video-audio-processor

A tool for turning video or audio recordings, such as meeting or Zoom files, into written transcripts and selected video frames. It uses Whisper for speech transcription and ffmpeg for extracting images.

In plain words
What is it for?
Use it to analyse meetings, interviews, presentations, or other recordings. It produces text for spoken content and image captures for reviewing what appeared on screen.
Why use it?
It avoids manually listening through recordings or scanning every moment of a video before analysis. It chooses processing settings based on the computer's operating system and available hardware.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/sacredvoid/skillkit/video-audio-processor
Any agent
npx skills add sacredvoid/skillkit --skill video-audio-processor
Clone the repo
git clone --depth 1 https://github.com/sacredvoid/skillkit

Made for: Claude Code, Codex.

Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,120 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
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 $0.00064 $0.03120
Opus 5 $0.00032 $0.01560
Sonnet 5 $0.00013 $0.00624
Haiku 4.5 $0.00006 $0.00312

Measured yesterday against content hash e367e328985e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

video-audio-processor scanned grade B with 1 finding 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 yesterday.

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.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

| Ubuntu/Debian | `sudo apt update && sudo apt install -y ffmpeg` |
skills/video-audio-processor/SKILL.md · 318 lines

How it starts

The opening of the file, as written. The whole thing — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Video/Audio Processor

Process video and audio recordings into text transcripts and visual frame captures for analysis. Auto-detects your hardware and picks the optimal transcription model, device, and settings.

Phase 0: Detect Compute Environment

Run this at the start of every invocation. It determines everything downstream.

python3 -c "
import platform, shutil, subprocess, json

info = {'os': platform.system(), 'arch': platform.machine(), 'ram_gb': 0, 'gpu': 'none', 'vram_gb': 0, 'device': 'cpu', 'dtype': 'float32'}

# RAM
try:
    if platform.system() == 'Darwin':
        import os; info['ram_gb'] = round(os.sysconf('SC_PAGE_SIZE') * os.sysconf('SC_PHYS_PAGES') / (1024**3))
    elif platform.system() == 'Linux':
        with open('/proc/meminfo') as f:
            for line in f:
                if line.startswith('MemTotal'):
                    info['ram_gb'] = round(int(line.split()[1]) / (1024**2))
                    break
    else:
        import ctypes
        mem = ctypes.c_ulonglong(0)
        ctypes.windll.kernel32.GetPhysicallyInstalledMemory(ctypes.byref(mem))
        info['ram_gb'] = round(mem.value / (1024**2))
except: pass

# GPU detection
try:
    import torch
    if torch.cuda.is_available():
        info['gpu'] = torch.cuda.get_device_name(0)
        info['vram_gb'] = round(torch.cuda.get_device_properties(0).total_mem / (1024**3))
        info['device'] = 'cuda'
        info['dtype'] = 'float16'
    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
        info['gpu'] = 'Apple Silicon (MPS)'
        info['vram_gb'] = info['ram_gb']  # unified memory
        info['device'] = 'mps'
        info['dtype'] = 'float16'
    elif hasattr(torch, 'hip') or 'AMD' in str(getattr(torch, '_C', '')):
        info['gpu'] = 'AMD (ROCm)'
        info['device'] = 'cuda'  # ROCm uses cuda API
        info['dtype'] = 'float16'
except ImportError:
    pass

# ffmpeg detection
info['ffmpeg'] = shutil.which('ffmpeg') is not None
info['ffprobe'] = shutil.which('ffprobe') is not None

print(json.dumps(info))
"

Read the full file on GitHub · 318 lines

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. yesterday First seen · 318 lines · 64 tokens per session scan B e367e328985e

Subscribe to this mod's changes

video-audio-processor is a skill published in the GitHub repository sacredvoid/skillkit (10 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 3,120 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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