Skill: augentdevs/augent (version 1.5.2)

--- name: augent description: The audio & video layer for agents. 22 local MCP tools. No cloud, no API keys. homepage: https://github.com/AugentDevs/Augent env: AUGENT_AUTH_TOKEN: description: Optional. Path to Twitter/X auth token file for Spaces recording. Default ~/.augent/auth.json. Only needed for X/Twitter Spaces. required: false AUGENT_DOWNLOAD_DIR: description: Directory for downloaded audio files. Default ~/Downloads/ required: false AUGENT_NOTES_DIR: description: Directory for notes, clips, and TTS output. Default ~/Desktop/ required: false AUGENT_MEMORY_DIR: description: Directory for persistent transcription memory. Default ~/.augent/memory/ required: false metadata: {"openclaw":{"emoji":"🎙","os":["darwin","linux","win32"],"requires":{"bins":["augent-mcp","ffmpeg","yt-dlp","aria2c"]},"install":[{"id":"uv","kind":"uv","package":"augent","bins":["augent-mcp","augent","augent-web"],"label":"Install augent (uv)"},{"id":"pip","kind":"pip","package":"augent[all]","bins":["augent-mcp","augent","augent-web"],"label":"Install augent (pip)"}]}} --- # 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 ```json { "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. | ### Analysis | Tool | Description | |------|-------------| | `chapters` | Auto-detect topic chapters with timestamps using embedding similarity. | | `search_proximity` | Find where two keywords appear near each other (e.g., "startup" within 30 words of "funding"). | | `identify_speakers` | Speaker diarization — identify who speaks when. No API keys required. | | `separate_audio` | Isolate vocals from music/noise using Meta's Demucs v4. Feed clean vocals into transcription. | | `batch_search` | Search multiple audio files in parallel. Ideal for podcast libraries or interview collections. | ### Utilities | Tool | Description | |------|-------------| | `text_to_speech` | Convert text to natural speech using Kokoro TTS. 54 voices, 9 languages. Runs in background. | | `list_files` | List media files in a directory with size info. | | `list_memories` | Browse all stored transcriptions by title, duration, and date. | | `memory_stats` | View memory statistics (file count, total duration). | | `clear_memory` | Clear the transcription memory to free disk space. | | `tag` | Add, remove, or list tags on transcriptions. Broad topic categories for organizing memories. | | `highlights` | Export the best moments from a transcription. Auto mode picks top moments; focused mode finds moments matching a topic. | | `visual` | Extract visual context from video at moments that matter. Query, auto, manual, and assist modes. Frames saved to Obsidian vault. | | `rebuild_graph` | Rebuild Obsidian graph view data for all transcriptions. Migrates files, computes wikilinks, generates MOC hubs. | | `spaces` | Download or live-record X/Twitter Spaces. Start, check status, or stop recordings. | ## Usage Examples ### Take notes from a video > "Take notes from https://youtube.com/watch?v=xxx" The agent calls `take_notes` which downloads, transcribes, and returns formatted notes. One tool call does everything. ### Search a podcast for topics > "Search this podcast for every mention of AI regulation" — provide the file path or URL. The agent uses `search_audio` for exact keyword matches, or `deep_search` for semantic matches (finds relevant discussion even without exact words). ### Transcribe and identify speakers > "Transcribe this meeting recording and tell me who said what" The agent calls `transcribe_audio` then `identify_speakers` to label each segment by speaker. ### Search across all transcriptions > "Search everything I've ever transcribed for mentions of funding" The agent uses `search_memory` to search across all stored transcriptions without needing a file path. ### Export a clip > "Clip the part where they talk about pricing" The agent uses `search_audio` or `deep_search` to find the moment, then `clip_export` to extract just that segment. ### Separate vocals from noisy audio > "This recording has music in the background, clean it up and transcribe" The agent calls `separate_audio` to isolate vocals, then `transcribe_audio` on the clean vocals track. ### Generate speech from text > "Read these notes aloud" The agent calls `text_to_speech` to generate an MP3 with natural speech. Supports multiple voices and languages. ## Note Styles When using `take_notes`, the `style` parameter controls formatting: | Style | Description | |-------|-------------| | `tldr` | Shortest possible summary. One screen. Bold key terms. | | `notes` | Clean sections with nested bullets (default). | | `highlight` | Notes with callout blocks for key insights and blockquotes with timestamps. | | `eye-candy` | Maximum visual formatting — callouts, tables, checklists, blockquotes. | | `quiz` | Multiple-choice questions with answer key. | ## Model Sizes `tiny` is the default and handles nearly everything. Only use larger models for heavy accents, poor audio quality, or maximum accuracy needs. | Model | Speed | Accuracy | |-------|-------|----------| | **tiny** | Fastest | Excellent (default) | | base | Fast | Excellent | | small | Medium | Superior | | medium | Slow | Outstanding | | large | Slowest | Maximum | ## File Paths Augent reads and writes to these locations on your machine: | Path | Purpose | |------|---------| | `~/Downloads/` | Default directory for downloaded audio files | | `~/Desktop/` | Default directory for notes, clips, and TTS output | | `~/.augent/memory/transcriptions.db` | SQLite database for persistent transcription memory | | `~/.augent/memory/transcriptions/` | Markdown files for each stored transcription | | `~/.augent/config.yaml` | User configuration (optional) | | `~/.augent/auth.json` | Twitter/X authentication cookies for Spaces recording (optional, user-created) | If Obsidian is installed, visual frames are saved to the Obsidian vault's `External Files/visual/` directory. The vault path is auto-detected from Obsidian's config. ## Network Access Network access is used for two purposes only: 1. Downloading media from user-provided URLs via yt-dlp and aria2c 2. Downloading ML models on first use (Whisper, sentence-transformers, pyannote, Demucs, Kokoro) from Hugging Face No telemetry. No background network activity. No data is uploaded. ## ML Dependencies The `augent[all]` install includes these local ML components: | Component | Purpose | Size | |-----------|---------|------| | faster-whisper | Speech-to-text transcription | ~75MB (tiny model) | | sentence-transformers | Semantic search, auto-tagging, chapter detection | ~90MB | | pyannote-audio | Speaker diarization | ~29MB | | Demucs v4 | Audio source separation (vocals from noise) | ~80MB | | Kokoro | Text-to-speech (54 voices, 9 languages) | ~200MB | All models run locally. None require API keys or cloud services. ## Requirements - Python 3.10+ - FFmpeg (audio processing) - yt-dlp + aria2c (for audio downloads) ## Links - [GitHub](https://github.com/AugentDevs/Augent) - [Documentation](https://docs.augent.app)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)