Skill
accuracy-improvement-l…
Use when an existing model's results are disappointing and the user wants higher accuracy - 'accuracy is still too low', 'improve/boost the model', 'why is it…
Development
Skill
/domain-video
Use for video understanding: action or activity recognition, video classification, temporal action detection or localization in untrimmed video, gesture recognition, and general video analysis. Picks the right video model and the correct video-level split and metric. Triggers on
Install
$ npx -y skills add mxslr/mlcraft --skill domain-video --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/domain-video
Context preview
The summary Claude sees to decide when to auto-load this skill.
Use for video understanding: action or activity recognition, video classification, temporal action detection or localization in untrimmed video, gesture recognition, and general video analysis. Picks the right video model and the correct video-level split and metric. Triggers on
SKILL.md
domain-video.SKILL.mdname: domain-video
description: "Use for video understanding: action or activity recognition, video classification, temporal action detection or localization in untrimmed video, gesture recognition, and general video analysis. Picks the right video model and the correct video-level split and metric. Triggers on 'video classification', 'action recognition', 'activity recognition', 'temporal action detection', 'video understanding', 'recognize actions', 'gesture', 'video dataset'."
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`.
Read more
name: domain-video description: "Use for video understanding: action or activity recognition, video classification, temporal action detection or localization in untrimmed video, gesture recognition, and general video analysis. Picks the right video model and the correct video-level split and metric. Triggers on 'video classification', 'action recognition', 'activity recognition', 'temporal action detection', 'video understanding', 'recognize actions', 'gesture', 'video dataset'."
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`.
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