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 agentmods add commands/ivanlutsenko/awac-ai-agent-plugins/transcribegit clone --depth 1 https://github.com/IvanLutsenko/awac-ai-agent-pluginsWrote 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/commands/ivanlutsenko/awac-ai-agent-plugins/transcribe)<a href="https://agentmods.dev/commands/ivanlutsenko/awac-ai-agent-plugins/transcribe"><img src="https://agentmods.dev/badge/commands/ivanlutsenko/awac-ai-agent-plugins/transcribe.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.00265 |
| Opus 5 | $0.00005 | $0.00133 |
| Sonnet 5 | $0.00002 | $0.00053 |
| Haiku 4.5 | $0.00001 | $0.00026 |
Grade A, and why
transcribe 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 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.
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.
What it actually says
Transcribe Video
Transcribe a video file using OpenAI Whisper. Outputs a JSON file with timestamped segments.
Arguments
Parse $ARGUMENTS for:
<video_path>— required, path to video file--api— use OpenAI Whisper API instead of local model--language LANG— language code (default: ru)
Steps
1. Check dependencies
bash ${CLAUDE_PLUGIN_ROOT}/scripts/install-deps.sh [--api if passed]
2. Determine output location
Output directory: same directory as the video file.
Output file: <video_name>_transcript.json
3. Transcribe
bash ${CLAUDE_PLUGIN_ROOT}/scripts/transcribe.sh "<video_path>" "<output_dir>" [--api] [--language LANG]
4. Report
Tell the user:
- Output file path
- Number of segments
- Total duration covered
- First few lines of transcript as preview
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 · 44 lines · 11 tokens per session scan A e56addc84c3d
transcribe is a command published in the GitHub repository IvanLutsenko/awac-ai-agent-plugins (2 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 265 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
brand-generate
Generate an on-brand document from a saved Brand Profile.
stt
Transcribe a local audio file or remote audio URL into text.
audition-voices
Generate voice audition samples for a character using Venice TTS.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
tag
Add or remove free-form tags on photos, then find them with search --tag.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.