DEV Community: George Kioko

Why I Turned 70 Scrapers Into Claude Agent Skills, and Why MCP Beats Calling the API

George Kioko — Fri, 19 Jun 2026 09:10:13 +0000

Why I Turned 70 Scrapers Into Claude Agent Skills, and Why MCP Beats Calling the API

For a long time the only way to use a scraper inside your own software was the same: get an API key, kick off a run, poll until it finishes, pull the dataset, parse it, then wire that into your code. It works. It is also a lot of plumbing for what should be one step.

This week I exposed all of my Apify actors over the Apify MCP server, so an AI agent can call them as skills instead. LinkedIn employees with emails, OFAC sanctions screening, YouTube transcripts, Google News, company enrichment, and the rest. Same actors, same pricing. The difference is how you reach them.

The plumbing problem

Calling an actor through the API looks like this. You authenticate. You start a run with the right input. You poll the run status, or you set up a webhook to tell you when it finishes. You fetch the dataset. You parse the rows. You map them into whatever your app expects. Every one of those steps is code you write, test, and maintain. Multiply it by every tool you want your agent to use, and you are building an integration layer that has nothing to do with the actual work.

What MCP changes

MCP flips it. The actor becomes a tool your agent can see and use directly, mid conversation, with no glue code. The agent reads the tool's schema, calls it, and gets a clean result back. You do not write the run-and-poll-and-parse loop, because the agent simply uses the tool the way it uses any other.

In practice that turns "go build an OFAC integration" into a sentence:

"Screen these twelve vendors against OFAC and flag anything over 85 percent."

The agent runs the actor, reads the matches, and hands you the shortlist. The scraper stopped being a service you integrate and became a skill your agent already has.

Why this matters for anyone building with agents

The whole point of an AI agent is that it does multi step work for you. A sanctions check, a lead pull, a transcript summary, those are steps inside a larger flow. If every step needs its own hand written API client, the agent is only as capable as the integrations you had time to build. MCP removes that ceiling. Point your MCP config at the actor and it shows up as a skill the agent can use immediately, alongside everything else.

How to use mine

Every actor is public under george.the.developer on the Apify Store, and exposed over the Apify MCP server. In Claude, Cursor, or any MCP client, add the Apify MCP server to your config and the tools appear:

https://mcp.apify.com?tools=george.the.developer/ofac-sanctions-screener

Then ask your agent to use it. Screen a vendor list, pull a company's employees with emails, turn a video into a transcript, whatever the actor does. Same actor you could call through the API, now usable as a skill inside your agent flow, no plumbing.

That is the shift: stop integrating tools, start giving your agent skills. Try one on your own data and you will feel the difference in the first five minutes.

Browse the actors here: https://apify.com/george.the.developer

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Feed Your AI Agent Fresh News on Any Topic, No News API

George Kioko — Thu, 18 Jun 2026 13:15:47 +0000

How to Feed Your AI Agent Fresh News on Any Topic, No News API

If you are building anything that needs to know what is happening right now, a brand monitor, a market research agent, a competitor tracker, you hit the same wall. The agent is smart but blind to anything after its training cutoff. The paid news APIs are expensive and rate limited, and most of them cover a narrow slice of sources.

There is a simpler path. You give a topic or a brand name and get back the latest news on it as structured data, ready to feed an agent or drop into a report. Here is the workflow.

The job, stated plainly

You want fresh news on a topic, a company, a person, whatever you are tracking, and you want it as clean structured rows, not a scraped mess of HTML. Title, source, link, date, enough to act on or hand to a model. And you want it to keep up, not to be a one time pull you run by hand.

The slow way is opening Google News, scanning headlines, copying links into a spreadsheet. That does not scale and it goes stale the moment you stop. The tool way pulls the latest on any query in one call.

Step 1: point the actor at a topic

I use the Google News Monitor on Apify. You give it a search query, a brand, a keyword, a person, and it returns the recent news matching it.

{
  "query": "your brand or topic",
  "maxResults": 50,
  "language": "en"
}

Track a competitor, a product category, a regulation, anything you need to stay current on.

Step 2: get structured rows back

Each article comes back as a clean row: title, source, link, and publish date. That is the difference between data you can sort, filter, and feed to a model, and a page of HTML you have to clean first.

For an agent the structure is the point. A list of titles with sources and dates is context your model can summarize, classify, or alert on. A raw page is just noise.

Step 3: turn it into a brief or an alert

Once you have the rows, the rest is your pipeline. Summarize the day's coverage into a brief, classify articles by sentiment, or alert when a competitor shows up in the news. A query becomes a feed, and the feed becomes something your team or your agent acts on.

This is the use case that keeps coming up: give an agent a live window into the news so it is not blind to the present. The monitor is the step that opens that window.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an agent can pull fresh news mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/google-news-monitor

Ask your agent "what is the latest news on this company and summarize the three biggest stories" and it runs the actor, reads the rows, and answers. The news becomes one more source your agent can check on demand.

Why no news API matters

The paid news APIs cost real money, cap your calls, and often miss sources. This gives you the breadth of Google News behind one query, as structured data, without a contract or a key to manage. That is the difference between a feature you can wire into an agent today and a vendor bill you have to justify.

