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 receptron/mulmoterminal --skill mulmoterminal-video-editgit clone --depth 1 https://github.com/receptron/mulmoterminalWrote 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/receptron/mulmoterminal/mulmoterminal-video-edit)<a href="https://agentmods.dev/skills/receptron/mulmoterminal/mulmoterminal-video-edit"><img src="https://agentmods.dev/badge/skills/receptron/mulmoterminal/mulmoterminal-video-edit/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/receptron/mulmoterminal/mulmoterminal-video-edit"><img src="https://agentmods.dev/badge/skills/receptron/mulmoterminal/mulmoterminal-video-edit.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00147 | $0.03522 |
| Opus 5.5 | $0.00059 | $0.01409 |
| Sonnet 5.5 | $0.00029 | $0.00704 |
| Haiku 4.5 | $0.00015 | $0.00352 |
Grade A, and why
mulmoterminal-video-edit scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -L -o "$models/ggml-large-v3-turbo.bin" https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Edit a video
The user points at a video and says what they want in plain words. They do not write ffmpeg commands; you do, in the terminal, and show them the result. Talk to them about their video — the part from 0:12 to 0:40, the subtitles, the title at the start — not about codecs and filters, unless they ask.
What you need
-
ffmpegandffprobe. Check withffmpeg -version. If it is missing, say so and give the install command (brew install ffmpegon a Mac); do not install it yourself. -
For subtitles or captions drawn onto the picture, ffmpeg built with libass. Check:
ffmpeg -hide_banner -filters | grep -E ' (subtitles|ass) 'prints two lines when it is. -
For subtitles made from speech,
whisper-clifrom whisper.cpp (brew install whisper-cppon a Mac; the same package MulmoTerminal's voice input uses) and a model. Look for one MulmoTerminal has already downloaded, in the workspace'smodelsfolder:ls "${CLAUDE_CWD:-$HOME/mulmoclaude}/models"/ggml-*.binUse the file you find (prefer
ggml-large-v3-turbo.binwhen there are several) as$modelbelow. If there is none, ask before downloading one, and say how big it is:ggml-large-v3-turbo.bin(about 1.6 GB, the most accurate) orggml-small.bin(about 490 MB, faster, less accurate). Then put it in that folder, so voice input finds it too:models="${CLAUDE_CWD:-$HOME/mulmoclaude}/models" && mkdir -p "$models" && curl -L -o "$models/ggml-large-v3-turbo.bin" https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin(
ggml-small.binthe same way.) Then$modelis the file you downloaded. -
showInPane, to open the result beside the terminal. In a project directory it exists only when Canvas was switched on before the session started. Without it, give the user the file's path.
The speech is transcribed on this machine. Never send the video or its sound anywhere.
Rules for every edit
- Never change the original. Write a new file in the same folder, named after the original and
the edit:
talk.mp4→talk-cut.mp4,talk-subtitled.mp4. If that name is taken, add a number. When the original is outside your working directory, write the new file in your working directory instead, so it can be shown. - Keep intermediate files in a working directory of their own (
work=$(mktemp -d)), and give them plain names (audio.wav,subs.srt), which keeps the filter arguments below free of quoting. - Encode the result so it plays everywhere, the pane included: H.264 video, AAC sound,
yuv420p, and the index at the front of the file.
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.
- 3d ago First seen · 235 lines · 147 tokens per session scan A 41791c775678
mulmoterminal-video-edit is a skill published in the GitHub repository receptron/mulmoterminal (245 stars, last pushed yesterday), licensed MIT. It adds 147 tokens to every session and 3,522 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-22.
Other skills, from other repositories
moai-ref-ui-polish
UI polish and interface-completion reference: the small visual details — concentric border radius, optical alignment, shadow-vs-border, motion easing, typography smoothing, tabular numbers, icon stroke weight, hit areas — that separate polished interfaces from generic ones. Agent-extending skill that amplifies…
self-assessment
Interactive skill assessment with personalized learning path generation.
project-design
Roteador de processos de design e marca que carrega orientações para criar arquivos DESIGN.md completos para um projeto e suas aplicações de interface.
loom-swift
Swift language expertise for idiomatic, production-quality code.
loom-usage
Meta-orchestration skill for Claude driving loom itself.
loom-c
C language expertise (C11/C17/C23) for memory-safe, portable, production-quality systems code.