YouTube Script AI Analyzer - Hooks, Pacing & CTA Teardown

What does YouTube Script AI Analyzer do?

The YouTube script analyzer for competitor research. Paste a channel — or specific video URLs — and get a structured script teardown for every video: hook, pacing, CTAs, tone, title alignment, and attention-rated winning moments, all in one sortable JSON row per video.

Point it at a channel URL and it automatically finds and analyzes that channel's top-viewed videos — no hunting for individual links. Or paste up to 50 specific URLs to compare a curated set head-to-head.

Under the hood it fetches public captions and metadata from YouTube, segments each script into timestamped blocks, classifies every block with a fixed schema, and converts a public attention signal into coarse winner / neutral / weak labels. For batches of 3+ videos it also emits a cross-video pattern summary at no extra charge.

Run it from Apify Console, schedule it, or call it via API. Download results as JSON, CSV, or Excel.

Why use YouTube Script AI Analyzer?

• Tear down a whole channel in one run. Paste a channel URL; the Actor ranks its top-viewed recent videos and analyzes each one. Perfect for sizing up a competitor fast.
• Compare competitor scripts without reading every transcript. Each run returns timestamped blocks, hook type, CTA placements, SEO keywords, and pacing stats for every video.
• Evidence-backed labels. Attention-rated winner and weak moments come from a public signal, not guesswork.
• Stable JSON output. Fixed schema across every run — easy to sort, filter, and pipe into your own tools or automations.
• Batch pattern detection. Analyze 3–50 videos and get a cross-video summary: repeated winning tactics, weak moves, common scene sequences, and pacing medians.
• Pay only for results. Failed, private, or caption-less videos are not charged.

Who is this for?

Persona	Job to be done
Content agencies	Compare competitor scripts for a client niche without reading every transcript
Faceless-channel operators	Find repeatable script moves from proven videos backed by structured evidence
Creator-tool builders	Feed stable script intelligence (JSON) into a workflow or product

Raw transcript buyers are out of scope — there are 100+ cheaper transcript Actors for that.

How to use YouTube Script AI Analyzer

1. Open the Actor on Apify Console.
2. Either paste public YouTube video URLs into the Video URLs field, or paste a Channel URL (e.g. https://www.youtube.com/@HalfasInteresting) and set Top N to analyze that channel's top-viewed recent uploads. You can do both — up to 50 videos total.
3. Leave Include Batch Pattern Summary checked if you want cross-video patterns (free, requires 3+ usable videos).
4. Click Start and wait for the run to complete (typically 30–90 seconds per video; channel runs also spend a little time ranking).
5. Open the Dataset tab to browse, sort, or download results.

Input

Analyze specific videos:

{
    "video_urls": [
        "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
        "https://www.youtube.com/watch?v=9bZkp7q19f0",
        "https://www.youtube.com/watch?v=JGwWNGJdvx8"
    ],
    "include_batch_summary": true,
    "language": "en"
}

Or analyze a channel's top-viewed recent videos:

{
    "channel_url": "https://www.youtube.com/@HalfasInteresting",
    "top_n": 20,
    "include_batch_summary": true,
    "language": "en"
}

Field	Type	Default	Notes
video_urls	string[]	optional	Public YouTube URLs. Provide these, channel_url, or both — up to 50 videos total
channel_url	string	optional	A channel URL. Expands to its top-top_n most-viewed recent uploads (not all-time)
top_n	integer	20	1–50. How many top-viewed recent videos to pull from channel_url. Each is charged
include_batch_summary	boolean	true	Emits a batch summary row when 3+ videos succeed. Included, not charged.
language	string	"en"	V1: English captions only

Provide at least one of video_urls or channel_url. A channel run analyzing 20 videos costs about $3.00 (20 × $0.15); the ranking step is not charged.

Output

You can download the dataset in JSON, CSV, HTML, or Excel from the Dataset tab.

video_teardown row (attention rating applied)

{
    "record_type": "video_teardown",
    "video_id": "9bZkp7q19f0",
    "video_url": "https://www.youtube.com/watch?v=9bZkp7q19f0",
    "title": "PSY - GANGNAM STYLE(강남스타일) M/V",
    "duration_sec": 253,
    "attention_rating_status": "applied",
    "analysis_notes": "Strong hook with visual contrast. CTA cluster at 70% mark.",
    "hook_type": "visual_contrast",
    "hook_strength": 0.82,
    "cta_placements": [176.0, 231.0],
    "title_alignment": "Title matches hook framing and first 10 seconds.",
    "seo_keywords": ["gangnam style", "kpop", "viral music video"],
    "block_count": 14,
    "pattern_interrupts_total": 9,
    "interrupts_per_minute": 2.13,
    "blocks": [
        {
            "id": 0,
            "t0": 0.0,
            "t1": 12.4,
            "text": "Oppa Gangnam style...",
            "scene_type": "hook",
            "pulls": ["pattern_interrupt", "high_energy_open"],
            "tone_markers": ["playful", "high_energy"],
            "pattern_interrupts": 1,
            "attention_rating": "winner"
        }
    ]
}

video_teardown row (attention rating unavailable)

