Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Ai Agent Tools
Python library offering file handling, text extraction, data conversion, utilities, memory storage, and validation tools for AI agent workflows.
skill openclawclawhub Free
Python library offering file handling, text extraction, data conversion, utilities, memory storage, and validation tools for AI agent workflows.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- INSTALLATION.md, README.md, SKILL.md, ai_agent_tools.py, requirements.txt, setup.py
Validation
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- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- cerbug45
- Source page
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Documentation
📖 Overview
This library provides ready-to-use Python functions that AI agents can leverage to perform various tasks including file operations, text analysis, data transformation, memory management, and validation.
Installation
Method 1: Clone from GitHub git clone https://github.com/cerbug45/ai-agent-tools.git cd ai-agent-tools Method 2: Direct Download wget https://raw.githubusercontent.com/cerbug45/ai-agent-tools/main/ai_agent_tools.py Method 3: Copy-Paste Simply copy the ai_agent_tools.py file into your project directory.
Requirements
Python 3.7 or higher No external dependencies (uses only standard library)
1. FileTools - File Operations
Operations for reading, writing, and managing files. Available Methods: from ai_agent_tools import FileTools # Read a file content = FileTools.read_file("path/to/file.txt") # Write to a file FileTools.write_file("path/to/file.txt", "Hello World!") # List files in directory files = FileTools.list_files(".", extension=".py") # Check if file exists exists = FileTools.file_exists("path/to/file.txt") Use Cases: Reading configuration files Saving agent outputs Listing available resources Checking file existence before operations
2. TextTools - Text Processing
Extract information and process text data. Available Methods: from ai_agent_tools import TextTools text = "Contact: john@example.com, phone: 0532 123 45 67" # Extract emails emails = TextTools.extract_emails(text) # Output: ['john@example.com'] # Extract URLs urls = TextTools.extract_urls("Visit https://example.com") # Output: ['https://example.com'] # Extract phone numbers phones = TextTools.extract_phone_numbers(text) # Output: ['0532 123 45 67'] # Count words count = TextTools.word_count("Hello world from AI") # Output: 4 # Summarize text summary = TextTools.summarize_text("Long text here...", max_length=50) # Clean whitespace clean = TextTools.clean_whitespace("Too many spaces") # Output: "Too many spaces" Use Cases: Extracting contact information from documents Cleaning and formatting text Text summarization Data extraction from unstructured text
3. DataTools - Data Transformation
Convert between different data formats. Available Methods: from ai_agent_tools import DataTools # Save data as JSON data = {"name": "Alice", "age": 30} DataTools.save_json(data, "output.json") # Load JSON file loaded_data = DataTools.load_json("output.json") # Convert CSV text to dictionary list csv_text = """name,age,city Alice,30,New York Bob,25,London""" data_list = DataTools.csv_to_dict(csv_text) # Output: [{'name': 'Alice', 'age': '30', 'city': 'New York'}, ...] # Convert dictionary list to CSV data = [ {"name": "Alice", "age": 30}, {"name": "Bob", "age": 25} ] csv = DataTools.dict_to_csv(data) Use Cases: Saving structured data Converting between formats Processing API responses Generating reports
4. UtilityTools - General Utilities
Helper functions for common operations. Available Methods: from ai_agent_tools import UtilityTools # Get current timestamp timestamp = UtilityTools.get_timestamp() # Output: "2026-02-15 14:30:25" # Generate unique ID from text id = UtilityTools.generate_id("user_john_doe") # Output: "a3f5b2c1" # Calculate percentage percent = UtilityTools.calculate_percentage(25, 100) # Output: 25.0 # Safe division (no divide by zero error) result = UtilityTools.safe_divide(10, 0, default=0.0) # Output: 0.0 Use Cases: Timestamping events Generating unique identifiers Safe mathematical operations Data analysis calculations
5. MemoryTools - Memory Management
Store and retrieve data during agent execution. Available Methods: from ai_agent_tools import MemoryTools # Initialize memory memory = MemoryTools() # Store a value memory.store("user_name", "Alice") memory.store("session_id", "abc123") # Retrieve a value name = memory.retrieve("user_name") # Output: "Alice" # List all keys keys = memory.list_keys() # Output: ["user_name", "session_id"] # Delete a value memory.delete("session_id") # Clear all memory memory.clear() Use Cases: Maintaining conversation context Storing intermediate results Session management Caching computed values
6. ValidationTools - Data Validation
