video-frame-extractor

video-frame-extractor is a skill for Claude Code, Codex from anbeime/skill. It costs 78 tokens per session (2,181 once invoked), scanned A, original, no licence file.

A video-frame extraction and analysis helper that selects key frames, describes their contents with a vision model, and creates structured prompts for further creation.

In plain words
What is it for?
Use it to study shots, extract visual references, and generate prompts from video scenes.
Why use it?
It avoids manually scanning a video frame by frame when you need visual references or scene descriptions.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it to study shots, extract visual references, and generate prompts from video scenes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/anbeime/skill/video-frame-extractor
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 anbeime/skill --skill video-frame-extractor
Clone the repo
git clone --depth 1 https://github.com/anbeime/skill

Made for: Claude Code, Codex.

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-frame-extractor

README.md
[![agentmods](https://agentmods.dev/badge/skills/anbeime/skill/video-frame-extractor/github.svg)](https://agentmods.dev/skills/anbeime/skill/video-frame-extractor)
Your own site
<a href="https://agentmods.dev/skills/anbeime/skill/video-frame-extractor"><img src="https://agentmods.dev/badge/skills/anbeime/skill/video-frame-extractor/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-frame-extractor

Your own site · 80×15
<a href="https://agentmods.dev/skills/anbeime/skill/video-frame-extractor"><img src="https://agentmods.dev/badge/skills/anbeime/skill/video-frame-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,181 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
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Data Exfiltration · line 34
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin unknown 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 $0.00078 $0.02181
Opus 5.5 $0.00031 $0.00872
Sonnet 5.5 $0.00016 $0.00436
Haiku 4.5 $0.00008 $0.00218

Measured 1mo ago against content hash 9adf985a0d38, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

video-frame-extractor 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 1mo ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/coze_bot_client.py, scripts/video_frame_extractor.py, scripts/visual_analyzer.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.

skills/video-frame-extractor/video-frame-extractor/SKILL.md · 240 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Files

What ships with it

4 files 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. 1mo ago First seen · 240 lines · 78 tokens per session scan A 9adf985a0d38

Subscribe to this mod's changes

video-frame-extractor is a skill published in the GitHub repository anbeime/skill (8,649 stars, last pushed yesterday), with no licence file. It adds 78 tokens to every session and 2,181 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.

Related

Other skills, from other repositories

daftai-gpt-image-2-5-prompt-optimizer

A prompt editor that turns natural-language image ideas and rough requests into ready-to-use prompts for GPT Image 2.5. It produces text prompts, not images, and follows that model’s official prompting guidance.

daftAI2026/daftAI-skills · 102 tokens

manus-prompt-engineering

Design and audit Manus prompts for engineering, release, production, and security work. Use when writing a Manus prompt or reviewing a Manus execution or recording against authorization and evidence.

JesseRaber/manus-prompt-engineering-skill · 41 tokens

prompt-engineer

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…

Jeffallan/claude-skills · 93 tokens

sandbase

Access 2,000+ AI models and API tools through one MCP interface for inference, media generation, search, scraping, embeddings, social data, and structured retrieval. Use sandbasediscover before building custom integrations or declaring external data inaccessible; prefer an existing dedicated tool or API key when the…

iflytek/skillhub · 67 tokens

baoyu-danger-gemini-web

Generates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation"…

JimLiu/baoyu-skills · 74 tokens

ai-workflow-architect

Designs AI systems, automations, and agent workflows for a business — identifying which manual work is worth automating, how to structure the system, which tools fit, and what could go wrong. Use this to automate part of an operation, design an agent or MCP workflow, reduce repetitive manual work, connect tools into a…

cbrock84/headcount · 89 tokens