{ "schemaVersion": "1.0", "item": { "slug": "ai-agent-tools", "name": "Ai Agent Tools", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/cerbug45/ai-agent-tools", "canonicalUrl": "https://clawhub.ai/cerbug45/ai-agent-tools", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/ai-agent-tools", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=ai-agent-tools", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "INSTALLATION.md", "README.md", "SKILL.md", "ai_agent_tools.py", "requirements.txt", "setup.py" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/ai-agent-tools" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/ai-agent-tools", "agentPageUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent", "manifestUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent.json", "briefUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "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." }, { "label": "Upgrade existing", "body": "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." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "📖 Overview", "body": "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." }, { "title": "Installation", "body": "Method 1: Clone from GitHub\n\ngit clone https://github.com/cerbug45/ai-agent-tools.git\ncd ai-agent-tools\n\nMethod 2: Direct Download\n\nwget https://raw.githubusercontent.com/cerbug45/ai-agent-tools/main/ai_agent_tools.py\n\nMethod 3: Copy-Paste\n\nSimply copy the ai_agent_tools.py file into your project directory." }, { "title": "Requirements", "body": "Python 3.7 or higher\nNo external dependencies (uses only standard library)" }, { "title": "1. FileTools - File Operations", "body": "Operations for reading, writing, and managing files.\n\nAvailable Methods:\n\nfrom ai_agent_tools import FileTools\n\n# Read a file\ncontent = FileTools.read_file(\"path/to/file.txt\")\n\n# Write to a file\nFileTools.write_file(\"path/to/file.txt\", \"Hello World!\")\n\n# List files in directory\nfiles = FileTools.list_files(\".\", extension=\".py\")\n\n# Check if file exists\nexists = FileTools.file_exists(\"path/to/file.txt\")\n\nUse Cases:\n\nReading configuration files\nSaving agent outputs\nListing available resources\nChecking file existence before operations" }, { "title": "2. TextTools - Text Processing", "body": "Extract information and process text data.\n\nAvailable Methods:\n\nfrom ai_agent_tools import TextTools\n\ntext = \"Contact: john@example.com, phone: 0532 123 45 67\"\n\n# Extract emails\nemails = TextTools.extract_emails(text)\n# Output: ['john@example.com']\n\n# Extract URLs\nurls = TextTools.extract_urls(\"Visit https://example.com\")\n# Output: ['https://example.com']\n\n# Extract phone numbers\nphones = TextTools.extract_phone_numbers(text)\n# Output: ['0532 123 45 67']\n\n# Count words\ncount = TextTools.word_count(\"Hello world from AI\")\n# Output: 4\n\n# Summarize text\nsummary = TextTools.summarize_text(\"Long text here...\", max_length=50)\n\n# Clean whitespace\nclean = TextTools.clean_whitespace(\"Too many spaces\")\n# Output: \"Too many spaces\"\n\nUse Cases:\n\nExtracting contact information from documents\nCleaning and formatting text\nText summarization\nData extraction from unstructured text" }, { "title": "3. DataTools - Data Transformation", "body": "Convert between different data formats.\n\nAvailable Methods:\n\nfrom ai_agent_tools import DataTools\n\n# Save data as JSON\ndata = {\"name\": \"Alice\", \"age\": 30}\nDataTools.save_json(data, \"output.json\")\n\n# Load JSON file\nloaded_data = DataTools.load_json(\"output.json\")\n\n# Convert CSV text to dictionary list\ncsv_text = \"\"\"name,age,city\nAlice,30,New York\nBob,25,London\"\"\"\ndata_list = DataTools.csv_to_dict(csv_text)\n# Output: [{'name': 'Alice', 'age': '30', 'city': 'New York'}, ...]