The 10 minute version

Point it at a topic, pull the latest, and feed it into a brief, an alert, or an agent. After the first run you have a repeatable live news feed on anything you track, without a paid news API.

You can run the Google News Monitor here: https://apify.com/george.the.developer/google-news-monitor

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

Turn Any YouTube Video Into Clean Text for Your AI Agent, No API Key

George Kioko — Thu, 18 Jun 2026 13:12:51 +0000

Turn Any YouTube Video Into Clean Text for Your AI Agent, No API Key

If you are building anything that reasons over video, you have hit this wall. The knowledge you want is locked inside a YouTube talk, a tutorial, a podcast episode, and there is no clean way to get the words out. The official captions panel is fiddly, the YouTube Data API does not return transcripts, and the whisper route means downloading the audio and burning compute on something that already exists as text.

There is a faster path. You point a tool at a video URL and get back the full transcript as clean text, ready to feed an agent, a RAG pipeline, or a summary. No API key, no audio download, no whisper. Here is the workflow.

The job, stated plainly

You have a video, or a hundred videos, and you want the spoken words as text you can actually use. Not a caption file with timestamps glued to every line, but clean readable text you can drop into a prompt, chunk for retrieval, or summarize. You want it fast and you want it to scale past doing one video by hand.

The slow way is opening each video, fighting the captions panel, copying broken lines into a doc. That does not scale and it produces messy text. The tool way pulls the transcript straight from the source in one call.

Step 1: point the actor at a video

I use the YouTube Transcript Scraper on Apify. You give it a video URL, or a list of them, and it returns the transcript. No login, no API key, no audio processing.

{
  "videoUrls": ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"],
  "language": "en"
}

Pass one URL for a quick pull, or a batch of them to build a corpus from a whole channel or playlist.

Step 2: get clean text back

Each video returns its transcript as text, plus the basic metadata you need to keep track of which words came from which video. That is the difference between a caption dump you have to clean and text you can feed straight into a model.

For an AI workflow the text is the whole point. A clean transcript is a chunk of context your agent can reason over. A messy caption file with a timestamp on every line wastes tokens and confuses retrieval.

Step 3: feed it to your agent or RAG pipeline

Once you have the text, the rest is your normal pipeline. Chunk it, embed it, drop it into a vector store, or just pass a single transcript into a prompt for a summary or a question answer. A talk becomes searchable knowledge. A playlist becomes a corpus your agent can answer from.

This is the use case that keeps showing up: turn video into knowledge an agent can use. The scraper is the first step that makes the rest possible.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an agent can pull a transcript mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/youtube-transcript-scraper

Ask your agent "pull the transcript of this talk and summarize the three main arguments" and it runs the actor, reads the text, and answers. The video becomes one more source your agent can read on demand, inside whatever flow it already runs.

Why no API key matters

The YouTube Data API does not give you transcripts, and the workarounds either need OAuth setup or push you into downloading audio and running speech to text on words that already exist as captions. This skips all of that. You give a URL, you get text. That is the difference between a step you can wire into an agent in five minutes and a side project you have to maintain.

The 10 minute version

Point it at a video or a list, pull the transcripts, and feed them into whatever you are building. After the first run you have a repeatable way to turn any video into clean text for an agent, a RAG store, or a summary, without an API key and without touching audio.

You can run the YouTube Transcript Scraper here: https://apify.com/george.the.developer/youtube-transcript-scraper

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Build a Company Employee List With Emails From LinkedIn, No Login

George Kioko — Thu, 18 Jun 2026 09:46:29 +0000

How to Build a Company Employee List With Emails From LinkedIn, No Login

If you do sales or recruiting, you have hit this wall. You find a company you want to sell into or hire from, you open LinkedIn to see who works there, and within a few profile views LinkedIn throws up the login wall, then the rate limit, then the account warning. Doing it from your own logged in account is how you get your account restricted.

There is a cleaner way to pull a company's employees with their work emails, without logging in and without burning a LinkedIn account. Here is the exact workflow.

The job, stated plainly

You have a company. You want a list of the people who work there, their titles, and a work email for each, so you can actually reach the decision makers instead of staring at a profile you cannot contact. You want it as a clean export you can drop into your CRM or a sequence, not fifty browser tabs.

The slow way is opening profiles one at a time from your own account. That does not scale and it puts your account at risk. The tool way pulls the whole roster in one run from infrastructure that is not your personal login.

Step 1: point the actor at a company

I use the LinkedIn Company Employees Scraper on Apify. You give it a company, either the LinkedIn company URL or the name, and it returns the people who work there. No login, no cookies from your account, so your own profile never touches the request.

{
  "company": "https://www.linkedin.com/company/stripe",
  "maxResults": 200,
  "findEmails": true
}

Set maxResults to keep the list focused, and turn on email finding so each person comes back with a work email where one can be resolved.

Step 2: read what comes back

Each employee returns as a structured row: full name, job title, the company, profile URL, and a work email when it can be found and verified. That is the difference between a list of names you cannot use and a list you can actually send to.

The email step is the part that matters. A list of two hundred names with no contact path is busywork. A list where the head of growth and the VP of sales come back with a deliverable email is a pipeline.

