video-content-analyzer

video-content-analyzer is a skill for Claude Code from bradautomates/head-of-content. It costs 113 tokens per session (1,040 once invoked), scanned A, original, MIT.

A workflow for researching high-performing Instagram posts and short videos from a selected group of accounts. It collects recent content, finds unusually successful posts, and uses AI to study the best videos.

In plain words
What is it for?
Use it to research Instagram trends, identify strong posts or Reels, and produce reports about hooks, video structure, and reusable content patterns.
Why use it?
It replaces manual browsing and comparison with a repeatable process for spotting which content performs better than an account's usual results. It also requires setup such as API keys and configured accounts.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths.

Good fit Use it to research Instagram trends, identify strong posts or Reels, and produce reports about hooks, video structure, and reusable content patterns.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bradautomates/head-of-content/video-content-analyzer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add bradautomates/head-of-content --skill video-content-analyzer
Clone the repo
git clone --depth 1 https://github.com/bradautomates/head-of-content

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for video-content-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/bradautomates/head-of-content/video-content-analyzer/github.svg)](https://agentmods.dev/skills/bradautomates/head-of-content/video-content-analyzer)
Your own site
<a href="https://agentmods.dev/skills/bradautomates/head-of-content/video-content-analyzer"><img src="https://agentmods.dev/badge/skills/bradautomates/head-of-content/video-content-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for video-content-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/bradautomates/head-of-content/video-content-analyzer"><img src="https://agentmods.dev/badge/skills/bradautomates/head-of-content/video-content-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,040 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 Mar 2026
  • Snyk warn 15 Feb 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00113 $0.01040
Opus 5 $0.00056 $0.00520
Sonnet 5 $0.00023 $0.00208
Haiku 4.5 $0.00011 $0.00104

Measured 12d ago against content hash 0271821f291b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

video-content-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/analyze_videos.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/video-content-analyzer/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video Content Analyzer

Analyze short-form videos with Gemini AI to extract hooks, content structure, delivery style, and CTA strategies.

Prerequisites

  • GEMINI_API_KEY environment variable
  • google-genai and requests Python packages

Usage

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \
  --input outliers.json \
  --output video-analysis.json \
  --platform instagram \
  --max-videos 5

Parameters

Arg Description
--input, -i Input JSON file with outlier posts (required)
--output, -o Output JSON file for results (required)
--platform, -p Platform: instagram, tiktok, or youtube (default: instagram)
--max-videos, -n Max videos to analyze (default: 5)

Input Format

Accepts outlier JSON from platform-specific research skills. Handles both formats:

  • Direct list: [{post1}, {post2}, ...]
  • Wrapped: {"outliers": [{post1}, {post2}, ...]}

The script automatically maps platform-specific fields:

Platform Video URL Fields Caption Username
Instagram videoUrl caption ownerUsername
TikTok videoUrl, video_url, webVideoUrl text, desc authorUsername
YouTube videoUrl, url title channelTitle

TikTok Note: The Apify TikTok Scraper returns webVideoUrl (the TikTok page URL) rather than a direct video download URL. Gemini will attempt to analyze from this page URL.

Output

Returns JSON array with analysis for each video:

[
  {
    "post_id": "ABC123",
    "username": "creator",
    "url": "https://...",
    "platform": "instagram",
    "likes": 50000,
    "comments": 1200,
    "views": 500000,
    "analysis": {
      "hook": {
        "technique": "pattern-interrupt",
        "opening_line": "Stop scrolling if you...",
        "attention_grab": "Creates urgency and targets specific audience",
        "replicable_formula": "Stop scrolling if you [pain point]"
      },
      "content_structure": {
        "format": "problem-solution",
        "sections": [...],
        "pacing": "fast",
        "retention_techniques": ["pattern interrupts", "text overlays"]
      },
      "delivery_style": {
        "speaking": "direct-to-camera",
        "energy": "high-energy",
        "text_overlays": true,
        "visual_style": "quick cuts with b-roll"
      },
      "cta_strategy": {
        "type": "follow",
        "cta_text": "Follow for more tips",
        "placement": "end"
      },
      "why_it_works": "..."
    }
  }
]

Read the full file on GitHub · 130 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 130 lines · 113 tokens per session scan A 0271821f291b

Subscribe to this mod's changes

video-content-analyzer is a skill published in the GitHub repository bradautomates/head-of-content (232 stars, last pushed 7mo ago), licensed MIT. It adds 113 tokens to every session and 1,040 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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