\n\n# Convert dictionary list to CSV\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Bob\", \"age\": 25}\n]\ncsv = DataTools.dict_to_csv(data)\n\nUse Cases:\n\nSaving structured data\nConverting between formats\nProcessing API responses\nGenerating reports" }, { "title": "4. UtilityTools - General Utilities", "body": "Helper functions for common operations.\n\nAvailable Methods:\n\nfrom ai_agent_tools import UtilityTools\n\n# Get current timestamp\ntimestamp = UtilityTools.get_timestamp()\n# Output: \"2026-02-15 14:30:25\"\n\n# Generate unique ID from text\nid = UtilityTools.generate_id(\"user_john_doe\")\n# Output: \"a3f5b2c1\"\n\n# Calculate percentage\npercent = UtilityTools.calculate_percentage(25, 100)\n# Output: 25.0\n\n# Safe division (no divide by zero error)\nresult = UtilityTools.safe_divide(10, 0, default=0.0)\n# Output: 0.0\n\nUse Cases:\n\nTimestamping events\nGenerating unique identifiers\nSafe mathematical operations\nData analysis calculations" }, { "title": "5. MemoryTools - Memory Management", "body": "Store and retrieve data during agent execution.\n\nAvailable Methods:\n\nfrom ai_agent_tools import MemoryTools\n\n# Initialize memory\nmemory = MemoryTools()\n\n# Store a value\nmemory.store(\"user_name\", \"Alice\")\nmemory.store(\"session_id\", \"abc123\")\n\n# Retrieve a value\nname = memory.retrieve(\"user_name\")\n# Output: \"Alice\"\n\n# List all keys\nkeys = memory.list_keys()\n# Output: [\"user_name\", \"session_id\"]\n\n# Delete a value\nmemory.delete(\"session_id\")\n\n# Clear all memory\nmemory.clear()\n\nUse Cases:\n\nMaintaining conversation context\nStoring intermediate results\nSession management\nCaching computed values" }, { "title": "6. ValidationTools - Data Validation", "body": "Validate different types of data.\n\nAvailable Methods:\n\nfrom ai_agent_tools import ValidationTools\n\n# Validate email\nis_valid = ValidationTools.is_valid_email(\"user@example.com\")\n# Output: True\n\n# Validate URL\nis_valid = ValidationTools.is_valid_url(\"https://example.com\")\n# Output: True\n\n# Validate phone number (Turkish format)\nis_valid = ValidationTools.is_valid_phone(\"0532 123 45 67\")\n# Output: True\n\nUse Cases:\n\nInput validation\nData quality checks\nForm validation\nPre-processing data" }, { "title": "💡 Complete Usage Example", "body": "from ai_agent_tools import (\n FileTools, TextTools, DataTools, \n UtilityTools, MemoryTools, ValidationTools\n)\n\n# Initialize memory for session\nmemory = MemoryTools()\n\n# Read input file\ntext = FileTools.read_file(\"contacts.txt\")\n\n# Extract information\nemails = TextTools.extract_emails(text)\nphones = TextTools.extract_phone_numbers(text)\n\n# Validate extracted data\nvalid_emails = [e for e in emails if ValidationTools.is_valid_email(e)]\nvalid_phones = [p for p in phones if ValidationTools.is_valid_phone(p)]\n\n# Create structured data\ncontacts = []\nfor i, (email, phone) in enumerate(zip(valid_emails, valid_phones)):\n contact = {\n \"id\": UtilityTools.generate_id(f\"contact_{i}\"),\n \"email\": email,\n \"phone\": phone,\n \"timestamp\": UtilityTools.get_timestamp()\n }\n contacts.append(contact)\n\n# Save results\nDataTools.save_json(contacts, \"output/contacts.json\")\n\n# Store in memory\nmemory.store(\"total_contacts\", len(contacts))\nmemory.store(\"last_processed\", UtilityTools.get_timestamp())\n\nprint(f\"Processed {len(contacts)} contacts\")\nprint(f\"Saved to: output/contacts.json\")" }, { "title": "1. Error Handling", "body": "Always wrap file operations in try-except blocks:\n\ntry:\n content = FileTools.read_file(\"data.txt\")\n # Process content\nexcept Exception as e:\n print(f\"Error reading file: {e}\")" }, { "title": "2. Memory Management", "body": "Clear memory when no longer needed:\n\nmemory = MemoryTools()\n# ... use memory ...