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 lattifai/omni-captions-skills --skill omnicaptions-laicutgit clone --depth 1 https://github.com/lattifai/omni-captions-skillsWrote 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/lattifai/omni-captions-skills/omnicaptions-laicut)<a href="https://agentmods.dev/skills/lattifai/omni-captions-skills/omnicaptions-laicut"><img src="https://agentmods.dev/badge/skills/lattifai/omni-captions-skills/omnicaptions-laicut/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/lattifai/omni-captions-skills/omnicaptions-laicut"><img src="https://agentmods.dev/badge/skills/lattifai/omni-captions-skills/omnicaptions-laicut.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 | $0.00049 | $0.01559 |
| Opus 5 | $0.00024 | $0.00779 |
| Sonnet 5 | $0.00010 | $0.00312 |
| Haiku 4.5 | $0.00005 | $0.00156 |
Grade A, and why
omnicaptions-LaiCut 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 12d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LaiCut
LattifAI's audio-text processing toolkit. Currently supports forced alignment, with translate and speaker diarization coming soon.
Requires LattifAI API Key - Get from https://lattifai.com/dashboard/api-keys
When to Use
- Accurate/precise timing needed - When user requests accurate timestamps or precise alignment
- Sync misaligned captions - Fix timing drift in downloaded captions
- Align manual transcripts - Match text to speech precisely
- Post-transcription alignment - Improve timing from auto-generated captions
- Multi-format support - SRT, VTT, ASS, LRC, TXT, MD
When NOT to Use
- Need full transcription (use
/omnicaptions:transcribe) - No existing caption/transcript (nothing to align)
- Very short clips (<5 seconds)
Setup
pip install "omni-captions-skills[laicut]" --extra-index-url https://lattifai.github.io/pypi/simple/
API Key
Priority: LATTIFAI_API_KEY env → .env file → ~/.config/omnicaptions/config.json
If not set, ask user: Please enter your LattifAI API key (get from https://lattifai.com/dashboard/api-keys):
Then run with -k <key>. Key will be saved to config file automatically.
CLI Usage
# Basic alignment (default: JSON with word-level timing, RECOMMENDED)
omnicaptions LaiCut audio.mp3 caption.srt
# → caption_LaiCut.json
# Then convert to desired format (preserves word timing in JSON for future use)
omnicaptions convert caption_LaiCut.json -o caption_LaiCut.srt
# Smart sentence segmentation (for word-level captions like YouTube VTT)
omnicaptions LaiCut video.mp4 caption.vtt --split-sentence
| Option | Description |
|---|---|
-o, --output |
Output file (default: <caption>_LaiCut.json) |
-f, --format |
Output format (default: json) |
-k, --api-key |
LattifAI API key |
--split-sentence |
AI-powered semantic sentence segmentation |
-v, --verbose |
Show progress |
JSON Output (Recommended)
JSON output preserves word-level timing for downstream tasks:
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.
- 12d ago First seen · 174 lines · 49 tokens per session scan A f5bc4d90af47
omnicaptions-LaiCut is a skill published in the GitHub repository lattifai/omni-captions-skills (22 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 1,559 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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