domain-video

domain-video is a skill for Claude Code, Codex from mxslr/mlcraft. It costs 86 tokens per session (430 once invoked), scanned A, original, MIT.

A workflow for understanding video content, including actions, activities, gestures, and when events occur in an unedited video. It helps select models, data splits, and evaluation measures for these tasks.

In plain words
What is it for?
Use it for video classification, action detection, temporal localization, gesture recognition, and general video analysis.
Why use it?
Video tasks are sensitive to how clips are sampled and separated between training and testing. The workflow addresses those choices and helps avoid letting related frames leak across the split.

Skill for Claude CodeCodex

Part of the mlcraft plugin — 23 skills, 1 command, 1 agent shipped together

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/mxslr/mlcraft/domain-video
Any agent
npx skills add mxslr/mlcraft --skill domain-video
Clone the repo
git clone --depth 1 https://github.com/mxslr/mlcraft

Made for: Claude Code, Codex.

Or install mlcraft, the plugin that ships this one along with the rest of its 23 skills, 1 command, 1 agent.

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 domain-video

README.md
[![agentmods](https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-video.svg)](https://agentmods.dev/skills/mxslr/mlcraft/domain-video)
Your own site
<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-video"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-video.svg" alt="Measured on agentmods" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 430 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 $0.00086 $0.00430
Opus 5 $0.00043 $0.00215
Sonnet 5 $0.00017 $0.00086
Haiku 4.5 $0.00009 $0.00043

Measured 5d ago against content hash 52bbf2e76bb0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

domain-video 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 5d 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.

skills/domain-video/SKILL.md · 25 lines

What it actually says

Video Understanding - Method Selection

Prefer models pretrained on large video datasets (Kinetics). Video is compute-heavy, so use mixed precision and clip-based training.

Decision table

Task Recommended (recent, 2021-2025) Notes
Action or video classification VideoMAE or VideoMAEv2 (self-supervised pretrain, then fine-tune), Video Swin, UniFormerV2 Kinetics-pretrained. Metric is top-1 and top-5 accuracy.
Efficient or low-budget adapt a strong image backbone with temporal modules (AIM), or a small MoViNet cheaper than a full video transformer.
Temporal action detection (localize actions in long untrimmed video) ActionFormer, TadTR metric is mAP at temporal IoU.
General-purpose video features InternVideo family strong for many downstream tasks.

Cross-cutting practice

  • Sample frames (uniform or dense), train on short clips, and average multiple clips at test time.
  • Augment with temporal cropping, per-frame flip and light photometric jitter. Keep flips label-safe.
  • Leakage: split by VIDEO or subject, never by frame or by clips taken from the same video.
  • Metrics: top-1 and top-5 accuracy for trimmed classification; mAP at temporal IoU for detection. Not frame accuracy.
  • Explainability: per-frame Grad-CAM or spatiotemporal attention; show which frames the model attended to.
  • Improve accuracy: use accuracy-improvement-loop; evaluate with rigorous-evaluation.
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. 5d ago First seen · 25 lines · 86 tokens per session scan A 52bbf2e76bb0

Subscribe to this mod's changes

domain-video is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 430 once invoked, about $0.0004 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 2026-08-31.

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