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Transcribe audio files locally using faster-whisper (100% offline, free, high quality)
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Transforms the Hermes agent from a reactive question-answerer into a proactive autonomous executor. ARCHITECT takes any high-level goal, decomposes it into a dependency-aware task graph, executes each step with validation, self-corrects on failure, and delivers results — all without hand-holding. The missing execution layer for personal AI agents. Zero dependencies. Zero config. Works with any model. Pairs with apex-agent and agent-memoria for the complete autonomous agent stack.
Auto-reflective self-improvement skill — extracts learnings from corrections and success patterns, permanently encodes them into memory and skills. Philosophy: Correct once, never again.
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
Integrate existing autonomous agents (like Hermes) into multi-agent orchestration platforms (AionUI, LangChain, etc.) via ACP over stdio.
MiniMax Agent Platform (agent.minimax.io) — MaxHermes, MaxClaw, Skills marketplace. Relacao com Hermes Agent (NousResearch) e OpenClaw.
Paperclip AI agent operations — creating agents with hierarchy, autonomous operation via hierarchical issues, and troubleshooting. Use when: creating agents, setting up org hierarchy, recovering from errors, or monitoring agent health.
| name | whisper-local-transcription |
| description | Transcribe audio files locally using faster-whisper (100% offline, free, high quality) |
Transcribe audio files (Telegram voice messages, etc.) using faster-whisper 100% locally and free.
Model: medium (already downloaded and cached)
Python: 3.12 (system Python at /usr/bin/python3)
Location: /home/alvarobiano/transcribe_audio.py
# Install in user space (Python 3.12)
pip3 install --break-system-packages faster-whisper
# Verify (use system Python, NOT the one at ~/.local/bin)
export PYTHONPATH=""
/usr/bin/python3 -c "from faster_whisper import WhisperModel; print('OK')"
The server has two Python versions:
/home/alvarobiano/.local/bin/python3 → Python 3.14 (BROKEN for faster-whisper)/usr/bin/python3 → Python 3.12 (WORKS)The script auto-switches to the correct Python version. Just run:
/home/alvarobiano/transcribe_audio.py <audio_file>
When user sends audio via Telegram, the audio is saved to a temp file. Run:
/usr/bin/python3 /home/alvarobiano/transcribe_audio.py <path_to_audio>
from faster_whisper import WhisperModel
model = WhisperModel('medium', device='cpu', compute_type='int8')
segments, info = model.transcribe("audio.ogg", language="pt")
/home/alvarobiano/transcribe_audio.py~/.cache/huggingface/hub/