What specific file formats and data points do I need to provide?

The skill requires a CSV file containing columns for views, dates, and titles. It is optimized for exports from popular tools like vidIQ, TubeBuddy, and Social Blade, but it can process any manual spreadsheet that meets the basic formatting requirements.

How does this perform better than just pasting my data into a standard AI prompt?

Unlike basic prompts, this skill uses a specialized Python-based logic to perform median-based outlier detection. This allows it to mathematically isolate 'viral' hits from standard performance, which is difficult for standard LLMs to do accurately with large datasets.

What is included in the purchase of this skill?

You will receive the complete competitor content audit script, documentation on required CSV headers, and a guide on how to interpret the generated Markdown reports. It is designed for immediate deployment within your AI agent environment.

Can this skill help me find specific video ideas that my competitors are missing?

The skill identifies 'Strategic Topic Gaps' by comparing high-performing competitor themes against your own provided content history, highlighting viral topics that your channel has not yet addressed.

Does this skill automatically pull data from YouTube or TikTok in real-time?

No, this skill does not have a live web-scraping component. It is a data-processing engine that requires you to upload an export or a spreadsheet of competitor data for analysis.

Competitor Content Audit

Example session with this skill installed

Run a competitor audit on 'competitors_raw.csv' with my channel 'DevLogs' as the benchmark. Use a 3x outlier threshold and identify title patterns in the top 10% of posts.

Read your context and instructions
Compiled the competitor content audit

I have processed the audit for 4 competitors across 142 total posts. The analysis identified 12 high-performing outliers and significant title patterns related to "how-to" formats.

Competitor Audit: DevLogs vs. Niche Peers

Metric	DevLogs (Me)	Competitor Avg	Outlier Threshold (3x)
Median Views	1,200	2,850	8,550
Avg. Duration	12:40	08:15	N/A
Engagement Rate	4.2%	3.8%	9.5%
Best Posting Day	Tuesday	Thursday	Sunday

Performance Analysis

Outlier Content: The top 3 videos in this niche all used "Brackets [ ]" in the title and focused on "VS" comparisons. These videos achieved 5.2x the median view count.

Duration Sweet Spot: Videos between 07:00 and 09:30 minutes show a 22% higher comment-to-view ratio than longer deep dives.

Topic Gaps: Competitors are missing "Deployment workflows" and "Rust migrations," which are frequent keywords in the comments of their top-performing videos but absent from their recent titles.

Title Pattern Correlation

Question Titles: 1.5x higher engagement than declarative statements.
Lists (Top 5...): Correlates with higher click-through but lower average watch time.
Emotional Triggers: Keywords like "Mistake," "Stop," and "Fixed" appear in 40% of the identified outliers.

Next steps

Test a "VS" comparison video in the 8-minute range to hit the duration sweet spot.
Draft three titles for the "Rust migrations" gap using brackets and a "mistake" hook.
Shift next week's upload to Sunday to test the high-engagement outlier window.

Connects securely to your tools. The creator never sees your data.

About this skill

Manual competitor research is slow and prone to bias. This skill audits exported creator data to identify exactly why certain posts outperform the rest and where the content gaps lie.

What it does

Performance Benchmarking calculates average and median views, likes, and comments across all analyzed creators.
Outlier Detection flags "hit" content that performs at 3x or more above a creator's median baseline.
Pattern Recognition analyzes title structures, including length, emotional triggers, brackets, and question formats.
Strategic Analysis identifies optimal video durations, posting cadences, and the best days to publish for engagement.
Gap Identification finds high-performing topics used by competitors that you are currently missing.

How it works

Data Import accepts a CSV export from tools like vidIQ, TubeBuddy, or platform-native exports.
Configuration allows setting custom outlier thresholds and minimum post counts for statistical significance.
Execution processes the creator, date, title, format, and engagement metrics via the audit script.
Synthesis generates a Markdown report detailing the competitive landscape and specific title correlations.

Frameworks & tools

The skill utilizes a Python-based analysis script designed to process CSV data from Social Blade, vidIQ, TubeBuddy, or manual spreadsheets.

Why this beats prompting it yourself

Generic prompts cannot handle large CSV datasets or perform precise median-based outlier calculations. This tool applies consistent mathematical filters to separate genuine viral hits from standard channel growth.

Use cases

Niche Entry to understand the content standards and formats of a new market.
Viral Reverse-Engineering to isolate the title patterns and durations of outlier videos.
Content Planning to identify topic gaps your competitors haven't covered yet.
Performance Auditing to benchmark your own channel against a specific peer group.

Known limitations

Requires a CSV file with specific columns like views and dates. It does not scrape live data directly from platforms; it requires an export or manual collection first.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

Reviews

No reviews yet

Be one of the first to try it. Every listed skill passes our trust checks below.

Security scanned

Passed our 8-point scan before listing

Fresh listing

Recently published to Agensi

30-day refund

Not a fit? Get your money back

Competitor Content Audit