Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
日本語の概要は準備中です。原文の説明を表示しています。
Create audiobooks from web content or text files. Handles content fetching, text processing, and TTS conversion with automatic fallback between ElevenLabs, OpenAI TTS, and gTTS.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Create audiobooks from web articles, essays, or text files. This skill covers the full pipeline: content fetching, text processing, and audio generation.
import os
# 1. Check which TTS API is available
def get_tts_provider():
if os.environ.get("ELEVENLABS_API_KEY"):
return "elevenlabs"
elif os.environ.get("OPENAI_API_KEY"):
return "openai"
else:
return "gtts" # Free, no API key needed
provider = get_tts_provider()
print(f"Using TTS provider: {provider}")
WebFetch and similar tools may return summaries instead of full text. Always verify:
import subprocess
def fetch_article_content(url):
"""Fetch article content using curl for reliability."""
# Use curl to get raw HTML - more reliable than web fetch tools
result = subprocess.run(
["curl", "-s", url],
capture_output=True,
text=True
)
html = result.stdout
# Strip HTML tags (basic approach)
import re
text = re.sub(r'<script[^>]*>.*?</script>', '', html, flags=re.DOTALL)
text = re.sub(r'<style[^>]*>.*?</style>', '', html, flags=re.DOTALL)
text = re.sub(r'<[^>]+>', ' ', text)
text = re.sub(r'\s+', ' ', text).strip()
return text
Before converting to audio, verify:
def verify_content(text, expected_min_chars=1000):
"""Basic verification that content is complete."""
if len(text) < expected_min_chars:
print(f"WARNING: Content may be truncated ({len(text)} chars)")
return False
if "summary" in text.lower()[:500] or "key points" in text.lower()[:500]:
print("WARNING: Content appears to be a summary, not full text")
return False
return True
import re
def clean_text_for_tts(text):
"""Clean text for better TTS output."""
# Remove URLs
text = re.sub(r'http[s]?://\S+', '', text)
# Remove footnote markers like [1], [2]
text = re.sub(r'\[\d+\]', '', text)
# Normalize whitespace
text = re.sub(r'\s+', ' ', text)
# Remove special characters that confuse TTS
text = re.sub(r'[^\w\s.,!?;:\'"()-]', '', text)
return text.strip()
def chunk_text(text, max_chars=4000):
"""Split text into chunks at sentence boundaries."""
sentences = re.split(r'(?<=[.!?])\s+', text)
chunks = []
current_chunk = ""
for sentence in sentences:
if len(current_chunk) + len(sentence) < max_chars:
current_chunk += sentence + " "
else:
if current_chunk:
chunks.append(current_chunk.strip())
current_chunk = sentence + " "
if current_chunk:
chunks.append(current_chunk.strip())
return chunks
import os
import subprocess
def create_audiobook(text, output_path):
"""Convert text to audiobook with automatic TTS provider selection."""
# Check available providers
has_elevenlabs = bool(os.environ.get("ELEVENLABS_API_KEY"))
has_openai = bool(os.environ.get("OPENAI_API_KEY"))
if has_elevenlabs:
print("Using ElevenLabs TTS (highest quality)")
return create_with_elevenlabs(text, output_path)
elif has_openai:
print("Using OpenAI TTS (high quality)")
return create_with_openai(text, output_path)
else:
print("Using gTTS (free, no API key required)")
return create_with_gtts(text, output_path)
import requests
def create_with_elevenlabs(text, output_path):
"""Generate audiobook using ElevenLabs API."""
api_key = os.environ.get("ELEVENLABS_API_KEY")
voice_id = "21m00Tcm4TlvDq8ikWAM" # Rachel - calm female voice
chunks = chunk_text(text, max_chars=4500)
audio_files = []
for i, chunk in enumerate(chunks):
chunk_file = f"/tmp/chunk_{i:03d}.mp3"
response = requests.post(
f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}",
headers={
"xi-api-key": api_key,
"Content-Type": "application/json"
},
json={
"text": chunk,
"model_id": "eleven_turbo_v2_5",
"voice_settings": {"stability": 0.5, "similarity_boost": 0.75}
}
)
if response.status_code == 200:
with open(chunk_file, "wb") as f:
f.write(response.content)
audio_files.append(chunk_file)
else:
print(f"Error: {response.status_code} - {response.text}")
return False
return concatenate_audio(audio_files, output_path)
def create_with_openai(text, output_path):
"""Generate audiobook using OpenAI TTS API."""
