القائمة
Check voice message pipeline config — verify Whisper, ElevenLabs/OpenAI TTS keys and dependencies.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/oimiragieo/agent-studio --skill check-telegram-voiceيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Agent frontmatter enhancements — disallowedTools, mcpServers scoping, fork_eligible field
Context management — pre-compact persistence, threshold alignment, microcompact/circuit breaker detection
Hook enhancements — updatedInput for bash safety prefixes, suppressOutput for verbose blocks, denial-based routing feedback
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | check-telegram-voice |
| description | Check voice message pipeline config — verify Whisper, ElevenLabs/OpenAI TTS keys and dependencies. |
| version | 3.0.0 |
| trigger | when user says /check-telegram-voice, "check voice", "voice status" |
| invoked_by | user |
| user_invocable | true |
| tools | ["Bash","Read","TaskUpdate"] |
| agents | ["developer","general-assistant"] |
| category | External Integrations |
| tags | ["telegram","voice","tts","whisper","elevenlabs","speech-to-text","audio","channels"] |
| best_practices | ["Always download the attachment before attempting transcription","Guard ElevenLabs text length to 4000 chars to avoid API errors","Use fallback OpenAI TTS when ELEVENLABS_API_KEY is not set","Clean up temp files after the reply is sent"] |
| error_handling | strict |
| streaming | not-applicable |
| source | builtin |
| trust_score | 100 |
| provenance_sha | 908a9a406e74832a |
This skill handles Telegram voice messages end-to-end:
attachment_file_id in channel tagtranscribe-anything)Skill({ skill: 'telegram-voice-pipeline' });
Invoke when:
attachment_file_id attribute (voice or audio message)Telegram voice messages arrive as channel tags:
<channel source="telegram" chat_id="123456" message_id="789" user="username" ts="1234567890" attachment_file_id="BQACAgIAAxkBAAIBc2...">
Key detection logic:
attachment_file_id present → voice/audio message → invoke this skillimage_path present → photo → use image handling insteadCall the Telegram MCP download tool with the file_id from the channel tag:
// MCP tool call (agent uses this directly)
mcp__telegram - ({ : });
Verify: The returned path exists and is non-empty (> 1KB for a real voice message).
Error handling: If download fails, reply with a text message: "Sorry, I couldn't download your voice message. Please try again."
Install transcribe-anything if not present:
pip install transcribe-anything
Run transcription:
transcribe-anything /tmp/voice_abc123.ogg --model medium --output_dir /tmp/tg_voice/
Read the transcript:
cat /tmp/tg_voice/voice_abc123.txt
Model selection (trade-off between speed and accuracy):
| Model | Speed | Accuracy | Use when |
|---|---|---|---|
tiny | ~2s | Low | Rapid prototyping only |
small | ~5s | Medium | Short messages, speed priority |
medium | ~12s | High | Default — best balance |
large-v3 | ~30s | Best | Long/complex messages |
Override via env: WHISPER_MODEL=small (default: medium)
Verify: /tmp/tg_voice/<filename>.txt exists and is non-empty.
Error handling: If transcription fails or output is empty, reply: "I received your voice message but couldn't transcribe it. Could you try sending it again or type your message?"
Use the transcribed text as the user input. Generate a text response using the agent's normal response logic.
transcribed_text = contents of /tmp/tg_voice/<filename>.txt
response_text = <agent's generated response to transcribed_text>
Guard max length for TTS: response_text[:4000] (ElevenLabs limit) or response_text[:4096] (OpenAI TTS limit).
ELEVENLABS_API_KEY)import os
from elevenlabs import ElevenLabs
client = ElevenLabs(api_key=os.environ["ELEVENLABS_API_KEY"])
audio = client.text_to_speech.convert(
text=response_text[:4000],
voice_id="JBFqnCBsd6RMkjVDRZzb", # George — clear, neutral voice
model_id="eleven_turbo_v2",
output_format="mp3_44100_128",
)
output_path = "/tmp/tg_voice_response.mp3"
with open(output_path, "wb") as f:
for chunk in audio:
f.write(chunk)
print(f"TTS written to {output_path}")
Override voice via env: ELEVENLABS_VOICE_ID=<voice_id> (default: JBFqnCBsd6RMkjVDRZzb)
OPENAI_API_KEY, no ELEVENLABS_API_KEY)import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
with client.audio.speech.with_streaming_response.create(
model="tts-1",
voice="nova",
input=response_text[:4096],
) as response:
response.stream_to_file("/tmp/tg_voice_response.mp3")
print("TTS written to /tmp/tg_voice_response.mp3")
Selection logic:
if os.environ.get("ELEVENLABS_API_KEY"):
# Use ElevenLabs
else:
# Use OpenAI TTS fallback
Verify: /tmp/tg_voice_response.mp3 exists and is > 1KB.
Error handling: If TTS fails, send response_text as a plain text reply instead of audio.
// MCP tool call
mcp__telegram -
relay__reply({
chat_id: '<chat_id from channel tag>',
text: response_text, // Also send the transcript so user can read it
files: ['/tmp/tg_voice_response.mp3'],
});
Note: Including text alongside the audio file gives the user both a readable transcript and the audio reply — useful for accessibility and noisy environments.
Verify: No error returned from the reply tool.
After a successful reply, clean up to avoid disk accumulation:
rm -f /tmp/tg_voice_response.mp3
rm -rf /tmp/tg_voice/
| Variable | Required | Default | Purpose |
|---|---|---|---|
ELEVENLABS_API_KEY | NO (if OpenAI set) | — | ElevenLabs TTS API key |
ELEVENLABS_VOICE_ID | NO | JBFqnCBsd6RMkjVDRZzb | ElevenLabs voice (George) |
OPENAI_API_KEY | NO (if ElevenLabs set) | — | OpenAI TTS fallback key |
WHISPER_MODEL | NO | medium | Whisper model size |
At least one of ELEVENLABS_API_KEY or OPENAI_API_KEY must be set for TTS to work.
[Telegram] User sends 10-second voice message
↓
[Agent] Detects attachment_file_id in channel tag
↓
[MCP] download_attachment(file_id) → /tmp/voice_abc123.ogg
↓
[Bash] transcribe-anything /tmp/voice_abc123.ogg --model medium → "What is the weather like today?"
↓
[Agent] Generates response: "I don't have real-time weather data, but I can help you check..."
↓
[Python] ElevenLabs TTS → /tmp/tg_voice_response.mp3
↓
[MCP] reply(chat_id, text="I don't have...", files=["/tmp/tg_voice_response.mp3"])
↓
[Telegram] User receives text + audio reply
↓
[Bash] rm /tmp/tg_voice_response.mp3 && rm -rf /tmp/tg_voice/
Total time for 10-second voice message: ~15-25 seconds (download 1s + transcribe 12s + TTS 2s + reply 1s).
attachment_file_id is not a file path, it must be resolved via the MCP toolshell: true for subprocess calls in transcription — use array args with shell: falseimage_path, not attachment_file_id; route them differentlyenable-telegram — Start the channel daemon for background Telegram monitoringtranscription — Whisper transcription workflow (used in Step 2)tts-generation — ElevenLabs and OpenAI TTS (used in Step 4)mcp__telegram-relay__download_attachment, mcp__telegram-relay__replytranscribe-anything: https://github.com/modal-labs/transcribe-anythingFor code discovery and search tasks, follow this priority order:
pnpm search:code "<query>" (Primary intent-based search).ripgrep (for exact keyword/regex matches).Before starting:
cat .claude/context/memory/learnings.md
cat .claude/context/memory/decisions.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.