skill-seekers-reference/AGENTS.md

AGENTS.md - Skill Seekers

This file provides essential guidance for AI coding agents working with the Skill Seekers codebase.

---

Project Overview

Skill Seekers is a Python CLI tool that converts documentation websites, GitHub repositories, and PDF files into AI-ready skills for LLM platforms and RAG (Retrieval-Augmented Generation) pipelines. It serves as the universal preprocessing layer for AI systems.

Key Facts

Attribute	Value
Current Version	3.0.0
Python Version	3.10+ (tested on 3.10, 3.11, 3.12, 3.13)
License	MIT
Package Name	skill-seekers (PyPI)
Website	https://skillseekersweb.com/
Repository	https://github.com/yusufkaraaslan/Skill_Seekers

Supported Target Platforms

Platform	Format	Use Case
Claude AI	ZIP + YAML	Claude Code skills
Google Gemini	tar.gz	Gemini skills
OpenAI ChatGPT	ZIP + Vector Store	Custom GPTs
LangChain	Documents	QA chains, agents, retrievers
LlamaIndex	TextNodes	Query engines, chat engines
Haystack	Documents	Enterprise RAG pipelines
Pinecone	Ready for upsert	Production vector search
Weaviate	Vector objects	Vector database
Qdrant	Points	Vector database
Chroma	Documents	Local vector database
FAISS	Index files	Local similarity search
Cursor IDE	.cursorrules	AI coding assistant rules
Windsurf	.windsurfrules	AI coding rules
Cline	.clinerules + MCP	VS Code extension
Continue.dev	HTTP context	Universal IDE support
Generic Markdown	ZIP	Universal export

Core Workflow

1. Scrape Phase - Crawl documentation/GitHub/PDF sources
2. Build Phase - Organize content into categorized references
3. Enhancement Phase - AI-powered quality improvements (optional)
4. Package Phase - Create platform-specific packages
5. Upload Phase - Auto-upload to target platform (optional)

