القائمة
Transcribe audio files to text using OpenAI Whisper
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use this skill when the user asks about release dates, release owners, rollout status, or launch checklist details.
Delegate coding tasks to Codex, Claude Code, or Pi agents via background host sessions. Use when: (1) building or creating new features or apps, (2) reviewing PRs or parallel coding with managed worktree isolation when subagents are available, (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat, or any work in ~/clawd workspace (never spawn agents here). Requires OpenClaw host tools with exec_command plus write_stdin.
Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, CLI/type generation, and MCP-backed skills that need a durable command path.
Create or update AgentSkills, especially when a user wants the agent to learn a reusable capability, workflow, integration, domain rule, team process, or tool usage pattern for future tasks. Use when designing, structuring, reviewing, validating, packaging, or improving skills with SKILL.md, scripts, references, and assets.
Simple operations on user-provided text files including summarization.
| name | whisper |
| description | Transcribe audio files to text using OpenAI Whisper |
Transcribe audio files to text using OpenAI Whisper.
python3 scripts/transcribe.py <audio_file> <output_file>
# Specify model size (default: base)
python3 scripts/transcribe.py audio.mp3 transcript.txt --model medium
# Specify language (improves accuracy)
python3 scripts/transcribe.py audio.mp3 transcript.txt --language zh
# Include timestamps
python3 scripts/transcribe.py audio.mp3 transcript.txt --timestamps
# JSON output with metadata
python3 scripts/transcribe.py audio.mp3 output.json --format json
audio_file (required): Path to input audio fileoutput_file (required): Path to output text/JSON file--model: Whisper model size (tiny/base/small/medium/large, default: base)--language: Language code (e.g., en, zh, es, fr, auto for detection)--timestamps: Include word-level timestamps in output--format: Output format (text/json, default: text)| Model | Parameters | Speed | Accuracy | Memory |
|---|---|---|---|---|
| tiny | 39M | ~32x | Good | ~1GB |
| base | 74M | ~16x | Better | ~1GB |
| small | 244M | ~6x | Great | ~2GB |
| medium | 769M | ~2x | Excellent | ~5GB |
| large | 1.5B | 1x | Best | ~10GB |
MP3, WAV, M4A, FLAC, OGG, AAC, WMA, and more (via FFmpeg)
pip install openai-whisper
sudo apt-get install ffmpeg # Ubuntu/Debian