Step 3: filter to the people who matter

Sort by job title and keep the roles you actually sell to or recruit for. Founder, VP, head of, director, whatever your buyer or candidate looks like. A roster of two hundred becomes a target list of the fifteen people worth a message, each with a name, a title, and an email already attached.

That is the whole point. You are not contacting two hundred people. You are finding the handful who decide and reaching them directly.

Step 4: run it inside Claude or any AI agent (MCP)

The actor is exposed over the Apify MCP server, so an AI agent can build the list mid conversation with no glue code:

https://mcp.apify.com?tools=george.the.developer/linkedin-company-employees-scraper

Ask your agent "pull the marketing and sales leaders at these ten companies with their emails" and it runs the actor, filters the roles, and hands you a ready list. The prospecting becomes one step inside whatever outreach flow your agent already runs.

Why no login is the whole game

Every LinkedIn scraping headache traces back to one thing: people run it from their own logged in account, then the account gets warned, restricted, or banned. The no login approach moves the request off your personal profile entirely. Your account stays clean because it was never in the loop. That is the difference between a tool you can run every day and one that gets your LinkedIn flagged in a week.

The 15 minute version

Point it at a company, turn on email finding, run it, filter to the titles you want, export to CSV or push to your CRM. After the first run you have a repeatable way to turn any company into a contactable target list, without ever risking your own account.

You can run the LinkedIn Company Employees Scraper here: https://apify.com/george.the.developer/linkedin-company-employees-scraper

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

How to Screen a Vendor List Against OFAC Sanctions in 20 Minutes

George Kioko — Thu, 18 Jun 2026 06:27:36 +0000

How to Screen a Vendor List Against OFAC Sanctions in 20 Minutes

If your company pays vendors, onboards customers, or moves money across a border, you are on the hook for OFAC sanctions screening. The US Treasury publishes the SDN list, and checking your counterparties against it is not optional. The problem is that most teams still do it the slow way: one name at a time in the public search box, copying results into a spreadsheet, hoping nobody fat-fingered a transliteration.

There is a faster way that takes about 20 minutes to set up and then runs on a whole list in one shot. Here is exactly how I do it.

The job, stated plainly

You have a list of names. Companies, people, vessels, whatever your counterparties are. You need to know which of them match an entry on the OFAC SDN list, how strong each match is, and enough surrounding data to either clear the name or send it to a human for review. You want an audit trail for the ones you cleared, because "we screened everyone" is a sentence you may have to prove later.

Doing that by hand does not scale past a few names. The single name search also misses the thing that actually catches sanctioned parties: spelling variants. "Ivan Petrov" and "Ivan Petroff" are the same person to a fuzzy matcher and two different rows to a copy and paste workflow.

Step 1: point an actor at your list

I use the OFAC Sanctions Screener on Apify. You give it a list of entity names, either inline or as a file, and it screens every one against the current SDN list. The whole input is this small:

{
  "entities": ["Acme Trading LLC", "Ivan Petrov", "MV Northern Star"],
  "minConfidence": 80
}

The minConfidence setting controls how aggressive the fuzzy matching is. Start at 80, then tune it once you see how your real data behaves. Lower it and you catch more variants but review more false positives. Raise it and you review less but risk missing a sloppy transliteration.

Step 2: run it and read the output

Each name comes back as a structured record. The fields that matter for a screening decision:

match_confidence, a 0 to 100 score for how close the name is to an SDN entry
risk_level, bucketed into clear, review, or high
matched_name, the exact SDN entry that triggered the hit
aliases and addresses for the matched entity
the sanctions program the entry falls under

The fuzzy match is the whole point. It runs your input against primary names and known aliases, so a name that is spelled a little differently than the official listing still surfaces. That is the failure mode that gets compliance teams in trouble, and it is the one a manual search quietly skips.

Step 3: filter to what a human reviews

Sort the output by match_confidence. Everything above your threshold is the short list a compliance analyst actually looks at. Everything below is cleared, and you keep the full output as the record that you screened the whole book. A list of three hundred vendors becomes a review queue of maybe five, with the data for each one already attached.

That is the part that turns a day of work into a coffee break. You are not screening fewer names. You are screening all of them and only spending human attention on the handful that need a judgment call.

Step 4: make it run inside your AI agent

This is the part that surprises people. The actor is exposed over the Apify MCP server, which means an AI agent can call it mid conversation. If you work in Claude, you point your MCP config at the Apify server:

https://mcp.apify.com?tools=george.the.developer/ofac-sanctions-screener

Now you can hand your agent a list and say "screen these twelve vendors against OFAC and flag anything over 85 percent," and it runs the actor, reads the matches, and gives you back the shortlist. The screening becomes one step inside a larger onboarding or due diligence workflow the agent is already running, instead of a separate tool you context switch into.

Why this beats the enterprise tools for most teams

The big sanctions screening platforms do this well and charge enterprise money for it, often five to six figures a year. If you are a bank with a dedicated compliance department, that is the right tool. If you are a fintech onboarding customers, an importer checking a new supplier, or a consultant running due diligence for clients, you are paying for a department you do not have. Screening per entity, at a cent a name, covers the same first pass screening without the contract.

The honest line: a tool gives you a fast, auditable first pass. The final compliance call stays with your team, because you know your own risk policy and your regulator does not accept "the API said it was fine." What you get is the slow part automated and the judgment part handed to you on a plate.

The 20 minute version

Set up the input once, run it on your real list, tune minConfidence to your data, and wire it into whatever onboarding flow or agent you already run. After that, screening a new batch is one call. The next time someone asks whether you checked a counterparty against OFAC, the answer is yes, with the record to show it.

You can run the OFAC Sanctions Screener here: https://apify.com/george.the.developer/ofac-sanctions-screener

Source and verification reports: github.com/the-ai-entrepreneur-ai-hub/apify-actor-portfolio.

June 16 Apify Store Read: Promote Buyer Outcomes, Not A Scraper Catalog

George Kioko — Wed, 17 Jun 2026 04:41:45 +0000

I pulled a fresh read-only Apify Store check before promoting anything today.

The portfolio is still moving:

70 public actors
70,652 total Store runs
2,790 total Store users
17,223 rolling 30-day runs
16,824 rolling 30-day successful runs

The mistake would be turning that into another catalog post.

"I have 70 actors" is not a buyer offer.

The better move is to package working actors into workflows that map to a budget owner.

Content QA

The content lane is:

AI Content Detector
AI Text Humanizer API

The AI Content Detector has 2,924 rolling 30-day runs and 99.9% recent public-run success. AI Text Humanizer has 111 rolling 30-day runs and 100% recent public-run success.

That is a better pitch than "AI detector API."

The buyer outcome is:

Check large volumes of content before publishing, review, moderation, or education workflows.

Video And Market-Intel Ingestion

The research lane is:

YouTube Transcript Scraper
Telegram Channel Scraper
Google News Monitor

YouTube Transcript has 4,211 total runs, 363 users, 77 users in the last 30 days, and 98.8% recent success.

Telegram has 6,351 total runs, 120 users, and 99.0% recent success.

Google News Monitor has 1,801 total runs, 69 users, and 100% recent success.

The buyer outcome is:

Turn videos, channels, and news sources into structured input for RAG, creator research, and market-intelligence workflows.

Sales Data Hygiene

The RevOps lane is:

Email Validator API
Company Enrichment API

Email Validator has 2,287 rolling 30-day runs and 100% recent success. Company Enrichment has 97 users, 28 users in the last 30 days, and 95.4% recent success.

The buyer outcome is:

Clean and enrich outbound lists before spending money on outreach.

Local Lead Packs

The higher-ticket lane is still local lead research.

That stack includes:

Google Maps lead discovery
website audit
company enrichment
email validation
niche lead finders for law firms, dentists, HVAC, restaurants, and other local markets

The better sales motion is not:

Go run this scraper.

The better sales motion is:

I will produce a scored local lead pack for one niche and city, with website checks, enrichment, and outreach angles.

That can start as a $300-$500 proof, then turn into a pilot or recurring workflow if the output is useful.

What I Am Not Pushing Hard Today

LinkedIn has the biggest demand, but the current public 30-day run stats still show too many failures, aborted runs, and time-outs for me to make it today's main public push.

Google Scholar and TikTok Shop both have demand, but they are careful-lane actors right now.

Fix-first actors stay out of hard promotion until the reliability story improves.

Demand is useful. Broken demand burns trust.

The portfolio is here:

https://apify.com/george.the.developer

Scraping LinkedIn company employees without a login (and without flagging your account)

George Kioko — Tue, 16 Jun 2026 06:12:45 +0000

Every LinkedIn scraper I tried fell into one of two buckets. Either it wanted my session cookies, which is a great way to get your own account restricted, or it scraped the logged-in DOM and broke the next time LinkedIn shuffled a class name. Neither felt like something I wanted running on a schedule.

So I built the version I actually wanted to use. It works off public company pages, the ones you can see without being logged in. No cookies, no credentials, nothing tied to a real account that can get burned.

What it does

You give it a company. It returns the employees it can find: full names, job titles, profile links, and emails where they genuinely exist. Output is CSV, JSON or Excel, so it lands straight in a sheet or a pipeline without a conversion step.

There is a verification pass on the employer field so you are not getting the 60 percent false-positive rate you see when a scraper trusts whatever the page first renders. If the current-employer signal does not check out, that row does not ship.

How the pricing works

It bills per result, not per month. Starting a run is a fraction of a cent, each profile is a couple cents, and the email-enriched profiles cost a little more because the enrichment step does real work. The point is that an empty run does not cost you a subscription you were not using that month.

Where it is

It is on the Apify Store, currently sitting at 353 users with a Rising Star badge, which mostly tells me it keeps working under load rather than just on my machine.

https://apify.com/george.the.developer/linkedin-company-employees-scraper

If you run it on something weird and it chokes, tell me. That is the fastest way it gets better.

June 13 Apify Scale Lane: The Actors I Would Promote Today

George Kioko — Sat, 13 Jun 2026 07:53:02 +0000

June 13 Apify Scale Lane: The Actors I Would Promote Today

I pulled a fresh read-only Apify Store check on June 13 before promoting anything.

The portfolio is still getting used:

69 public actors checked
68,514 total Store runs
2,695 total Store users
15,775 rolling 30-day runs
15,356 rolling 30-day successful runs
97.3% rolling 30-day success across the portfolio

The important part is not "promote everything."