---

Project Structure

/mnt/1ece809a-2821-4f10-aecb-fcdf34760c0b/Git/Skill_Seekers/
├── src/skill_seekers/              # Main source code (src/ layout)
│   ├── cli/                        # CLI tools and commands (70+ modules, ~40k lines)
│   │   ├── adaptors/               # Platform adaptors (Strategy pattern)
│   │   │   ├── base.py             # Abstract base class
│   │   │   ├── claude.py           # Claude AI adaptor
│   │   │   ├── gemini.py           # Google Gemini adaptor
│   │   │   ├── openai.py           # OpenAI ChatGPT adaptor
│   │   │   ├── markdown.py         # Generic Markdown adaptor
│   │   │   ├── chroma.py           # Chroma vector DB adaptor
│   │   │   ├── faiss_helpers.py    # FAISS index adaptor
│   │   │   ├── haystack.py         # Haystack RAG adaptor
│   │   │   ├── langchain.py        # LangChain adaptor
│   │   │   ├── llama_index.py      # LlamaIndex adaptor
│   │   │   ├── qdrant.py           # Qdrant vector DB adaptor
│   │   │   ├── weaviate.py         # Weaviate vector DB adaptor
│   │   │   └── streaming_adaptor.py # Streaming output adaptor
│   │   ├── storage/                # Cloud storage backends
│   │   │   ├── base_storage.py     # Storage interface
│   │   │   ├── s3_storage.py       # AWS S3 support
│   │   │   ├── gcs_storage.py      # Google Cloud Storage
│   │   │   └── azure_storage.py    # Azure Blob Storage
│   │   ├── parsers/                # CLI argument parsers
│   │   ├── arguments/              # CLI argument definitions
│   │   ├── presets/                # Preset configuration management
│   │   ├── main.py                 # Unified CLI entry point
│   │   ├── create_command.py       # NEW: Unified create command
│   │   ├── doc_scraper.py          # Documentation scraper
│   │   ├── github_scraper.py       # GitHub repository scraper
│   │   ├── pdf_scraper.py          # PDF extraction
│   │   ├── unified_scraper.py      # Multi-source scraping
│   │   ├── codebase_scraper.py     # Local codebase analysis
│   │   ├── enhance_skill_local.py  # AI enhancement (local mode)
│   │   ├── package_skill.py        # Skill packager
│   │   ├── upload_skill.py         # Upload to platforms
│   │   ├── cloud_storage_cli.py    # Cloud storage CLI
│   │   ├── benchmark_cli.py        # Benchmarking CLI
│   │   ├── sync_cli.py             # Sync monitoring CLI
│   │   └── ...                     # Additional CLI modules
│   ├── mcp/                        # MCP server integration
│   │   ├── server_fastmcp.py       # FastMCP server (main, ~708 lines)
│   │   ├── server_legacy.py        # Legacy server implementation
│   │   ├── server.py               # Server entry point
│   │   ├── agent_detector.py       # AI agent detection
│   │   ├── git_repo.py             # Git repository operations
│   │   ├── source_manager.py       # Config source management
│   │   └── tools/                  # MCP tool implementations
│   │       ├── config_tools.py     # Configuration tools
│   │       ├── scraping_tools.py   # Scraping tools
│   │       ├── packaging_tools.py  # Packaging tools
│   │       ├── source_tools.py     # Source management tools
│   │       ├── splitting_tools.py  # Config splitting tools
│   │       └── vector_db_tools.py  # Vector database tools
│   ├── sync/                       # Sync monitoring module
│   │   ├── detector.py             # Change detection
│   │   ├── models.py               # Data models (Pydantic)
│   │   ├── monitor.py              # Monitoring logic
│   │   └── notifier.py             # Notification system
│   ├── benchmark/                  # Benchmarking framework
│   │   ├── framework.py            # Benchmark framework
│   │   ├── models.py               # Benchmark models
│   │   └── runner.py               # Benchmark runner
│   ├── embedding/                  # Embedding server
│   │   ├── server.py               # FastAPI embedding server
│   │   ├── generator.py            # Embedding generation
│   │   ├── cache.py                # Embedding cache
│   │   └── models.py               # Embedding models
│   ├── _version.py                 # Version information (reads from pyproject.toml)
│   └── __init__.py                 # Package init
├── tests/                          # Test suite (94 test files)
├── configs/                        # Preset configuration files
├── docs/                           # Documentation (80+ markdown files)
│   ├── integrations/               # Platform integration guides
│   ├── guides/                     # User guides
│   ├── reference/                  # API reference
│   ├── features/                   # Feature documentation
│   ├── blog/                       # Blog posts
│   └── roadmap/                    # Roadmap documents
├── examples/                       # Usage examples
│   ├── langchain-rag-pipeline/     # LangChain example
│   ├── llama-index-query-engine/   # LlamaIndex example
│   ├── pinecone-upsert/            # Pinecone example
│   ├── chroma-example/             # Chroma example
│   ├── weaviate-example/           # Weaviate example
│   ├── qdrant-example/             # Qdrant example
│   ├── faiss-example/              # FAISS example
│   ├── haystack-pipeline/          # Haystack example
│   ├── cursor-react-skill/         # Cursor IDE example
│   ├── windsurf-fastapi-context/   # Windsurf example
│   └── continue-dev-universal/     # Continue.dev example
├── .github/workflows/              # CI/CD workflows
├── pyproject.toml                  # Main project configuration
├── requirements.txt                # Pinned dependencies
├── mypy.ini                        # MyPy type checker configuration
├── Dockerfile                      # Main Docker image (multi-stage)
├── Dockerfile.mcp                  # MCP server Docker image
└── docker-compose.yml              # Full stack deployment

---

Build and Development Commands

Prerequisites

• Python 3.10 or higher
• pip or uv package manager
• Git (for GitHub scraping features)

Setup (REQUIRED before any development)

# Install in editable mode (REQUIRED for tests due to src/ layout)
pip install -e .

# Install with all platform dependencies
pip install -e ".[all-llms]"

# Install with all optional dependencies
pip install -e ".[all]"

# Install specific platforms only
pip install -e ".[gemini]"    # Google Gemini support
pip install -e ".[openai]"    # OpenAI ChatGPT support
pip install -e ".[mcp]"       # MCP server dependencies
pip install -e ".[s3]"        # AWS S3 support
pip install -e ".[gcs]"       # Google Cloud Storage
pip install -e ".[azure]"     # Azure Blob Storage
pip install -e ".[embedding]" # Embedding server support
pip install -e ".[rag-upload]" # Vector DB upload support

# Install dev dependencies (using dependency-groups)
pip install -e ".[dev]"

CRITICAL: The project uses a src/ layout. Tests WILL FAIL unless you install with pip install -e . first.