{
    "record_type": "video_teardown",
    "video_id": "dQw4w9WgXcQ",
    "video_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "title": "Rick Astley - Never Gonna Give You Up",
    "duration_sec": 212,
    "attention_rating_status": "unavailable",
    "analysis_notes": "Attention rating unavailable — this video's public engagement signal isn't established yet (this can happen with newer or lower-traffic videos). The full script teardown above is complete; only the per-block winner/weak attention labels are affected.",
    "hook_type": "music_intro",
    "hook_strength": null,
    "cta_placements": [],
    "title_alignment": "Title matches song content.",
    "seo_keywords": ["rick astley", "never gonna give you up", "80s pop"],
    "block_count": 11,
    "pattern_interrupts_total": 4,
    "interrupts_per_minute": 1.13,
    "blocks": [
        {
            "id": 0,
            "t0": 0.0,
            "t1": 18.0,
            "text": "We're no strangers to love...",
            "scene_type": "hook",
            "pulls": ["familiar_reference"],
            "tone_markers": ["sincere", "nostalgic"],
            "pattern_interrupts": 0,
            "attention_rating": "unavailable"
        }
    ]
}

batch_pattern_summary row

{
    "record_type": "batch_pattern_summary",
    "source_video_ids": ["9bZkp7q19f0", "dQw4w9WgXcQ", "JGwWNGJdvx8"],
    "repeated_winning_pulls": ["pattern_interrupt", "open_loop"],
    "repeated_weak_moves": ["delayed_hook", "generic_cta"],
    "common_scene_sequences": ["hook -> frame -> story", "exposition -> list_item -> cta"],
    "pacing_summary": {
        "median_block_count": 12.0,
        "median_interrupts_per_minute": 1.8
    },
    "attention_rating_coverage": {
        "applied": 2,
        "unavailable": 1
    }
}

Field Reference

video_teardown fields

Field	Type	Description
record_type	string	Always "video_teardown"
video_id	string	YouTube video ID
video_url	string	Original input URL
title	string	Video title
duration_sec	integer	Video duration in seconds
attention_rating_status	string	"applied" or "unavailable"
analysis_notes	string	Brief plain-English summary of key observations
hook_type	string	Identified hook strategy (e.g. "open_loop", "visual_contrast")
hook_strength	float or null	Rating-backed hook score 0–1; null when rating is unavailable
cta_placements	float[]	Timestamps (seconds) where CTAs appear
title_alignment	string	How well the title aligns with the hook and first block
seo_keywords	string[]	Keywords identified in the script
block_count	integer	Total number of script blocks
pattern_interrupts_total	integer	Total rhetorical/structural shifts across all blocks
interrupts_per_minute	float	Pattern interrupts per minute (comparable across videos)
blocks	object[]	Timestamped script blocks (see below)

blocks[] item fields

Field	Type	Description
id	integer	Block index (0-based)
t0	float	Start time in seconds
t1	float	End time in seconds
text	string	Block transcript text
scene_type	string	hook, frame, story, exposition, evidence, list_item, turn, payoff, cta, or outro
pulls	string[]	Attention tactics used (e.g. "open_loop", "social_proof")
tone_markers	string[]	Short tone labels (e.g. "urgent", "conversational")
pattern_interrupts	integer	Structural or rhetorical shifts within this block
attention_rating	string	"winner", "neutral", "weak", or "unavailable"

batch_pattern_summary fields

Field	Type	Description
record_type	string	Always "batch_pattern_summary"
source_video_ids	string[]	IDs of videos included in this summary
repeated_winning_pulls	string[]	Tactics seen in winner-rated blocks across multiple videos
repeated_weak_moves	string[]	Patterns seen in weak-rated blocks across multiple videos
common_scene_sequences	string[]	Repeated block-order patterns
pacing_summary.median_block_count	float	Median block count across analyzed videos
pacing_summary.median_interrupts_per_minute	float	Median interrupts per minute
attention_rating_coverage.applied	integer	Videos where the attention rating was successfully applied
attention_rating_coverage.unavailable	integer	Videos where the attention rating was unavailable

Pricing

$0.15 per successfully analyzed video (launch test price). The video_analyzed event is charged once per video_teardown row emitted.

Item	Cost
Actor start	$0.00005 (platform fee)
Each analyzed video	$0.15
Batch pattern summary	Included — $0 extra
Failed, private, or caption-less videos	$0 — not charged

Example: Analyze 10 competitor videos → $0.00005 + (10 × $0.15) = $1.50005

Pricing is subject to change after the cloud cost spike confirms real per-video LLM and compute costs. Subscribe to Actor updates for changes.

How to run via API

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run_input = {
    "video_urls": [
        "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
        "https://www.youtube.com/watch?v=9bZkp7q19f0",
    ],
    "include_batch_summary": True,
    "language": "en",
}

run = client.actor("dominvo/youtube-script-ai-analyzer").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

Tips

• Start with 3–5 videos to verify the output structure before running a full batch.
• Use include_batch_summary: true (the default) any time you submit 3+ URLs — the cross-video summary is the most actionable output for competitor research.
• Filter by record_type in your downstream tool to separate teardown rows from the summary row.
• Sort by interrupts_per_minute to quickly find the fastest-paced videos in a batch.
• Videos without English captions are skipped and not charged.

FAQ

Is this affiliated with YouTube or Google? No. This Actor reads publicly available captions and metadata — the same data accessible in any browser. It is not affiliated with, endorsed by, or a product of YouTube or Google.

English captions only? Yes, V1 supports English captions only. Other languages may be added after demand is confirmed.

What is the "attention rating"? The Actor converts a publicly available per-segment signal into coarse block-level labels: winner, neutral, or weak. This is not YouTube's private creator audience-retention curve and does not represent any internal YouTube analytics data. When the signal is unavailable for a video, all blocks get attention_rating: "unavailable" and attention_rating_status: "unavailable".

What counts as a "failed" video? Private videos, deleted videos, age-gated videos, and videos without English captions are all skipped and not charged. You'll see a log message for each skip.

Can I export to CSV or Excel? Yes. Use the Dataset tab's export options or the API with ?format=csv or ?format=xlsx.

Need help or a custom solution? Open an issue on the Issues tab or contact the actor author.