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 ImGoodBai/goodable --skill good-ttvideo2textgit clone --depth 1 https://github.com/ImGoodBai/goodableWrote 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/imgoodbai/goodable/good-ttvideo2text)<a href="https://agentmods.dev/skills/imgoodbai/goodable/good-ttvideo2text"><img src="https://agentmods.dev/badge/skills/imgoodbai/goodable/good-ttvideo2text/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/imgoodbai/goodable/good-ttvideo2text"><img src="https://agentmods.dev/badge/skills/imgoodbai/goodable/good-ttvideo2text.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 | $0.00038 | $0.01291 |
| Opus 5 | $0.00019 | $0.00646 |
| Sonnet 5 | $0.00008 | $0.00258 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
good-TTvideo2text 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 9d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
good-TTvideo2text
Extract audio from Douyin/TikTok videos and transcribe to text using ASR service.
Task Objective
Convert Douyin/TikTok video audio to text with timestamps, supporting both interactive UI and command-line workflow.
Capabilities: Video parsing, audio extraction, ASR transcription, timestamp generation
Trigger: User provides Douyin/TikTok URL and requests transcription
Usage Modes
Mode 1: Web UI (Recommended)
Visual interface for transcription management:
# Install dependencies
cd skills/good-TTvideo2text
pip install -r requirements.txt
# Start service (default port 8000)
python app/main.py
# Browser access
http://localhost:8000
Features:
- Paste video URL for instant transcription
- View results with timestamps
- Cookie management for restricted videos
- Real-time progress updates
Mode 2: Script (Command Line)
Suitable for automation, AI workflow integration:
# Basic usage
python scripts/transcribe.py "https://v.douyin.com/xxx"
# Extract URL from share text
python scripts/transcribe.py "7.47 复制打开抖音,看看【用户名】的作品 https://v.douyin.com/xxx"
# JSON output
python scripts/transcribe.py "https://v.douyin.com/xxx" --output json
# Text output (default)
python scripts/transcribe.py "https://v.douyin.com/xxx" --output text
Output Format (Text):
=== Video Info ===
Title: Video title
Author: Author name
Duration: 30s
=== Transcription ===
Full Text:
Complete transcription text...
Sentences with Timestamps:
[00:00-00:03] First sentence
[00:03-00:06] Second sentence
Output Format (JSON):
{
"success": true,
"video_info": {
"title": "Video title",
"author": "Author name",
"duration": 30,
"create_time": 1234567890
},
"transcription": {
"text": "Complete transcription...",
"sentences": [
{
"start_ms": 0,
"end_ms": 3000,
"text": "First sentence"
}
]
}
}
Prerequisites
Dependencies:
- fastapi>=0.120.3, uvicorn>=0.35.0, httpx>=0.28.1
- TikTokDownloader dependencies (see requirements.txt)
What ships with it
11 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.
- 9d ago First seen · 186 lines · 38 tokens per session scan A 20972ba60b98
good-TTvideo2text is a skill published in the GitHub repository ImGoodBai/goodable (197 stars, last pushed 7mo ago), licensed MIT. It adds 38 tokens to every session and 1,291 once invoked, about $0.0002 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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