Building

# Build package using uv (recommended)
uv build

# Or using standard build
python -m build

# Publish to PyPI
uv publish

Docker

# Build Docker image
docker build -t skill-seekers .

# Run with docker-compose (includes vector databases)
docker-compose up -d

# Run MCP server only
docker-compose up -d mcp-server

# View logs
docker-compose logs -f mcp-server

---

Testing Instructions

Running Tests

CRITICAL: Never skip tests - all tests must pass before commits.

# All tests (must run pip install -e . first!)
pytest tests/ -v

# Specific test file
pytest tests/test_scraper_features.py -v
pytest tests/test_mcp_fastmcp.py -v
pytest tests/test_cloud_storage.py -v

# With coverage
pytest tests/ --cov=src/skill_seekers --cov-report=term --cov-report=html

# Single test
pytest tests/test_scraper_features.py::test_detect_language -v

# E2E tests
pytest tests/test_e2e_three_stream_pipeline.py -v

# Skip slow tests
pytest tests/ -v -m "not slow"

# Run only integration tests
pytest tests/ -v -m integration

# Run only specific marker
pytest tests/ -v -m "not slow and not integration"

Test Architecture

• 94 test files covering all features
• 1200+ tests passing
• CI Matrix: Ubuntu + macOS, Python 3.10-3.12
• Test markers defined in pyproject.toml:

Marker	Description
slow	Tests taking >5 seconds
integration	Requires external services (APIs)
e2e	End-to-end tests (resource-intensive)
venv	Requires virtual environment setup
bootstrap	Bootstrap skill specific
benchmark	Performance benchmark tests

Test Configuration

From pyproject.toml:

[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
addopts = "-v --tb=short --strict-markers"
asyncio_mode = "auto"
asyncio_default_fixture_loop_scope = "function"

The conftest.py file checks that the package is installed before running tests.

---

Code Style Guidelines

Linting and Formatting

# Run ruff linter
ruff check src/ tests/

# Run ruff formatter check
ruff format --check src/ tests/

# Auto-fix issues
ruff check src/ tests/ --fix
ruff format src/ tests/

# Run mypy type checker
mypy src/skill_seekers --show-error-codes --pretty

Style Rules (from pyproject.toml)

• Line length: 100 characters
• Target Python: 3.10+
• Enabled rules: E, W, F, I, B, C4, UP, ARG, SIM
• Ignored rules: E501, F541, ARG002, B007, I001, SIM114
• Import sorting: isort style with skill_seekers as first-party

MyPy Configuration (from mypy.ini)

[mypy]
python_version = 3.10
warn_return_any = False
warn_unused_configs = True
disallow_untyped_defs = False
check_untyped_defs = True
ignore_missing_imports = True
no_implicit_optional = True
show_error_codes = True

# Gradual typing - be lenient for now
disallow_incomplete_defs = False
disallow_untyped_calls = False

Code Conventions

1. Use type hints where practical (gradual typing approach)
2. Docstrings: Use Google-style or standard docstrings
3. Error handling: Use specific exceptions, provide helpful messages
4. Async code: Use asyncio, mark tests with @pytest.mark.asyncio
5. File naming: Use snake_case for all Python files
6. Class naming: Use PascalCase for classes
7. Function naming: Use snake_case for functions and methods
8. Constants: Use UPPER_CASE for module-level constants

---

Architecture Patterns

Platform Adaptor Pattern (Strategy Pattern)