The important part is promoting the actors that have both movement and completion. If an actor is getting demand but failing too often, I do not want it as the public CTA until it is repaired.

Today, the scale lane is clear.

The actors I would put in front of buyers today

These actors have recent Store movement and 96%+ rolling 30-day success in the June 13 read.

Actor	Delta runs	Delta users	30d runs	30d success
AI Content Detector	1,919	2	2,107	99.9%
Email Validator API	1,102	2	1,992	100.0%
Telegram Channel Scraper	679	19	991	98.8%
YouTube Transcript Scraper	498	56	1,107	98.8%
Google News Monitor	115	11	217	100.0%
AI Text Humanizer API	94	8	113	100.0%
TikTok Shop Affiliate Sales Scraper	68	20	101	96.0%
Shopify DTC Brand Discovery	32	12	79	98.7%
TikTok to Shopify Trending Products	26	5	43	100.0%
HVAC Contractor Lead Finder	23	3	40	100.0%

Those are the lanes I would push today.

They map to actual buying intent:

AI teams need transcripts, AI detection, and text humanization.
Marketing teams need Telegram monitoring, Google News monitoring, and ecommerce product discovery.
Agencies need local lead lists and verified contact data.
Ecommerce operators need TikTok Shop and Shopify intelligence.

Start with the obvious AI data lane

The AI lane has the cleanest story.

Use YouTube Transcript Scraper when you need video transcripts for:

RAG pipelines
video summarization
creator research
podcast repurposing
competitive channel analysis
training-data preparation

Actor:

https://apify.com/george.the.developer/youtube-transcript-scraper

Use AI Content Detector when you need to classify generated text at scale:

https://apify.com/george.the.developer/ai-content-detector

Use AI Text Humanizer API when the workflow needs a rewrite layer after detection:

https://apify.com/george.the.developer/ai-text-humanizer-api

That is one complete workflow: collect source material, detect generated copy, and rewrite text for downstream use.

Monitoring lane: Telegram and news

Telegram Channel Scraper has one of the best user-growth signals in the June 13 read:

6,310 total runs
119 total users
19 users in the last 30 days
991 rolling 30-day runs
98.8% rolling 30-day success
+679 runs and +19 users since the May 27 baseline

Actor:

https://apify.com/george.the.developer/telegram-channel-scraper

That is useful for people tracking:

crypto communities
product launches
public market chatter
competitor announcements
niche communities where the signal does not start on Google

Google News Monitor is the cleaner mainstream-news companion:

https://apify.com/george.the.developer/google-news-monitor

It is smaller than Telegram, but the June 13 read shows 100% rolling 30-day success and +11 users since the May 27 baseline.

Ecommerce and agency lane

The ecommerce lane is worth promoting because buyers understand the outcome quickly.

TikTok Shop Affiliate Sales Scraper:

https://apify.com/george.the.developer/tiktok-shop-affiliate-sales-scraper

Shopify DTC Brand Discovery:

https://apify.com/george.the.developer/shopify-dtc-brand-discovery

TikTok to Shopify Trending Products:

https://apify.com/george.the.developer/tiktok-to-shopify-trending-products

That lane is for operators looking for products, brands, and sales motion instead of generic scraped rows.

The agency lane is even more direct. HVAC Contractor Lead Finder is small but clean in this read:

+23 runs since the May 27 baseline
40 rolling 30-day runs
100% rolling 30-day success

Actor:

https://apify.com/george.the.developer/hvac-contractor-lead-finder

What I am not pushing as the main CTA

Some actors still show demand, but I would not make them the main promotion today.

Actor	Reason
Influencer Marketing Intel	87.7% rolling 30-day success
Lead Enrichment Pipeline	84.9% rolling 30-day success
US Tariff Lookup	86.7% rolling 30-day success
Obituary Life Insurance Leads	83.3% rolling 30-day success
AI Deepfake Detector	0% rolling 30-day success in this read
Web Content Extractor API	0% rolling 30-day success in this read
Amazon Product Data	0% rolling 30-day success in this read

Demand is a signal.

Reliability decides whether I promote it today or fix it first.

The June 13 promotion filter

The rule for today is simple:

lead with actors above 95% rolling 30-day success
use current Store movement as proof
package actors by buyer outcome, not by scraper category
do not send buyers into the fix-first lane

Main portfolio:

https://apify.com/george.the.developer

Promote the Paid Apify Actors That Still Complete: YouTube Transcript Is the Lead Today

George Kioko — Thu, 11 Jun 2026 14:42:07 +0000

Promote the Paid Apify Actors That Still Complete: YouTube Transcript Is the Lead Today

I pulled a fresh read-only Apify Store check on June 11 before promoting anything.

The portfolio is still moving:

69 public actors checked
67,218 total Store runs
2,662 total Store users
14,810 rolling 30-day runs
14,354 rolling 30-day successful runs
96.9% rolling 30-day success across the portfolio

Revenue is not where I want it, but the right move is not to promote every actor.

The right move is to promote the paid actors that are still completing reliably, and hold back the ones that need repair.

Today, the lead CTA is the YouTube Transcript Scraper.

Why YouTube Transcript is the lead today

The June 11 read shows:

4,080 total runs
352 total users
86 users in the last 30 days
1,089 rolling 30-day runs
98.9% rolling 30-day success
+466 runs and +54 users since the May 27 baseline

That is the kind of paid actor worth putting in front of buyers.

