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/s3yed/appie-kit/video-usenpx skills add S3YED/appie-kit --skill video-usegit clone --depth 1 https://github.com/S3YED/appie-kitWrote 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/s3yed/appie-kit/video-use)<a href="https://agentmods.dev/skills/s3yed/appie-kit/video-use"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/video-use.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 | $0.00070 | $0.05693 |
| Opus 5 | $0.00035 | $0.02847 |
| Sonnet 5 | $0.00014 | $0.01139 |
| Haiku 4.5 | $0.00007 | $0.00569 |
Grade A, and why
video-use 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 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.
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.
This is a copy
100% identical to video-use — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Use
Principle
- LLM reasons from raw transcript + on-demand visuals. The only derived artifact that earns its keep is a packed phrase-level transcript (
takes_packed.md). Everything else — filler tagging, retake detection, shot classification, emphasis scoring — you derive at decision time. - Audio is primary, visuals follow. Cut candidates come from speech boundaries and silence gaps. Drill into visuals only at decision points.
- Ask → confirm → execute → iterate → persist. Never touch the cut until the user has confirmed the strategy in plain English.
- Generalize. Do not assume what kind of video this is. Look at the material, ask the user, then edit.
- Artistic freedom is the default. Every specific value, preset, font, color, duration, pitch structure, and technique in this document is a worked example from one proven video — not a mandate. Read them to understand what's possible and why each worked. Then make your own taste calls based on what the material actually is and what the user actually wants. The only things you MUST do are in the Hard Rules section below. Everything else is yours.
- Invent freely. If the material calls for a technique not described here — split-screen, picture-in-picture, lower-third identity cards, reaction cuts, speed ramps, freeze frames, crossfades, match cuts, L-cuts, J-cuts, speed ramps over breath, whatever — build it. The helpers are ffmpeg and PIL. They can do anything the format supports. Do not wait for permission.
- Verify your own output before showing it to the user. If you wouldn't ship it, don't present it.
Hard Rules (production correctness — non-negotiable)
These are the things where deviation produces silent failures or broken output. They are not taste, they are correctness. Memorize them.
- Subtitles are applied LAST in the filter chain, after every overlay. Otherwise overlays hide captions. Silent failure.
- Per-segment extract → lossless
-c copyconcat, not single-pass filtergraph. Otherwise you double-encode every segment when overlays are added. - 30ms audio fades at every segment boundary (
afade=t=in:st=0:d=0.03,afade=t=out:st={dur-0.03}:d=0.03). Otherwise audible pops at every cut. - Overlays use
setpts=PTS-STARTPTS+T/TBto shift the overlay's frame 0 to its window start. Otherwise you see the middle of the animation during the overlay window. - Master SRT uses output-timeline offsets:
output_time = word.start - segment_start + segment_offset. Otherwise captions misalign after segment concat. - Never cut inside a word. Snap every cut edge to a word boundary from the Scribe transcript.
- Pad every cut edge. Working window: 30–200ms. Scribe timestamps drift 50–100ms — padding absorbs the drift. Tighter for fast-paced, looser for cinematic.
- Word-level verbatim ASR only. Never SRT/phrase mode (loses sub-second gap data). Never normalized fillers (loses editorial signal).
- Cache transcripts per source. Never re-transcribe unless the source file itself changed.
- Parallel sub-agents for multiple animations. Never sequential. Spawn N at once via the
Agenttool; total wall time ≈ slowest one. - Strategy confirmation before execution. Never touch the cut until the user has approved the plain-English plan.
- All session outputs in
<videos_dir>/edit/. Never write inside thevideo-use/project directory.
What ships with it
33 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.
- .env.example 20 B
- .gitignore 790 B
- helpers/grade.py 13 KB runs code
- helpers/pack_transcripts.py 7.0 KB runs code
- helpers/render.py 23 KB runs code
- helpers/timeline_view.py 13 KB runs code
- helpers/transcribe_batch.py 3.6 KB runs code
- helpers/transcribe.py 5.0 KB runs code
- install.md 7.6 KB
- LICENSE 1.0 KB
- poster.html 20 KB
- pyproject.toml 450 B
- README.md 5.4 KB
- skills/manim-video/README.md 886 B
- skills/manim-video/references/animation-design-thinking.md 7.0 KB
- skills/manim-video/references/animations.md 8.6 KB
- skills/manim-video/references/camera-and-3d.md 4.0 KB
- skills/manim-video/references/decorations.md 4.9 KB
- skills/manim-video/references/equations.md 5.9 KB
- skills/manim-video/references/graphs-and-data.md 4.5 KB
- skills/manim-video/references/mobjects.md 9.5 KB
- skills/manim-video/references/paper-explainer.md 9.1 KB
- skills/manim-video/references/production-quality.md 5.8 KB
- skills/manim-video/references/rendering.md 5.2 KB
- skills/manim-video/references/scene-planning.md 2.7 KB
- skills/manim-video/references/troubleshooting.md 4.2 KB
- skills/manim-video/references/updaters-and-trackers.md 8.3 KB
- skills/manim-video/references/visual-design.md 4.7 KB
- skills/manim-video/scripts/setup.sh 921 B runs code
- skills/manim-video/SKILL.md 12 KB
- static/timeline-view.svg 12 KB
- static/video-use-banner.png 500 KB
- uv.lock 531 KB
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.
- yesterday First seen · 323 lines · 70 tokens per session scan A 37b24f7ec554
video-use is a skill published in the GitHub repository S3YED/appie-kit (7 stars, last pushed 9d ago), licensed MIT. It adds 70 tokens to every session and 5,693 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to video-use, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
internal-linking-optimizer
Use when improving internal link structure, anchor text, orphan pages, crawl depth, site architecture, or link equity flow. 内链优化/站内架构.
seo-geo-optimizer
Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and…
geo-review-html
Render an interactive, self-contained HTML companion for a GEO content brief (04-content-brief) or a publish-ready draft (05-production), so a NON-technical client reviewer (founder, organizer staff, the domain expert filling slots) can fill REQUIRED-FILL slots, leave section-level comments, and approve/return work in…
geo-pipeline
Entry point + orchestrator for the recomby-geo GEO (Generative Engine Optimization) workflow on OpenAI Codex CLI. Use when the user wants to run any stage of the GEO pipeline on a client folder — intake, visibility audit, content-gap analysis, content brief, draft production, distribution, or monthly re-audit — or…
follow-up-writer
Use when drafting a post-event follow-up message to someone met at a conference.
schedule
Create, list, remove or inspect this bot's scheduled work — recurring jobs ("every morning at 7 tell me X", "every weekday at 9 post the standup") and one-shot follow-ups ("check back in 20 minutes", "watch that deploy until it lands", "remind me on Friday"). Use whenever something must happen on a schedule…