\nmemory.clear() # Clean up" }, { "title": "3. Data Validation", "body": "Always validate data before processing:\n\nif ValidationTools.is_valid_email(email):\n # Process email\n pass\nelse:\n print(f\"Invalid email: {email}\")" }, { "title": "4. Path Handling", "body": "Use absolute paths or ensure working directory is correct:\n\nimport os\n\nbase_dir = os.path.dirname(__file__)\nfilepath = os.path.join(base_dir, \"data\", \"file.txt\")\ncontent = FileTools.read_file(filepath)" }, { "title": "Chaining Operations", "body": "# Read -> Process -> Validate -> Save pipeline\ntext = FileTools.read_file(\"input.txt\")\ncleaned = TextTools.clean_whitespace(text)\nemails = TextTools.extract_emails(cleaned)\nvalid = [e for e in emails if ValidationTools.is_valid_email(e)]\nDataTools.save_json({\"emails\": valid}, \"output.json\")" }, { "title": "Creating Custom Workflows", "body": "class DataProcessor:\n def __init__(self):\n self.memory = MemoryTools()\n \n def process_document(self, filepath):\n # Read\n text = FileTools.read_file(filepath)\n \n # Extract\n emails = TextTools.extract_emails(text)\n urls = TextTools.extract_urls(text)\n \n # Store results\n self.memory.store(\"emails\", emails)\n self.memory.store(\"urls\", urls)\n \n # Generate report\n report = {\n \"timestamp\": UtilityTools.get_timestamp(),\n \"file\": filepath,\n \"emails_found\": len(emails),\n \"urls_found\": len(urls)\n }\n \n return report" }, { "title": "Example: LangChain Integration", "body": "from langchain.tools import Tool\nfrom ai_agent_tools import FileTools, TextTools\n\ndef create_file_reader_tool():\n return Tool(\n name=\"ReadFile\",\n func=FileTools.read_file,\n description=\"Read contents of a file\"\n )\n\ndef create_email_extractor_tool():\n return Tool(\n name=\"ExtractEmails\",\n func=TextTools.extract_emails,\n description=\"Extract email addresses from text\"\n )\n\ntools = [create_file_reader_tool(), create_email_extractor_tool()]" }, { "title": "Example: OpenAI Function Calling", "body": "tools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"read_file\",\n \"description\": \"Read a file and return its contents\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"filepath\": {\n \"type\": \"string\",\n \"description\": \"Path to the file\"\n }\n },\n \"required\": [\"filepath\"]\n }\n }\n }\n]\n\n# In your agent loop\ndef execute_function(name, arguments):\n if name == \"read_file\":\n return FileTools.read_file(arguments[\"filepath\"])" }, { "title": "🧪 Testing", "body": "Run the built-in test suite:\n\npython ai_agent_tools.py\n\nExpected output:\n\n=== AI Ajanları İçin Araçlar Kütüphanesi ===\n\n1. Dosya Araçları:\n Okunan içerik: Merhaba AI Ajanı!\n\n2. Metin Araçları:\n Bulunan emailler: ['ali@example.com']\n Bulunan telefonlar: ['0532 123 45 67']\n\n3. Veri Araçları:\n CSV çıktısı:\n isim,yaş\n Ali,25\n Ayşe,30\n\n...\n\n✓ Tüm araçlar test edildi!" }, { "title": "🤝 Contributing", "body": "Contributions are welcome! To contribute:\n\nFork the repository\nCreate a feature branch: git checkout -b feature/new-tool\nCommit your changes: git commit -am 'Add new tool'\nPush to the branch: git push origin feature/new-tool\nSubmit a pull request" }, { "title": "📝 License", "body": "This project is open source and available under the MIT License." }, { "title": "👤 Author", "body": "GitHub: @cerbug45" }, { "title": "🐛 Issues & Support", "body": "Found a bug or need help? Please open an issue on GitHub:\nhttps://github.com/cerbug45/ai-agent-tools/issues" }, { "title": "📚 Additional Resources", "body": "Python Documentation\nRegular Expressions Guide\nJSON Format Specification" }, { "title": "v1.0.0 (2026-02-15)", "body": "Initial release\n6 tool categories\n25+ utility functions\nFull documentation\nTest suite included\n\nHappy Coding! 