Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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。
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
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。
Production-ready PDF processing toolkit with pre-built scripts, comprehensive error handling, and support for complex workflows.
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
text = pdf.pages[0].extract_text()
print(text)
python scripts/analyze_form.py input.pdf --output fields.json
# Returns: JSON with all form fields, types, and positions
python scripts/fill_form.py input.pdf data.json output.pdf
# Validates all fields before filling, includes error reporting
python scripts/extract_tables.py report.pdf --output tables.csv
# Extracts all tables with automatic column detection
All scripts include:
--help flag for all scriptsFor complete form workflows including:
See FORMS.md
For complex table extraction:
See TABLES.md
For scanned PDFs and image-based documents:
See OCR.md
analyze_form.py - Extract form field information
python scripts/analyze_form.py input.pdf [--output fields.json] [--verbose]
fill_form.py - Fill PDF forms with data
python scripts/fill_form.py input.pdf data.json output.pdf [--validate]
validate_form.py - Validate form data before filling
python scripts/validate_form.py data.json schema.json
extract_tables.py - Extract tables to CSV/Excel
python scripts/extract_tables.py input.pdf [--output tables.csv] [--format csv|excel]
extract_text.py - Extract text with formatting preservation
python scripts/extract_text.py input.pdf [--output text.txt] [--preserve-formatting]
merge_pdfs.py - Merge multiple PDFs
python scripts/merge_pdfs.py file1.pdf file2.pdf file3.pdf --output merged.pdf
split_pdf.py - Split PDF into individual pages
python scripts/split_pdf.py input.pdf --output-dir pages/
validate_pdf.py - Validate PDF integrity
python scripts/validate_pdf.py input.pdf
# 1. Analyze form structure
python scripts/analyze_form.py template.pdf --output schema.json
# 2. Validate submission data
python scripts/validate_form.py submission.json schema.json
# 3. Fill form
python scripts/fill_form.py template.pdf submission.json completed.pdf
# 4. Validate output
python scripts/validate_pdf.py completed.pdf
# 1. Extract tables
python scripts/extract_tables.py monthly_report.pdf --output data.csv
# 2. Extract text for analysis
python scripts/extract_text.py monthly_report.pdf --output report.txt
import glob
from pathlib import Path
import subprocess
# Process all PDFs in directory
for pdf_file in glob.glob("invoices/*.pdf"):
output_file = Path("processed") / Path(pdf_file).name
result = subprocess.run([
"python", "scripts/extract_text.py",
pdf_file,
"--output", str(output_file)
], capture_output=True)
if result.returncode == 0:
print(f"✓ Processed: {pdf_file}")
else:
print(f"✗ Failed: {pdf_file} - {result.stderr}")
All scripts follow consistent error patterns:
# Exit codes
# 0 - Success
# 1 - File not found
# 2 - Invalid input
# 3 - Processing error
# 4 - Validation error
# Example usage in automation
result = subprocess.run(["python", "scripts/fill_form.py", ...])
if result.returncode == 0:
print("Success")
elif result.returncode == 4:
print("Validation failed - check input data")
else:
print(f"Error occurred: {result.returncode}")
All scripts require:
pip install pdfplumber pypdf pillow pytesseract pandas
Optional for OCR:
# Install tesseract-ocr system package
# macOS: brew install tesseract
# Ubuntu: apt-get install tesseract-ocr
# Windows: Download from GitHub releases
--parallel flag (where supported)"Module not found" errors:
pip install -r requirements.txt
Tesseract not found:
# Install tesseract system package (see Dependencies)
Memory errors with large PDFs:
# Process page by page instead of loading entire PDF
with pdfplumber.open("large.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
# Process page immediately
Permission errors:
chmod +x scripts/*.py
All scripts support --help:
python scripts/analyze_form.py --help
python scripts/extract_tables.py --help
For detailed documentation on specific topics, see:
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概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。