api_key = os.environ.get("OPENAI_API_KEY")
chunks = chunk_text(text, max_chars=4000)
audio_files = []
for i, chunk in enumerate(chunks):
chunk_file = f"/tmp/chunk_{i:03d}.mp3"
response = requests.post(
"https://api.openai.com/v1/audio/speech",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
},
json={
"model": "tts-1",
"input": chunk,
"voice": "onyx", # Deep male voice, good for essays
"response_format": "mp3"
}
)
if response.status_code == 200:
with open(chunk_file, "wb") as f:
f.write(response.content)
audio_files.append(chunk_file)
else:
print(f"Error: {response.status_code} - {response.text}")
return False
return concatenate_audio(audio_files, output_path)
def create_with_gtts(text, output_path):
"""Generate audiobook using gTTS (free, no API key)."""
from gtts import gTTS
from pydub import AudioSegment
chunks = chunk_text(text, max_chars=4500)
audio_files = []
for i, chunk in enumerate(chunks):
chunk_file = f"/tmp/chunk_{i:03d}.mp3"
tts = gTTS(text=chunk, lang='en', slow=False)
tts.save(chunk_file)
audio_files.append(chunk_file)
return concatenate_audio(audio_files, output_path)
def concatenate_audio(audio_files, output_path):
"""Concatenate multiple audio files using ffmpeg."""
if not audio_files:
return False
# Create file list for ffmpeg
list_file = "/tmp/audio_list.txt"
with open(list_file, "w") as f:
for audio_file in audio_files:
f.write(f"file '{audio_file}'\n")
# Concatenate with ffmpeg
result = subprocess.run([
"ffmpeg", "-y", "-f", "concat", "-safe", "0",
"-i", list_file, "-c", "copy", output_path
], capture_output=True)
# Cleanup temp files
import os
for f in audio_files:
os.unlink(f)
os.unlink(list_file)
return result.returncode == 0
#!/usr/bin/env python3
"""Create audiobook from web articles."""
import os
import re
import subprocess
import requests
# ... include all helper functions above ...
def main():
# Fetch articles
urls = [
"https://example.com/article1",
"https://example.com/article2"
]
all_text = ""
for url in urls:
print(f"Fetching: {url}")
text = fetch_article_content(url)
if not verify_content(text):
print(f"WARNING: Content from {url} may be incomplete")
all_text += f"\n\n{text}"
# Clean and convert
clean_text = clean_text_for_tts(all_text)
print(f"Total text: {len(clean_text)} characters")
# Create audiobook
success = create_audiobook(clean_text, "/root/audiobook.mp3")
if success:
print("Audiobook created successfully!")
else:
print("Failed to create audiobook")
if __name__ == "__main__":
main()
| Provider | Quality | Cost | API Key Required | Best For |
|---|---|---|---|---|
| ElevenLabs | Excellent | Paid | Yes | Professional audiobooks |
| OpenAI TTS | Very Good | Paid | Yes | General purpose |
| gTTS | Good | Free | No | Testing, budget projects |
curl directly instead of web fetch toolsecho $OPENAI_API_KEYまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
日本語の概要は準備中です。原文の説明を表示しています。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
日本語の概要は準備中です。原文の説明を表示しています。
Redact text from PDF documents for blind review anonymization
日本語の概要は準備中です。原文の説明を表示しています。
Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze failed GitHub Action jobs for a pull request.
日本語の概要は準備中です。原文の説明を表示しています。
Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
日本語の概要は準備中です。原文の説明を表示しています。