🚀" } ], "body": "AI Agent Tools - Python Utility Library for AI Agents\n📖 Overview\n\nThis 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.\n\n⚡ Quick Start\nInstallation\nMethod 1: Clone from GitHub\ngit clone https://github.com/cerbug45/ai-agent-tools.git\ncd ai-agent-tools\n\nMethod 2: Direct Download\nwget https://raw.githubusercontent.com/cerbug45/ai-agent-tools/main/ai_agent_tools.py\n\nMethod 3: Copy-Paste\n\nSimply copy the ai_agent_tools.py file into your project directory.\n\nRequirements\nPython 3.7 or higher\nNo external dependencies (uses only standard library)\n🛠️ Available Tools\n1. FileTools - File Operations\n\nOperations for reading, writing, and managing files.\n\nAvailable Methods:\n\nfrom ai_agent_tools import FileTools\n\n# Read a file\ncontent = FileTools.read_file(\"path/to/file.txt\")\n\n# Write to a file\nFileTools.write_file(\"path/to/file.txt\", \"Hello World!\")\n\n# List files in directory\nfiles = FileTools.list_files(\".\", extension=\".py\")\n\n# Check if file exists\nexists = FileTools.file_exists(\"path/to/file.txt\")\n\n\nUse Cases:\n\nReading configuration files\nSaving agent outputs\nListing available resources\nChecking file existence before operations\n2. TextTools - Text Processing\n\nExtract information and process text data.\n\nAvailable Methods:\n\nfrom ai_agent_tools import TextTools\n\ntext = \"Contact: john@example.com, phone: 0532 123 45 67\"\n\n# Extract emails\nemails = TextTools.extract_emails(text)\n# Output: ['john@example.com']\n\n# Extract URLs\nurls = TextTools.extract_urls(\"Visit https://example.com\")\n# Output: ['https://example.com']\n\n# Extract phone numbers\nphones = TextTools.extract_phone_numbers(text)\n# Output: ['0532 123 45 67']\n\n# Count words\ncount = TextTools.word_count(\"Hello world from AI\")\n# Output: 4\n\n# Summarize text\nsummary = TextTools.summarize_text(\"Long text here...\", max_length=50)\n\n# Clean whitespace\nclean = TextTools.clean_whitespace(\"Too many spaces\")\n# Output: \"Too many spaces\"\n\n\nUse Cases:\n\nExtracting contact information from documents\nCleaning and formatting text\nText summarization\nData extraction from unstructured text\n3. DataTools - Data Transformation\n\nConvert between different data formats.\n\nAvailable Methods:\n\nfrom ai_agent_tools import DataTools\n\n# Save data as JSON\ndata = {\"name\": \"Alice\", \"age\": 30}\nDataTools.save_json(data, \"output.json\")\n\n# Load JSON file\nloaded_data = DataTools.load_json(\"output.json\")\n\n# Convert CSV text to dictionary list\ncsv_text = \"\"\"name,age,city\nAlice,30,New York\nBob,25,London\"\"\"\ndata_list = DataTools.csv_to_dict(csv_text)\n# Output: [{'name': 'Alice', 'age': '30', 'city': 'New York'}, ...]\n\n# Convert dictionary list to CSV\ndata = [\n {\"name\": \"Alice\", \"age\": 30},\n {\"name\": \"Bob\", \"age\": 25}\n]\ncsv = DataTools.dict_to_csv(data)\n\n\nUse Cases:\n\nSaving structured data\nConverting between formats\nProcessing API responses\nGenerating reports\n4. UtilityTools - General Utilities\n\nHelper functions for common operations.