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.
npx agentmods add skills/siviter-xyz/dot-agent/media-processingnpx skills add siviter-xyz/dot-agent --skill media-processinggit clone --depth 1 https://github.com/siviter-xyz/dot-agentWrote 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.
[](https://agentmods.dev/skills/siviter-xyz/dot-agent/media-processing)<a href="https://agentmods.dev/skills/siviter-xyz/dot-agent/media-processing"><img src="https://agentmods.dev/badge/skills/siviter-xyz/dot-agent/media-processing.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.00985 |
| Opus 5 | $0.00017 | $0.00492 |
| Sonnet 5 | $0.00007 | $0.00197 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
media-processing 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 6d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Media Processing
Tools and workflows for working with images, audio, and video in a repeatable, scriptable way using standard CLI tools (FFmpeg, ImageMagick) and Python helpers.
When to Use
- Working with image batches (thumbnails, resizing, format conversion)
- Converting media between formats (video ↔ audio ↔ image)
- Optimizing video size while maintaining acceptable quality
- Preparing assets for web, mobile, or archival use
- Designing or refining CLI workflows around FFmpeg/ImageMagick
Key Principles
- CLI-first workflows: Prefer command-line tools (FFmpeg, ImageMagick) and scripts that can be automated in CI or local tooling.
- Deterministic scripts: Scripts should be safe to run repeatedly with predictable output paths and options.
- Non-destructive defaults: Default to writing outputs to new files/directories rather than overwriting originals.
- Cross-platform friendly: Keep examples and scripts usable on Linux, macOS, and Windows where possible.
- Agent-agnostic: Guidance should work with any coding agent (Cursor, Claude, Copilot, etc.), not one specific environment.
Capabilities
-
Image workflows
- Batch resize and thumbnail generation
- Aspect-ratio–aware resizing (fit, fill, cover, exact)
- Optional watermarking
- Format conversion (e.g., PNG → WebP, JPEG)
-
Media conversion
- Detects media type (video, audio, image) from extension
- Uses FFmpeg for video/audio, ImageMagick for images
- Quality presets for
web,archive, andmobileuse cases - Batch conversion with dry-run and verbose modes
-
Video optimization
- Resolution and frame-rate adjustments
- Single-pass (CRF) or two-pass encoding
- Audio bitrate tuning
- Basic before/after comparison (size, bitrate, resolution, FPS)
Scripts
Scripts live in scripts/ and are intended to be run directly from a shell:
batch_resize.py- Batch image resizing with multiple strategies (
fit,fill,cover,exact,thumbnail) - Optional watermark overlay
- Supports parallel processing and dry-run mode
- Batch image resizing with multiple strategies (
What ships with it
10 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.
- references/ffmpeg-encoding.md 2.0 KB
- references/ffmpeg-filters.md 1.9 KB
- references/ffmpeg-streaming.md 2.0 KB
- references/format-compatibility.md 2.1 KB
- references/imagemagick-batch.md 1.4 KB
- references/imagemagick-editing.md 1.2 KB
- scripts/batch_resize.py 10 KB runs code
- scripts/media_convert.py 8.3 KB runs code
- scripts/requirements.txt 559 B
- scripts/video_optimize.py 14 KB runs code
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.
- 6d ago First seen · 93 lines · 35 tokens per session scan A c26da9f7e3ae
media-processing is a skill published in the GitHub repository siviter-xyz/dot-agent (21 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 985 once invoked, about $0.0002 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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