oimiragieo
oimiragieo/agent-studio
相关职业SOC
基于 SOC 职业分类
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npx skills add https://github.com/oimiragieo/agent-studio --skill check-telegram-voice命令会保持在同一行。复制前请横向滚动并检查完整内容。
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基于 SOC 职业分类
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
正在显示 SKILL.md
| 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.