\n\nAvailable Methods:\n\nfrom ai_agent_tools import UtilityTools\n\n# Get current timestamp\ntimestamp = UtilityTools.get_timestamp()\n# Output: \"2026-02-15 14:30:25\"\n\n# Generate unique ID from text\nid = UtilityTools.generate_id(\"user_john_doe\")\n# Output: \"a3f5b2c1\"\n\n# Calculate percentage\npercent = UtilityTools.calculate_percentage(25, 100)\n# Output: 25.0\n\n# Safe division (no divide by zero error)\nresult = UtilityTools.safe_divide(10, 0, default=0.0)\n# Output: 0.0\n\n\nUse Cases:\n\nTimestamping events\nGenerating unique identifiers\nSafe mathematical operations\nData analysis calculations\n5. MemoryTools - Memory Management\n\nStore and retrieve data during agent execution.\n\nAvailable Methods:\n\nfrom ai_agent_tools import MemoryTools\n\n# Initialize memory\nmemory = MemoryTools()\n\n# Store a value\nmemory.store(\"user_name\", \"Alice\")\nmemory.store(\"session_id\", \"abc123\")\n\n# Retrieve a value\nname = memory.retrieve(\"user_name\")\n# Output: \"Alice\"\n\n# List all keys\nkeys = memory.list_keys()\n# Output: [\"user_name\", \"session_id\"]\n\n# Delete a value\nmemory.delete(\"session_id\")\n\n# Clear all memory\nmemory.clear()\n\n\nUse Cases:\n\nMaintaining conversation context\nStoring intermediate results\nSession management\nCaching computed values\n6. ValidationTools - Data Validation\n\nValidate different types of data.\n\nAvailable Methods:\n\nfrom ai_agent_tools import ValidationTools\n\n# Validate email\nis_valid = ValidationTools.is_valid_email(\"user@example.com\")\n# Output: True\n\n# Validate URL\nis_valid = ValidationTools.is_valid_url(\"https://example.com\")\n# Output: True\n\n# Validate phone number (Turkish format)\nis_valid = ValidationTools.is_valid_phone(\"0532 123 45 67\")\n# Output: True\n\n\nUse Cases:\n\nInput validation\nData quality checks\nForm validation\nPre-processing data\n💡 Complete Usage Example\nfrom ai_agent_tools import (\n FileTools, TextTools, DataTools, \n UtilityTools, MemoryTools, ValidationTools\n)\n\n# Initialize memory for session\nmemory = MemoryTools()\n\n# Read input file\ntext = FileTools.read_file(\"contacts.txt\")\n\n# Extract information\nemails = TextTools.extract_emails(text)\nphones = TextTools.extract_phone_numbers(text)\n\n# Validate extracted data\nvalid_emails = [e for e in emails if ValidationTools.is_valid_email(e)]\nvalid_phones = [p for p in phones if ValidationTools.is_valid_phone(p)]\n\n# Create structured data\ncontacts = []\nfor i, (email, phone) in enumerate(zip(valid_emails, valid_phones)):\n contact = {\n \"id\": UtilityTools.generate_id(f\"contact_{i}\"),\n \"email\": email,\n \"phone\": phone,\n \"timestamp\": UtilityTools.get_timestamp()\n }\n contacts.append(contact)\n\n# Save results\nDataTools.save_json(contacts, \"output/contacts.json\")\n\n# Store in memory\nmemory.store(\"total_contacts\", len(contacts))\nmemory.store(\"last_processed\", UtilityTools.get_timestamp())\n\nprint(f\"Processed {len(contacts)} contacts\")\nprint(f\"Saved to: output/contacts.json\")\n\n🎯 Best Practices\n1. Error Handling\n\nAlways wrap file operations in try-except blocks:\n\ntry:\n content = FileTools.read_file(\"data.txt\")\n # Process content\nexcept Exception as e:\n print(f\"Error reading file: {e}\")\n\n2. Memory Management\n\nClear memory when no longer needed:\n\nmemory = MemoryTools()\n# ... use memory ...