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 ECNU-ICALK/AutoSkill --skill ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-localgit clone --depth 1 https://github.com/ECNU-ICALK/AutoSkillWrote 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/ecnu-icalk/autoskill/ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-local)<a href="https://agentmods.dev/skills/ecnu-icalk/autoskill/ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-local"><img src="https://agentmods.dev/badge/skills/ecnu-icalk/autoskill/ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-local/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/ecnu-icalk/autoskill/ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-local"><img src="https://agentmods.dev/badge/skills/ecnu-icalk/autoskill/ferramenta-python-de-divisao-de-audio-com-gui-e-ffmpeg-local.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 | $0.00051 | $0.00668 |
| Opus 5 | $0.00026 | $0.00334 |
| Sonnet 5 | $0.00010 | $0.00134 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
Ferramenta Python de Divisão de Áudio com GUI e FFmpeg Local 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 6d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 6d ago First seen · 52 lines · 51 tokens per session scan A 3fe601bb9cd5
Ferramenta Python de Divisão de Áudio com GUI e FFmpeg Local is a skill published in the GitHub repository ECNU-ICALK/AutoSkill (577 stars, last pushed 4mo ago), with no licence file. It adds 51 tokens to every session and 668 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
python-pillow-graphics
Create technical graphics, diagrams, and posters using Python Pillow library with precise color control and typography.
image-generation-pillow
Technical image generation using Python's Pillow library. Use this when you need to programmatically create diagrams, posters, or technical drawings.
pillow-grayscale
Convert images to grayscale in-place using Pillow (PIL), overwriting original RGB files.
python-data-analysis
Best practices for multi-step Python tasks including data analysis, HuggingFace datasets, token counting, and any task requiring state across multiple python() calls.
python-packages
Installing and using common Python packages in SkillBench containers. Covers scientific computing, data analysis, and file format libraries.
manimgl-best-practices
Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains from manimlib import , (3) User runs manimgl CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown…