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 skills/augentdevs/augent/openclawnpx skills add AugentDevs/augent --skill openclawgit clone --depth 1 https://github.com/AugentDevs/augentWrote 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/augentdevs/augent/openclaw)<a href="https://agentmods.dev/skills/augentdevs/augent/openclaw"><img src="https://agentmods.dev/badge/skills/augentdevs/augent/openclaw.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 | $0.00024 | $0.02203 |
| Opus 5 | $0.00012 | $0.01102 |
| Sonnet 5 | $0.00005 | $0.00441 |
| Haiku 4.5 | $0.00002 | $0.00220 |
Grade A, and why
augent 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 4d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Augent — Audio & Video Intelligence for AI Agents
Augent is an MCP server that gives your agent 22 tools for audio and video intelligence. Download from 1000+ sites via yt-dlp and aria2c, transcribe in 99 languages via faster-whisper, search by keyword or meaning via sentence-transformers, take notes, identify speakers via pyannote-audio, detect chapters, separate audio via Demucs v4, export clips, extract visual frames, record X/Twitter Spaces (requires user-configured auth token in ~/.augent/auth.json), and generate speech via Kokoro TTS. All processing runs locally. Downloads are saved to ~/Downloads/, notes and clips to ~/Desktop/, transcription memory to ~/.augent/memory/.
Config
{
"mcpServers": {
"augent": {
"command": "augent-mcp"
}
}
}
If augent-mcp is not in PATH, use python3 -m augent.mcp as the command instead.
Install
Install via the ClawHub install button above, or use uv tool install augent for the base package or uv tool install "augent[all]" for all features. FFmpeg is required for audio processing.
Tools
Augent exposes 22 MCP tools:
Core
| Tool | Description |
|---|---|
download_audio |
Download audio from video URLs at maximum speed. Supports YouTube, Vimeo, TikTok, Twitter/X, SoundCloud, and 1000+ sites. Uses aria2c multi-connection + concurrent fragments. |
transcribe_audio |
Full transcription of any audio file with per-segment timestamps. Returns text, language, duration, and segments. Cached by file hash. |
search_audio |
Search audio for keywords. Returns timestamped matches with context snippets. Supports clip export. |
deep_search |
Semantic search — find moments by meaning, not just keywords. Uses sentence-transformers embeddings. |
search_memory |
Search across ALL stored transcriptions in one query. Keyword or semantic mode. |
take_notes |
All-in-one: download audio from URL, transcribe, and save formatted notes. Supports 5 styles: tldr, notes, highlight, eye-candy, quiz. |
clip_export |
Export a video clip from any URL for a specific time range. Downloads only the requested segment. |
What ships with it
2 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.
- 4d ago First seen · 200 lines · 24 tokens per session scan A aedb0ed949e6
augent is a skill published in the GitHub repository AugentDevs/augent (5 stars, last pushed 4d ago), licensed MIT. It adds 24 tokens to every session and 2,203 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 skills, from other repositories
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
audio-transcriber-transcription
Speech-to-text on the audio-transcriber MCP server — run Whisper (faster-whisper, falling back to openai-whisper) over a local audio/video file or a microphone recording, and export txt/srt/vtt/json captions. Use when the agent must transcribe or translate spoken audio, generate subtitle/caption files, or pick a…
faster-whisper
Local speech-to-text using faster-whisper. 4-6x faster than OpenAI Whisper with identical accuracy; GPU acceleration enables 20x realtime transcription. SRT/VTT/TTML/CSV subtitles, speaker diarization, URL/YouTube input, batch processing with ETA, transcript search, chapter detection, per-file language map.
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.
audio-transcription-pipeline
Build audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).
audio-transcriber
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration.