DEV Community: ntriq-gh

Check a list of usernames across 400+ sites without installing Sherlock

ntriq-gh — Tue, 03 Nov 2026 01:03:06 +0000

Sherlock is the tool most people reach for when they want to know where a username exists. It works well on your laptop. It gets awkward when the job is a list of handles, a schedule, or a pipeline that needs JSON instead of terminal output.

I wrapped the open-source Sherlock engine (MIT) as an Apify Actor so the setup step disappears. You send usernames, you get one row per site where the account exists.

What you get

For johnsmith the Actor checks 400+ sites, including GitHub, Reddit, Instagram, TikTok, X, Steam and Twitch. Each confirmed account becomes one dataset row:

{
  "username": "johnsmith",
  "siteName": "GitHub",
  "profileUrl": "https://github.com/johnsmith",
  "status": "Claimed",
  "httpStatus": 200,
  "responseTimeS": 0.41
}

A username with no hits returns a single "No accounts found" row, so an empty result is still an answer you can store.

Input

{
  "usernames": ["johnsmith", "jsmith_dev"],
  "sites": ["GitHub", "Reddit"],
  "timeout": 60
}

usernames: up to 5 per run on paid plans
sites: optional, limit the scan to the platforms you care about
timeout: per-request timeout in seconds, raise it if you suspect false negatives from slow sites
includeNsfw: off by default

Why bother with an Actor

Bulk and scheduled. Put the handles in one run, or schedule a weekly run to see when a brand name shows up on a new platform.
API and integrations. Start it from Python or JavaScript, or from Make, n8n or a webhook, and read the dataset as JSON, CSV or Excel.
Same Sherlock results. Nothing is re-ranked or guessed. A row exists only if Sherlock confirmed the account.

The free plan checks one username on the top 100 sites and shows up to 5 accounts, so you can see the real output before deciding anything. The last row of a free run says what was held back.

Limits worth knowing

Some sites rate-limit or block automated checks, which can produce false negatives. A hit also only shows that a handle exists, not that it belongs to the person you have in mind. Treat results as leads to verify.

It reads only publicly reachable pages and never logs in. Use it on accounts you own or are authorized to investigate, and follow the laws that apply to you, including GDPR and similar privacy rules.

The Actor: Username Search & Lookup on Apify

I build these Actors at Ntriq (Korea). If you need another output field, say so in the comments.

You have a name but no username: find public profiles on 3000+ sites with one API call

ntriq-gh — Tue, 03 Nov 2026 01:01:09 +0000

Most profile lookups start from a handle. In practice you often start from a name: a lead list, a candidate, a counterparty in a due diligence file. Nobody gives you the username.

I built an Apify Actor around the open-source Maigret engine (MIT) that closes that gap. You send a name, it builds the usernames a real person would plausibly have picked, ranks them, and checks each one against the full Maigret site catalogue.

What happens to a name

Give it "John Smith" and it tries candidates such as johnsmith, john.smith, john_smith and smithjohn, best guess first. Each candidate is checked against 3000+ sites. You get one row per confirmed public profile.

Input

names: up to 10 names per run
maxPermutations: how many username candidates to build per name (default 6)
siteScope: top300, top500 or all3000
minConfidence: drop weak matches, useful for common names
monitorMode: on a schedule, return only what changed since the last run

{
  "names": ["John Smith"],
  "maxPermutations": 6,
  "siteScope": "top500",
  "minConfidence": 0.5
}

Output

Every row carries a rowType, so filtering is one expression. A profile-found row looks like this:

{
  "rowType": "profile-found",
  "name": "John Smith",
  "username": "john.smith",
  "site": "GitHub",
  "url": "https://github.com/john.smith",
  "category": "Developer",
  "confidence": 1.0
}

Confidence is about the person, not the account

A match only tells you that a username exists on a site. The confidence score tries to answer a different question: how likely is it that this handle belongs to the person you named. A username that contains every part of the name scores high. A bare first name scores low, because thousands of unrelated people use it.

Treat the result as a lead to verify, not as proof of identity.

Where it fits

sales and recruiting: find the public GitHub, X or Medium presence of a name on a list
due diligence: a first pass before manual review
monitoring: a weekly schedule that reports only new or changed profiles

By default the rows hold account-existence evidence only. Profile fields such as bio or location are opt-in. The Actor only reads publicly reachable pages and never logs in. Use it where you have a lawful basis, and follow the laws that apply to you, including GDPR and similar rules.

The Actor: Social Media Finder by Name on Apify

I build these Actors at Ntriq (Korea). If you want another output field, tell me in the comments.

Search one username across 3000+ sites with an API call (Maigret, hosted)

ntriq-gh — Tue, 27 Oct 2026 23:39:10 +0000

Checking a username across thousands of sites usually means running Maigret locally: install it, let it walk the site list, then clean up the console output. I wrapped the open-source Maigret engine (MIT) as an Apify Actor so the same search runs as an API call and comes back as structured rows.

What you send

The input is small. The fields you will actually touch:

usernames: an array, so a whole investigation list goes in one run
topSites: limit the search to the top N sites (0 means all 3000+, which is slower)
tags / excludeTags: e.g. coding,video to stay on developer and video sites
sites: a fixed list like GitHub,Twitter,Instagram when you only care about a few
noRecursion: skip the follow-up search on extra usernames Maigret discovers

{
  "usernames": ["some_handle", "another_handle"],
  "topSites": 500,
  "tags": "coding,social"
}

What comes back

One row per site where the handle is registered, each with the site name, a direct profile URL and the status. By default the rows hold account-existence evidence only. Turn on extractProfileFields if you also need public profile fields such as name, bio or location. printNotFound adds the misses too, which helps when you have to show what was checked.

Because it is a dataset, you can pull it as JSON or CSV from the API and filter in code instead of grepping a console dump.

Why 3000+ sites matter

Most handle checkers cover a few hundred popular sites. The value of a bigger list is the long tail: forums, gaming, dating and developer sites where people reuse a handle and forget about it. That is where impersonation and fraud checks usually find the extra account.

Where it fits

brand-handle checks before you name a product
due diligence on a counterparty's public handle
the first pass of an OSINT workflow before manual review

Use it only for public profile lookups on subjects you have a legitimate reason to check, and follow the laws that apply to you.

The Actor: Maigret username search on Apify

I build these Actors at Ntriq (Korea). If you want an output field added, say so in the comments.

HTTP 402 + USDC: Pay-per-use AI APIs for Autonomous Agents

ntriq-gh — Tue, 07 Apr 2026 23:02:13 +0000

The Problem with API Keys for AI Agents

AI agents need to call external APIs autonomously. But traditional API key auth has a fundamental mismatch:

Who manages the keys? The agent can't sign up for accounts
How does billing work? Subscription tiers don't fit pay-per-task usage
What about multi-agent systems? Keys get shared, leaked, revoked

There's a better way: let agents pay directly, per request, with crypto.

x402: HTTP 402 as a Payment Protocol

The HTTP 402 status code has been "reserved for future use" since 1991. x402 finally uses it.

The flow:

Agent → POST /document-intel
Server → 402 Payment Required
        { amount: "0.05", currency: "USDC", network: "eip155:8453" }
Agent → EIP-3009 signature (gasless transfer authorization)
Server → Facilitator validates → 200 OK + result

No API keys. No subscriptions. No gas fees. The payment IS the authentication.

ntriq AgentShop: 6 Endpoints, $0.01–$0.05

We built a document intelligence server on this protocol, running 100% local inference on a Mac Mini.

Endpoint	Price	Model
`/document-intel`	$0.05	qwen2.5vl:7b
`/invoice-extract`	$0.03	qwen2.5vl:7b
`/screenshot-data`	$0.02	qwen2.5vl:7b
`/alt-text`	$0.01	qwen2.5vl:7b
`/pii-detect`	$0.02	qwen2.5:7b
`/sentiment`	$0.01	qwen2.5:7b

Cost to run: $0 (local inference). Margin: ~100%.

Try It

# Step 1: Hit the endpoint — you'll get a 402 back
curl -X POST https://x402.ntriq.co.kr/sentiment \
  -H 'Content-Type: application/json' \
  -d '{"text": "This product is amazing"}'

# Response:
# { "x402Version": 1, "accepts": [{ "amount": "10000", "currency": "USDC" }] }

# Step 2: Sign with EIP-3009 and retry
# The x402 client library handles this automatically

Why Local Inference?

Zero marginal cost: Every request is pure profit
No rate limits: Mac Mini handles concurrent requests
Privacy: Documents never leave our infrastructure
Speed: No network hop to OpenAI

qwen2.5vl:7b handles documents, invoices, and screenshots with accuracy comparable to GPT-4V for structured extraction tasks.

MCP Integration

ntriq-agentshop is available as a ClawHub skill. Any agent using OpenClaw or Claude can discover and use these endpoints automatically.

# Install via ClawHub
clawhub install ntriq-document-intelligence-mcp

DEV Community: ntriq-gh

Check a list of usernames across 400+ sites without installing Sherlock

What you get

Input

Why bother with an Actor

Limits worth knowing

You have a name but no username: find public profiles on 3000+ sites with one API call

What happens to a name

Input

Output

Confidence is about the person, not the account

Where it fits

Search one username across 3000+ sites with an API call (Maigret, hosted)

What you send

What comes back

Why 3000+ sites matter

Where it fits

HTTP 402 + USDC: Pay-per-use AI APIs for Autonomous Agents

The Problem with API Keys for AI Agents

x402: HTTP 402 as a Payment Protocol

ntriq AgentShop: 6 Endpoints, $0.01–$0.05

Try It

Why Local Inference?

MCP Integration

Links