Validate different types of data. Available Methods: from ai_agent_tools import ValidationTools # Validate email is_valid = ValidationTools.is_valid_email("user@example.com") # Output: True # Validate URL is_valid = ValidationTools.is_valid_url("https://example.com") # Output: True # Validate phone number (Turkish format) is_valid = ValidationTools.is_valid_phone("0532 123 45 67") # Output: True Use Cases: Input validation Data quality checks Form validation Pre-processing data
💡 Complete Usage Example
from ai_agent_tools import ( FileTools, TextTools, DataTools, UtilityTools, MemoryTools, ValidationTools ) # Initialize memory for session memory = MemoryTools() # Read input file text = FileTools.read_file("contacts.txt") # Extract information emails = TextTools.extract_emails(text) phones = TextTools.extract_phone_numbers(text) # Validate extracted data valid_emails = [e for e in emails if ValidationTools.is_valid_email(e)] valid_phones = [p for p in phones if ValidationTools.is_valid_phone(p)] # Create structured data contacts = [] for i, (email, phone) in enumerate(zip(valid_emails, valid_phones)): contact = { "id": UtilityTools.generate_id(f"contact_{i}"), "email": email, "phone": phone, "timestamp": UtilityTools.get_timestamp() } contacts.append(contact) # Save results DataTools.save_json(contacts, "output/contacts.json") # Store in memory memory.store("total_contacts", len(contacts)) memory.store("last_processed", UtilityTools.get_timestamp()) print(f"Processed {len(contacts)} contacts") print(f"Saved to: output/contacts.json")
1. Error Handling
Always wrap file operations in try-except blocks: try: content = FileTools.read_file("data.txt") # Process content except Exception as e: print(f"Error reading file: {e}")
2. Memory Management
Clear memory when no longer needed: memory = MemoryTools() # ... use memory ... memory.clear() # Clean up
3. Data Validation
Always validate data before processing: if ValidationTools.is_valid_email(email): # Process email pass else: print(f"Invalid email: {email}")
4. Path Handling
Use absolute paths or ensure working directory is correct: import os base_dir = os.path.dirname(__file__) filepath = os.path.join(base_dir, "data", "file.txt") content = FileTools.read_file(filepath)
Chaining Operations
# Read -> Process -> Validate -> Save pipeline text = FileTools.read_file("input.txt") cleaned = TextTools.clean_whitespace(text) emails = TextTools.extract_emails(cleaned) valid = [e for e in emails if ValidationTools.is_valid_email(e)] DataTools.save_json({"emails": valid}, "output.json")
Creating Custom Workflows
class DataProcessor: def __init__(self): self.memory = MemoryTools() def process_document(self, filepath): # Read text = FileTools.read_file(filepath) # Extract emails = TextTools.extract_emails(text) urls = TextTools.extract_urls(text) # Store results self.memory.store("emails", emails) self.memory.store("urls", urls) # Generate report report = { "timestamp": UtilityTools.get_timestamp(), "file": filepath, "emails_found": len(emails), "urls_found": len(urls) } return report
Example: LangChain Integration
from langchain.tools import Tool from ai_agent_tools import FileTools, TextTools def create_file_reader_tool(): return Tool( name="ReadFile", func=FileTools.read_file, description="Read contents of a file" ) def create_email_extractor_tool(): return Tool( name="ExtractEmails", func=TextTools.extract_emails, description="Extract email addresses from text" ) tools = [create_file_reader_tool(), create_email_extractor_tool()]
Example: OpenAI Function Calling
tools = [ { "type": "function", "function": { "name": "read_file", "description": "Read a file and return its contents", "parameters": { "type": "object", "properties": { "filepath": { "type": "string", "description": "Path to the file" } }, "required": ["filepath"] } } } ] # In your agent loop def execute_function(name, arguments): if name == "read_file": return FileTools.read_file(arguments["filepath"])
🧪 Testing
Run the built-in test suite: python ai_agent_tools.py Expected output: === AI Ajanları İçin Araçlar Kütüphanesi === 1. Dosya Araçları: Okunan içerik: Merhaba AI Ajanı! 2. Metin Araçları: Bulunan emailler: ['ali@example.com'] Bulunan telefonlar: ['0532 123 45 67'] 3. Veri Araçları: CSV çıktısı: isim,yaş Ali,25 Ayşe,30 ... ✓ Tüm araçlar test edildi!
🤝 Contributing
Contributions are welcome! To contribute: Fork the repository Create a feature branch: git checkout -b feature/new-tool Commit your changes: git commit -am 'Add new tool' Push to the branch: git push origin feature/new-tool Submit a pull request
📝 License
This project is open source and available under the MIT License.
👤 Author
GitHub: @cerbug45
🐛 Issues & Support
Found a bug or need help? Please open an issue on GitHub: https://github.com/cerbug45/ai-agent-tools/issues
📚 Additional Resources
Python Documentation Regular Expressions Guide JSON Format Specification
v1.0.0 (2026-02-15)
Initial release 6 tool categories 25+ utility functions Full documentation Test suite included Happy Coding! 🚀
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs2 Scripts1 Files
- SKILL.md
- INSTALLATION.md
- README.md
- ai_agent_tools.py
- setup.py
- requirements.txt