القائمة
Use when transcribing audio to text via the local whisper.cpp server (large-v3, CUDA) — POST an audio file, get JSON back.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Anthropic API rate limit handling - retry logic, backoff, throttling for batch workloads against Claude models
Use when building an automated test → issue → fix loop with Claude Code and GitHub issues — overnight auto-fixing, regression loops, self-healing CI.
Use when creating, editing, publishing, or deleting posts on Cyril's Workshop blog or the steponnopets.net devblog.
Use when writing or contributing a boofuzz network-protocol fuzzer in this repo — layout, formatting rules, and reading results.
Use when a task needs real-time control of a connected browser via the Browser Bridge Broker — submit JS jobs over HTTP that browsers eval and return.
Use when training a character LoRA (Chroma/Flux or Pony/SDXL) on a RunPod GPU and wiring it into the ComfyUI + pony_web render stack.
استنادا إلى تصنيف SOC المهني
| name | Whisper-Transcription |
| description | Use when transcribing audio to text via the local whisper.cpp server (large-v3, CUDA) — POST an audio file, get JSON back. |
Local speech-to-text via whisper.cpp with GPU acceleration on port 5555. Accepts WAV/MP3 and other common formats (16kHz mono WAV is optimal) as multipart POST.
curl -X POST http://localhost:5555/inference -F "file=@audio.wav"
Returns JSON {"text": ...}.
~/whisper.cpp/build/bin/whisper-server~/whisper.cpp/models/ggml-large-v3.binwhisper-server systemd service (systemctl status whisper-server, logs via journalctl -u whisper-server -f).Systemd unit at /etc/systemd/system/whisper-server.service:
[Unit]
Description=Whisper.cpp Transcription Server
After=network.target
[Service]
Type=simple
User=matt
WorkingDirectory=/home/matt/whisper.cpp/build
ExecStart=/home/matt/whisper.cpp/build/bin/whisper-server \
-m /home/matt/whisper.cpp/models/ggml-large-v3.bin \
-l en \
--port 5555 \
--host 0.0.0.0 \
--threads 4
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
~2–4x real-time; the first request after startup is slow (model load). large-v3 uses ≈ 6GB VRAM on the shared RTX 3090 — on OOM, wait for other GPU services to unload (see the Vram-GPU-OOM skill for the retry/signaling pattern).