It has proven demand, strong completion, and a use case that maps directly to AI workflows.

Actor:

https://apify.com/george.the.developer/youtube-transcript-scraper

What it does

The actor extracts YouTube transcripts, subtitles, captions, timestamps, and metadata without needing the official YouTube API.

That makes it useful for:

RAG pipelines over videos
podcast and video summarization
creator research
SEO and content repurposing
training-data preparation
competitive research over channels and playlists

Instead of manually opening videos, copying captions, and cleaning text, you can send video URLs and get structured data back.

Quick start

Example input:

{
  "videoUrls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
  ],
  "language": "en"
}

JavaScript client example:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor('george.the.developer/youtube-transcript-scraper').call({
  videoUrls: ['https://www.youtube.com/watch?v=dQw4w9WgXcQ'],
  language: 'en',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

The local actor config is pay-per-event, with an actor-start event and a transcript-extracted event. That means the actor is structured for paid usage rather than just being a demo.

The rest of the reliable paid lane

YouTube is not the only actor worth promoting today.

The clean lane from the June 11 read:

Actor	Delta runs	Delta users	30d runs	30d success
AI Content Detector	1,727	2	2,107	99.9%
Email Validator API	1,032	2	1,923	100.0%
Telegram Channel Scraper	660	16	991	98.6%
YouTube Transcript Scraper	466	54	1,089	98.9%
Google News Monitor	96	9	223	100.0%
Bluesky Scraper	96	1	161	100.0%
AI Text Humanizer API	93	8	114	100.0%
Shopify DTC Brand Discovery	32	12	79	98.7%
HVAC Contractor Lead Finder	22	3	40	100.0%
Law Firm Lead Finder	21	3	35	100.0%

That gives me three clean promotion angles:

AI and content workflows: YouTube Transcript, AI Content Detector, AI Text Humanizer.
Monitoring workflows: Telegram Channel Scraper, Google News Monitor, Bluesky Scraper.
Buyer outcome workflows: Shopify DTC Brand Discovery, HVAC Lead Finder, Law Firm Lead Finder.

What I am holding back

Some actors still have demand, but I do not want them as the main public CTA today.

Actor	Reason
TikTok Shop Affiliate Sales Scraper	87.3% 30-day success
Influencer Marketing Intel	87.3% 30-day success
Lead Enrichment Pipeline	81.8% 30-day success
US Tariff Lookup	86.7% 30-day success
MedSpa Lead Discovery PPE	89.7% 30-day success

Demand is useful.

Broken demand burns trust.

If an actor has too many failures, aborts, or timeouts, the right move is QA first and promotion second.

The June 11 promotion filter

The rule today is simple:

promote paid actors above 95% 30-day success
lead with use cases buyers understand
use strong user growth as trust proof
keep careful actors out of the main CTA
fix weak-success actors before sending more traffic

Main actor today:

https://apify.com/george.the.developer/youtube-transcript-scraper

Full portfolio:

https://apify.com/george.the.developer

My Apify Promotion Filter: Scale Clean APIs, Hold Back Noisy Demand

George Kioko — Sun, 07 Jun 2026 04:05:11 +0000

I pulled a fresh read-only Apify Store check on June 6 after seeing revenue cool down from the better days earlier in the week.

The portfolio is not dead. The traffic is still moving:

63,720 total runs
2,532 total users
+426 runs since the last fresh read
+25 users since the last fresh read

The problem is not whether the actors exist. The problem is where the promotion is pointed.

When I promote the whole catalog, the message is too broad. A buyer does not wake up looking for "66 Apify actors." They wake up needing validated emails, YouTube transcripts, domain/company intelligence, local business lists, or a way to score prospects before outreach.

So today's rule is simple:

Promote the monetized actors that are reliable enough to absorb traffic.

Hold back the ones with demand but weak completion until they are repaired.

The Scale Lane

These are the actors I am comfortable putting in public today because the current 30-day Store stats show strong reliability.

Actor	Rolling 30d runs	30d success
Email Validator API	1,575	100%
Domain WHOIS Lookup	1,654	99.9%
YouTube Transcript Scraper	1,123	98.8%
Telegram Channel Scraper	861	98.6%
AI Content Detector	621	99.8%
Company Enrichment API	395	96.0%
Google News Monitor	232	100%

That list has two jobs.

First, it proves the portfolio is not just a batch of abandoned demos. These actors are getting used and completing at a level I can defend.

Second, it gives the buyer a clean entry point. A developer building an AI workflow can start with transcripts, enrichment, WHOIS, news monitoring, or validation without taking on the risk of a noisier scraper.

The Money Lane

The second lane is the buyer-outcome stack.

These actors are not the highest-volume tools in the catalog, but they map more directly to business value:

Actor	Rolling 30d result
HVAC Contractor Lead Finder	40/40 succeeded
Law Firm Lead Finder	35/35 succeeded
Dental Practice Lead Finder	31/31 succeeded
Google Maps Leads + Website Audit	29/29 succeeded
Google Maps Lead Intelligence	29/30 succeeded
Restaurant Lead Finder	33/34 succeeded

The pitch here is not "scrape a website."

