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 skills add jianshuo/claude-skills --skill wjs-reframing-videogit clone --depth 1 https://github.com/jianshuo/claude-skillsWrote 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/jianshuo/claude-skills/wjs-reframing-video)<a href="https://agentmods.dev/skills/jianshuo/claude-skills/wjs-reframing-video"><img src="https://agentmods.dev/badge/skills/jianshuo/claude-skills/wjs-reframing-video/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.
<a href="https://agentmods.dev/skills/jianshuo/claude-skills/wjs-reframing-video"><img src="https://agentmods.dev/badge/skills/jianshuo/claude-skills/wjs-reframing-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00177 | $0.03310 |
| Opus 5 | $0.00088 | $0.01655 |
| Sonnet 5 | $0.00035 | $0.00662 |
| Haiku 4.5 | $0.00018 | $0.00331 |
Grade A, and why
wjs-reframing-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 13d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wjs-reframing-video
Convert a video's orientation by cropping a narrow band from the source — not by physically rotating it. The crop window follows the active speaker (the face whose mouth is moving), not just the largest or most-confident face. A .crop.json sidecar records the crop plan, the per-segment speaker decisions, and the parameters used. The original input is never modified.
When to use
- Repurposing a 16:9 podcast / interview / talk for vertical short-video platforms (WeChat Channels 视频号, Douyin 抖音, Xiaohongshu 小红书, YouTube Shorts, TikTok, Reels).
- Repurposing a 9:16 phone recording for horizontal players (YouTube long-form, blog embeds).
- Repurposing 4:3 archive footage for 3:4 mobile, or vice versa.
The output aspect is the source aspect with width and height swapped — 16:9 → 9:16, not "letterboxed 16:9 in a 9:16 frame".
When NOT to use
- Multi-person Q&A where each face needs its own crop — this skill picks one crop track per video. For per-speaker split renders, use wjs-editing-multicam instead.
- Animated content / B-roll with no faces — falls back to center crop, usually wrong for the intent.
- Heavy camera motion in the source (handheld pan/zoom) — the face tracker amplifies camera shake. Stabilize first.
- Source already at target aspect — no work to do.
What this skill IS — and IS NOT
| Is | Is not |
|---|---|
| Visual active-speaker detection via MAR (mouth-aspect-ratio) variance | Audio-visual fusion (audio energy + lip motion cross-correlated) |
| Stable face tracking across frames by center-distance matching | Re-identification across long gaps / occlusions |
| Speaker-aligned segments with hysteresis to prevent flicker | Frame-by-frame switching on every flicker |
--face-pick speaker (default) — pick whoever's mouth is moving |
--face-pick largest (opt-in legacy) — pick largest face |
Hard cuts between segments, fixed crop within each segment (--motion cut, default) |
Smooth panning that drifts during a speaker's turn (opt-in --motion smooth) |
| Audio stream-copy (bit-exact) | Audio reprocessing / re-encoding |
MediaPipe Tasks FaceLandmarker (478-pt mesh) at 5 fps sampled via ffmpeg |
Per-frame neural inpainting / out-painting |
One ffmpeg crop + scale pass |
Frame-by-frame Python compositor |
What ships with it
3 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.
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.
- 13d ago First seen · 172 lines · 177 tokens per session scan A b1acc670b389
wjs-reframing-video is a skill published in the GitHub repository jianshuo/claude-skills (129 stars, last pushed 23d ago), licensed MIT. It adds 177 tokens to every session and 3,310 once invoked, about $0.0009 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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