\nmemory.clear() # Clean up\n\n3. Data Validation\n\nAlways validate data before processing:\n\nif ValidationTools.is_valid_email(email):\n # Process email\n pass\nelse:\n print(f\"Invalid email: {email}\")\n\n4. Path Handling\n\nUse absolute paths or ensure working directory is correct:\n\nimport os\n\nbase_dir = os.path.dirname(__file__)\nfilepath = os.path.join(base_dir, \"data\", \"file.txt\")\ncontent = FileTools.read_file(filepath)\n\n🔧 Advanced Usage\nChaining Operations\n# Read -> Process -> Validate -> Save pipeline\ntext = FileTools.read_file(\"input.txt\")\ncleaned = TextTools.clean_whitespace(text)\nemails = TextTools.extract_emails(cleaned)\nvalid = [e for e in emails if ValidationTools.is_valid_email(e)]\nDataTools.save_json({\"emails\": valid}, \"output.json\")\n\nCreating Custom Workflows\nclass DataProcessor:\n def __init__(self):\n self.memory = MemoryTools()\n \n def process_document(self, filepath):\n # Read\n text = FileTools.read_file(filepath)\n \n # Extract\n emails = TextTools.extract_emails(text)\n urls = TextTools.extract_urls(text)\n \n # Store results\n self.memory.store(\"emails\", emails)\n self.memory.store(\"urls\", urls)\n \n # Generate report\n report = {\n \"timestamp\": UtilityTools.get_timestamp(),\n \"file\": filepath,\n \"emails_found\": len(emails),\n \"urls_found\": len(urls)\n }\n \n return report\n\n📦 Integration with AI Agents\nExample: LangChain Integration\nfrom langchain.tools import Tool\nfrom ai_agent_tools import FileTools, TextTools\n\ndef create_file_reader_tool():\n return Tool(\n name=\"ReadFile\",\n func=FileTools.read_file,\n description=\"Read contents of a file\"\n )\n\ndef create_email_extractor_tool():\n return Tool(\n name=\"ExtractEmails\",\n func=TextTools.extract_emails,\n description=\"Extract email addresses from text\"\n )\n\ntools = [create_file_reader_tool(), create_email_extractor_tool()]\n\nExample: OpenAI Function Calling\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"read_file\",\n \"description\": \"Read a file and return its contents\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"filepath\": {\n \"type\": \"string\",\n \"description\": \"Path to the file\"\n }\n },\n \"required\": [\"filepath\"]\n }\n }\n }\n]\n\n# In your agent loop\ndef execute_function(name, arguments):\n if name == \"read_file\":\n return FileTools.read_file(arguments[\"filepath\"])\n\n🧪 Testing\n\nRun the built-in test suite:\n\npython ai_agent_tools.py\n\n\nExpected output:\n\n=== AI Ajanları İçin Araçlar Kütüphanesi ===\n\n1. Dosya Araçları:\n Okunan içerik: Merhaba AI Ajanı!\n\n2. Metin Araçları:\n Bulunan emailler: ['ali@example.com']\n Bulunan telefonlar: ['0532 123 45 67']\n\n3. Veri Araçları:\n CSV çıktısı:\n isim,yaş\n Ali,25\n Ayşe,30\n\n...\n\n✓ Tüm araçlar test edildi!\n\n🤝 Contributing\n\nContributions are welcome! To contribute:\n\nFork the repository\nCreate a feature branch: git checkout -b feature/new-tool\nCommit your changes: git commit -am 'Add new tool'\nPush to the branch: git push origin feature/new-tool\nSubmit a pull request\n📝 License\n\nThis project is open source and available under the MIT License.\n\n👤 Author\n\nGitHub: @cerbug45\n\n🐛 Issues & Support\n\nFound a bug or need help? Please open an issue on GitHub: https://github.com/cerbug45/ai-agent-tools/issues\n\n📚 Additional Resources\nPython Documentation\nRegular Expressions Guide\nJSON Format Specification\n🔄 Version History\nv1.0.0 (2026-02-15)\nInitial release\n6 tool categories\n25+ utility functions\nFull documentation\nTest suite included\n\nHappy Coding! 🚀" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/cerbug45/ai-agent-tools", "publisherUrl": "https://clawhub.ai/cerbug45/ai-agent-tools", "owner": "cerbug45", "version": "0.1.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/ai-agent-tools", "downloadUrl": "https://openagent3.xyz/downloads/ai-agent-tools", "agentUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent", "manifestUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent.json", "briefUrl": "https://openagent3.xyz/skills/ai-agent-tools/agent.md" } }