I keep seeing the same video on repeat: "Claude runs your whole YouTube channel" or "this AI agent finds clients for you". Ever

So I read the source and put four of those tools into my own production pipeline. Two of them are now part of every post I publish, including this one.

What a skill actually is

A skill is a folder with a SKILL.md instruction file and a few dependency-free Python scripts. The agent loads it as a working mode. No framework, no vector database, no API keys. My four live in a skills directory and run from any agent session.

1. Hook formulas: 47 ways to open

A hook is the first fifteen seconds, or the first line of a post. It has three jobs: confirm what the title promised, raise a question the reader cannot close, and prove the payoff exists. A hook that only does the third is a summary, and it leaks viewers.
The bank is two files: 21 formulas from a YouTube pack, 26 from an Instagram pack with 19 unique ones (Cold Open Demo, The Flop Record, The Objection, Cost Confession).
The scorer is the interesting part: the verdict is 60 percent the mean of five properties and 40 percent the weakest one, because averages hide the exact place a hook leaks. A real run:

SPECIFICITY 84 ADDRESS 26 <- say 'you' in the first six words STAKES 62 CURIOSITY 41 BREVITY 100 VERDICT 48 WEAK 

A hook with a strong number still scored WEAK, because it never addressed the viewer.

2. Viral radar: ideas are not view counts

Raw views rank channel size. 400,000 views on a channel with a 2 million median is a normal Tuesday; 400,000 on a channel with a 30,000 median is the one to study. So every video is scored as a multiple of its own channel's median, and a channel needs at least four videos for the median to mean anything.

yt-dlp --flat-playlist -J "https://www.youtube.com/@channel/videos" > ch1.json python3 swipe.py collected.json --min 2.0 

5.88x 50,000 vs 8,500 median "97% of developers quit a blog before post 30" 3.90x 410,000 vs 105,000 median "Why your first 30 seconds lose viewers" 

3. Lead pipeline: stages talk to the database, not to each other

This one is an adaptation of Harvey. Each stage asks the database "who have I not looked at, or not looked at recently", so any run survives a restart, and re-reading a site after 90 days turns the same signal into a time series (that is how you notice a client changed their platform).

python3 discover.py "Saratov" --cat furniture --limit 15 # OpenStreetMap, free python3 profile.py --limit 11 # CMS fingerprint python3 enrich.py --limit 4 # people, email, MX 

One run: 11 furniture companies with websites in a Russian city, sorted by engine (two Bitrix stores, WordPress, Nuxt). The CMS fingerprint is the core: you do not just know who they are, you know what their site runs on, and the offer follows.
Enrichment ranks contacts honestly: an email listed on the site beats a guessed pattern with a live MX record, and no MX means the address does not exist.

4. Script doctor: anti-slop and timing

The detector scores five properties: sentence-length variation, specificity (numbers, names, currency), slop density against an editable lexicon, the machine fingerprint (invisible characters, typographic tells) and voice. A typical AI-written post scored 11 out of 100; a real case study scored 55.
The timing tool turns a script into timecodes at about 165 words per minute and flags a hook longer than three seconds, a beat longer than four, three lines in a row with nothing checkable, and an ending that does not echo the opening.

Gotchas

• Overpass accepts form-encoded bodies only, and public servers flake with 502: back off across mirrors.
• Tarpit servers accept the connection and never answer, so fetches run in a thread with a hard join and redirects disabled.
• The slop scorer is calibrated on English; treat non-English numbers as a hint.
• In Russian the em dash is normal punctuation, so the check is language-aware. ## Why publish it The barrier was never secrecy, it was discipline. The same code sits in public repositories while funnels sell retellings of it. The difference between a retelling and a production line is the gates, the lexicon, and running your own drafts through the detector every morning. Full manual: https://automata.sale/en/blog/ai-automation/agent-skills-content-factory-manual/