484,
"taken_at": 1781457954,
"datetime": "2026-06-14 20:25:54",
"hours_since_post": 461,
"time_ago": "19 days ago",
"likes_per_hour": 12228,
"comments_per_hour": 151
},
{
"likes": 20000768,
"comments": 223510,
"taken_at": 1732824650,
"datetime": "2024-11-28 23:10:50",
"hours_since_post": 13971,
"time_ago": "2 years ago",
"likes_per_hour": 1432,
"comments_per_hour": 16
},
{
"likes": 929226,
"comments": 30633,
"taken_at": 1782232475,
"datetime": "2026-06-23 19:34:35",
"hours_since_post": 246,
"time_ago": "10 days ago",
"likes_per_hour": 3777,
"comments_per_hour": 125
},
{
"likes": 487761,
"comments": 22482,
"taken_at": 1781799425,
"datetime": "2026-06-18 19:17:05",
"hours_since_post": 366,
"time_ago": "15 days ago",
"likes_per_hour": 1333,
"comments_per_hour": 61
},
{
"likes": 712265,
"comments": 15716,
"taken_at": 1781366405,
"datetime": "2026-06-13 19:00:05",
"hours_since_post": 487,
"time_ago": "20 days ago",
"likes_per_hour": 1463,
"comments_per_hour": 32
},
{
"likes": 1475116,
"comments": 35386,
"taken_at": 1781277094,
"datetime": "2026-06-12 18:11:34",
"hours_since_post": 512,
"time_ago": "21 days ago",
"likes_per_hour": 2881,
"comments_per_hour": 69
},
{
"likes": 1108220,
"comments": 26632,
"taken_at": 1780160249,
"datetime": "2026-05-30 19:57:29",
"hours_since_post": 822,
"time_ago": "1 months ago",
"likes_per_hour": 1348,
"comments_per_hour": 32
},
{
"likes": 542948,
"comments": 28476,
"taken_at": 1779375582,
"datetime": "2026-05-21 17:59:42",
"hours_since_post": 1040,
"time_ago": "1 months ago",
"likes_per_hour": 522,
"comments_per_hour": 27
},
{
"likes": 698514,
"comments": 24401,
"taken_at": 1779120014,
"datetime": "2026-05-18 19:00:14",
"hours_since_post": 1111,
"time_ago": "2 months ago",
"likes_per_hour": 629,
"comments_per_hour": 22
},
{
"likes": 468000,
"comments": 13548,
"taken_at": 1778947209,
"datetime": "2026-05-16 19:00:09",
"hours_since_post": 1159,
"time_ago": "2 months ago",
"likes_per_hour": 404,
"comments_per_hour": 12
},
{
"likes": 526594,
"comments": 24411,
"taken_at": 1777737719,
"datetime": "2026-05-02 19:01:59",
"hours_since_post": 1495,
"time_ago": "2 months ago",
"likes_per_hour": 352,
"comments_per_hour": 16
},
{
"likes": 462652,
"comments": 14233,
"taken_at": 1777580305,
"datetime": "2026-04-30 23:18:25",
"hours_since_post": 1538,
"time_ago": "2 months ago",
"likes_per_hour": 301,
"comments_per_hour": 9
}
]
},
"credits_used": 5,
"credits_remaining": 9999,
"request_id": "req_example000000",
"cached": false
}Live sample illustrating the unified response shape. Field values reflect the record you query.
How does the Prism Handle Audit API work?
Send a GET request with your API key and get back clean, structured JSON. Every response follows our unified schema with computed fields.
Method
GET
Response
JSON
How do you scrape social media data in seconds?
The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 44 platforms covering 10B+ monthly active users.
One schema, every platform
Query 44 platforms with identical response structures. Write your integration once.
Computed fields, not just scraped
Every response includes engagement_rate, estimated_reach, content_category, and language — ready to use.
See your data before you code
Visual Data Explorer — paste any URL, get rich result cards, sortable tables, CSV export.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json(){
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}Ready to scrape Prism Handle Audit data?
Get your API key and start pulling Prism data in under 60 seconds.