The pitch is:

Find local businesses.
Score the website and digital presence.
Pull vertical-specific prospect lists.
Enrich the company data.
Validate emails before outreach.

That is a workflow an agency, consultant, or local sales team can understand.

The Hold-Back Lane

This is the part that matters if you care about revenue instead of vanity traffic.

Some actors have demand, but I do not want them as the main CTA today:

Actor	Current issue
TikTok Shop Affiliate Intelligence	84.0% 30d success
Google Scholar Scraper	80.1% 30d success
Reddit Scraper Pro	87.5% 30d success
Lead Enrichment Pipeline	65.6% 30d success
Influencer Marketing Intel	86.8% 30d success

Demand is useful. Broken demand burns trust.

If an actor has buyers but too many aborts, timeouts, or failures, the move is QA first and promotion second.

The Promotion Filter

This is the filter I am using today:

Scale anything above 95% 30-day success with meaningful use.
Use high-volume utility APIs as trust proof.
Put buyer-outcome lead actors in the main CTA.
Keep 90-95% actors in the careful lane.
Hold back anything under 90% until reliability improves.

That means the main public profile is still the best single entry point:

https://apify.com/george.the.developer

But the copy should not say "here are all my actors."

The copy should say:

Use the clean APIs for reliable data workflows.
Use the local lead stack when you need a buyer outcome.
Do not push noisy actors until the completion rate is fixed.

That is the playbook for today.

Stop Promoting a Scraper Catalog. Promote a Local Lead Research Stack.

George Kioko — Sat, 06 Jun 2026 05:41:14 +0000

I pulled a fresh read-only Apify Store check late on June 5.

The portfolio is moving:

66 public Store actors
63,294 total runs
2,507 total users
15,584 rolling 30-day runs
15,073 rolling 30-day successful runs

But the useful revenue lesson is not "post all the actors again."

The useful lesson is this:

Traffic and revenue are not the same thing. Utility actors can bring a lot of runs, but the bigger buyer intent is in the local lead stack.

The Local Lead Stack

This is the stack I am promoting harder now:

Google Maps Lead Intelligence
Google Maps Leads + Website Audit
HVAC Contractor Lead Finder
Law Firm Lead Finder
Dental Practice Lead Finder
Restaurant Lead Finder
Company Enrichment API
Email Validator API

That is a workflow, not a catalog.

For an agency, consultant, broker, or operator, the pitch is simple:

Find local businesses.
Score their websites.
Pull vertical-specific lead lists.
Enrich the companies.
Validate emails before outreach.

That is much clearer than saying "I have a bunch of scrapers."

Current Store Proof

The local lead actors are not the highest-volume actors in the portfolio, but they map to a buyer outcome.

Current Store stats from the June 5 read:

Actor	Rolling 30d runs	Success
HVAC Contractor Lead Finder	40	100%
Law Firm Lead Finder	35	100%
Dental Practice Lead Finder	31	100%
Google Maps Lead Intelligence	30	96.7%
Google Maps Leads + Website Audit	29	100%
Restaurant Lead Finder	34	97.1%

The support actors make the stack more useful:

Actor	Rolling 30d runs	Success
Email Validator API	1,507	100%
Domain WHOIS Lookup	3,054	100%
YouTube Transcript Scraper	1,100	98.7%
Telegram Channel Scraper	857	98.6%
Company Enrichment API	606	97.4%
Google News Monitor	239	100%

Why This Works Better Than a Catalog

A catalog asks the buyer to think.

A stack gives them a path.

"Here are 65 paid actors" is broad, but broad is not always helpful.

"Here is a local lead research stack for finding, scoring, enriching, and validating prospects" is specific. It tells the buyer what job the tools do.

Example Workflow

Start with a market:

dentists in Austin
law firms in Atlanta
HVAC contractors in Phoenix
restaurants in Miami

Then run the stack:

Use a vertical lead finder or Google Maps actor to find businesses.
Use the website audit actor to inspect digital presence.
Use Company Enrichment API to add firmographic details.
Use Email Validator API before sending outreach.

The buyer does not care that this is a scraper portfolio. They care that it helps build a prospecting list.

Quick Start With Apify Client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor('george.the.developer/law-firm-lead-finder').call({
  searchQuery: 'personal injury law firms in Atlanta GA',
  maxResults: 100,
  enrichWebsites: true,
  validateEmails: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.slice(0, 3));

Check each actor's input schema before running at scale. The important idea is the stack: discovery, audit, enrichment, validation.

What I Am Holding Back

Some monetized actors still have demand, but I am not putting them in the main cash CTA until reliability improves:

TikTok Shop Affiliate Sales Scraper
Google Scholar Scraper
Lead Enrichment Pipeline
Reddit Scraper Pro
Influencer Marketing Intel

Demand is useful. Broken demand burns trust.

Links

Full Apify profile:

https://apify.com/george.the.developer

Local lead actors:

Quiet Apify Store Day? Promote the Actors That Complete, Not the Ones That Need Repair

George Kioko — Thu, 04 Jun 2026 07:59:18 +0000

Revenue days do not always move in a clean line.

On June 4, my public Apify Store read was flat versus June 3: 65 active pay-per-event actors, 62,169 all-time monetized runs, 2,424 all-time users, 16,945 rolling 30-day public runs, and a 96.7% rolling 30-day success rate. That does not mean the portfolio is dead. It means the promotion lane has to get narrower.

When the Store is quiet, I do not promote every actor equally. I split the portfolio into three groups:

buyer-outcome actors that solve a direct cash problem
utility APIs that run cleanly and can sit inside someone else's workflow
actors that need QA before they deserve harder promotion

That keeps the message honest and gives buyers a cleaner path to the tools that are ready today.

The buyer-outcome lane

These are the actors I promote when the goal is direct revenue. They are not abstract data tools. They map to sales, outreach, lead generation, or market research work that a buyer can monetize.

Law Firm Lead Finder

Use this when you need a repeatable list of law firms for outreach, local lead generation, or agency research.

Public Store stats from today's read:

33 rolling 30-day runs
33 successful rolling 30-day runs
100% rolling 30-day success

Store link: https://apify.com/george.the.developer/law-firm-lead-finder

Dental Practice Lead Finder

Dental offices are a good fit for local SEO, ads, review management, and appointment-setting offers. This actor is built for that kind of prospecting workflow.

Public Store stats from today's read:

30 rolling 30-day runs
30 successful rolling 30-day runs
100% rolling 30-day success

Store link: https://apify.com/george.the.developer/dental-practice-lead-finder

Google Maps Leads + Website Audit

This is useful when a raw business list is not enough. The extra website audit angle helps separate weak digital presence from stronger prospects.

Public Store stats from today's read:

29 rolling 30-day runs
29 successful rolling 30-day runs
100% rolling 30-day success

Store link: https://apify.com/george.the.developer/google-maps-leads-website-audit

Restaurant Lead Finder

Restaurants are another direct outreach lane: websites, ordering flows, local ads, review systems, delivery tooling, and marketing services.

Public Store stats from today's read:

34 rolling 30-day runs
33 successful rolling 30-day runs
97.1% rolling 30-day success

Store link: https://apify.com/george.the.developer/restaurant-lead-finder

The utility API lane

These are not always flashy, but they are exactly the kind of actors that can become embedded in other products, agents, dashboards, and internal workflows.

Email Validator API

Use this before cold outreach, CRM imports, signup checks, or enrichment workflows. It covers email validation, disposable detection, and MX-related checks in one API-style actor.

Public Store stats from today's read:

1,371 rolling 30-day runs
1,371 successful rolling 30-day runs
100% rolling 30-day success

Store link: https://apify.com/george.the.developer/email-validator-api

Domain WHOIS Lookup

Domain age, expiry, registrar, and DNS details are useful for lead scoring, fraud checks, market maps, and due diligence tools.

Public Store stats from today's read:

5,007 rolling 30-day runs
5,007 successful rolling 30-day runs
100% rolling 30-day success

Store link: https://apify.com/george.the.developer/domain-whois-lookup

YouTube Transcript Scraper

This is a strong utility actor for AI builders, content teams, research workflows, and RAG pipelines that need transcript data without managing a custom scraper.

Public Store stats from today's read:

1,085 rolling 30-day runs
98.8% rolling 30-day success
28 users in the last 7 days

Store link: https://apify.com/george.the.developer/youtube-transcript-scraper

Telegram Channel Scraper

Good fit for market monitoring, community intelligence, crypto research, and niche data feeds where Telegram content matters.

Public Store stats from today's read:

861 rolling 30-day runs
98.8% rolling 30-day success
7 users in the last 7 days

Store link: https://apify.com/george.the.developer/telegram-channel-scraper

Company Enrichment API

This one is for turning a domain or company target into a richer company profile for sales workflows, prospect research, and internal tools.

Public Store stats from today's read:

599 rolling 30-day runs
97.3% rolling 30-day success
12 users in the last 7 days

Store link: https://apify.com/george.the.developer/company-enrichment-api

Google News Monitor

This fits brand monitoring, market alerts, competitor tracking, and lightweight news intelligence.

Public Store stats from today's read:

270 rolling 30-day runs
270 successful rolling 30-day runs
100% rolling 30-day success
8 users in the last 7 days

Store link: https://apify.com/george.the.developer/google-news-monitor

The hold-back lane

Some actors are still monetized, but I do not want to push them hard until the reliability story is stronger.

Today that hold-back list includes:

TikTok Shop Affiliate Sales Scraper
Google Scholar Scraper
HVAC Contractor Lead Finder
Lead Enrichment Pipeline
Reddit Scraper Pro
Influencer Marketing Intel
Medspa Lead Discovery

That does not mean those actors are worthless. It means they should get QA, better docs, or tighter inputs before they become the main public pitch.

Quick start with any Apify actor

Here is the basic Node.js pattern:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor('george.the.developer/youtube-transcript-scraper').call({
  urls: ['https://www.youtube.com/watch?v=dQw4w9WgXcQ'],
  language: 'en',
  outputFormat: 'both',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Swap the actor ID and input for the lane you are using.

My practical rule

When revenue drops, do not blast the whole catalog.

Promote the actors that already complete cleanly. Send buyer traffic to tools with a direct use case. Keep the weaker actors in a repair lane until the public numbers support a harder push.

Full Apify profile: https://apify.com/george.the.developer