Borrowing it
Nothing to install: this file belongs to different-ai/agent-bank. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/different-ai/agent-bank/main/.opencode/skill/video-subtitle-cutter/SKILL.mdgit clone --depth 1 https://github.com/different-ai/agent-bankWrote 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/different-ai/agent-bank/video-subtitle-cutter)<a href="https://agentmods.dev/skills/different-ai/agent-bank/video-subtitle-cutter"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/video-subtitle-cutter/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/different-ai/agent-bank/video-subtitle-cutter"><img src="https://agentmods.dev/badge/skills/different-ai/agent-bank/video-subtitle-cutter.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 | $0.00027 | $0.04588 |
| Opus 5.5 | $0.00011 | $0.01835 |
| Sonnet 5.5 | $0.00005 | $0.00918 |
| Haiku 4.5 | $0.00003 | $0.00459 |
Grade B, and why
video-subtitle-cutter scanned grade B with 3 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 1mo 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt install ffmpeg # Linux Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://api.openai.com/v1/audio/transcriptions \ Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, capture_output=True) How it starts
The opening of the file, as written. The whole thing — 623 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I Do
Automate video editing by:
- Transcribing video to timestamped subtitles (Whisper)
- Analyzing transcript with AI to identify cuts (filler words, pauses, mistakes)
- Generating FFmpeg commands to cut and concatenate clean segments
- Generating subtitles (SRT) for the final video
CRITICAL: Always Re-encode (Never Use -c copy)
The #1 mistake is using -c copy for cutting. This causes:
- Frozen frames at cut points (1-8 seconds of freeze)
- Audio/video sync issues
- Glitchy playback
Why? H.264 video uses keyframes (I-frames) every 2-10 seconds. -c copy can only cut at keyframes, so FFmpeg includes extra frames that display as frozen.
Solution: Always re-encode segments with quality settings:
# WRONG - causes freeze frames
ffmpeg -ss 10 -i video.mp4 -t 5 -c copy segment.mp4
# CORRECT - smooth cuts at any timestamp
ffmpeg -ss 10 -i video.mp4 -t 5 \
-c:v libx264 -preset fast -crf 18 \
-c:a aac -b:a 192k \
-avoid_negative_ts make_zero \
segment.mp4
Quality presets (CRF = Constant Rate Factor):
crf 15-17= Near lossless (large files)crf 18-20= High quality (recommended)crf 21-23= Good quality (smaller files)crf 24-28= Medium quality (much smaller)
Prerequisites
# Install Whisper (choose one)
pip install openai-whisper # Local (requires Python 3.9+)
# OR use OpenAI API (no local install needed)
# Install FFmpeg
brew install ffmpeg # macOS
sudo apt install ffmpeg # Linux
Quick Start
Step 1: Transcribe Video
Option A: Local Whisper (free, slower)
whisper video.mp4 --model medium --output_format json --output_dir ./
Option B: OpenAI Whisper API (fast, paid)
curl https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-F file="@video.mp4" \
-F model="whisper-1" \
-F response_format="verbose_json" \
-F timestamp_granularities[]="segment" \
> transcript.json
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.
- 1mo ago First seen · 623 lines · 27 tokens per session scan B 5da7df0f1f73
video-subtitle-cutter is a skill published in the GitHub repository different-ai/agent-bank (255 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 4,588 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 3 findings (asks for root, makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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