All platform-specific logic is encapsulated in adaptors:

from skill_seekers.cli.adaptors import get_adaptor

# Get platform-specific adaptor
adaptor = get_adaptor('gemini')  # or 'claude', 'openai', 'langchain', etc.

# Package skill
adaptor.package(skill_dir='output/react/', output_path='output/')

# Upload to platform
adaptor.upload(
    package_path='output/react-gemini.tar.gz',
    api_key=os.getenv('GOOGLE_API_KEY')
)

Each adaptor inherits from SkillAdaptor base class and implements:

• format_skill_md() - Format SKILL.md content
• package() - Create platform-specific package
• upload() - Upload to platform API
• validate_api_key() - Validate API key format
• supports_enhancement() - Whether AI enhancement is supported

CLI Architecture (Git-style)

Entry point: src/skill_seekers/cli/main.py

The CLI uses subcommands that delegate to existing modules:

# skill-seekers scrape --config react.json
# Transforms to: doc_scraper.main() with modified sys.argv

Available subcommands:

• create - NEW: Unified create command
• config - Configuration wizard
• scrape - Documentation scraping
• github - GitHub repository scraping
• pdf - PDF extraction
• unified - Multi-source scraping
• analyze / codebase - Local codebase analysis
• enhance - AI enhancement
• package - Package skill for target platform
• upload - Upload to platform
• cloud - Cloud storage operations
• sync - Sync monitoring
• benchmark - Performance benchmarking
• embed - Embedding server
• install / install-agent - Complete workflow
• stream - Streaming ingestion
• update - Incremental updates
• multilang - Multi-language support
• quality - Quality metrics
• resume - Resume interrupted jobs
• estimate - Estimate page counts

MCP Server Architecture

Two implementations:

• server_fastmcp.py - Modern, decorator-based (recommended, ~708 lines)
• server_legacy.py - Legacy implementation

Tools are organized by category:

• Config tools (3 tools): generate_config, list_configs, validate_config
• Scraping tools (8 tools): estimate_pages, scrape_docs, scrape_github, scrape_pdf, scrape_codebase, detect_patterns, extract_test_examples, build_how_to_guides
• Packaging tools (4 tools): package_skill, upload_skill, enhance_skill, install_skill
• Source tools (5 tools): fetch_config, submit_config, add_config_source, list_config_sources, remove_config_source
• Splitting tools (2 tools): split_config, generate_router
• Vector Database tools (4 tools): export_to_weaviate, export_to_chroma, export_to_faiss, export_to_qdrant

Running MCP Server:

# Stdio transport (default)
python -m skill_seekers.mcp.server_fastmcp

# HTTP transport
python -m skill_seekers.mcp.server_fastmcp --http --port 8765

Cloud Storage Architecture

Abstract base class pattern for cloud providers:

• base_storage.py - Defines BaseStorageAdaptor interface
• s3_storage.py - AWS S3 implementation
• gcs_storage.py - Google Cloud Storage implementation
• azure_storage.py - Azure Blob Storage implementation

Sync Monitoring Architecture

Pydantic-based models in src/skill_seekers/sync/:

• models.py - Data models (SyncConfig, ChangeReport, SyncState)
• detector.py - Change detection logic
• monitor.py - Monitoring daemon
• notifier.py - Notification system (webhook, email, slack)

---

Git Workflow

Branch Structure

main (production)
  ↑
  │ (only maintainer merges)
  │
development (integration) ← default branch for PRs
  ↑
  │ (all contributor PRs go here)
  │
feature branches

• main - Production, always stable, protected
• development - Active development, default for PRs
• Feature branches - Your work, created from development

Creating a Feature Branch

# 1. Checkout development
git checkout development
git pull upstream development

# 2. Create feature branch
git checkout -b my-feature

# 3. Make changes, commit, push
git add .
git commit -m "Add my feature"
git push origin my-feature

# 4. Create PR targeting 'development' branch

---

CI/CD Configuration

GitHub Actions Workflows

All workflows are in .github/workflows/:

tests.yml:

• Runs on: push/PR to main and development
• Lint job: Ruff + MyPy
• Test matrix: Ubuntu + macOS, Python 3.10-3.12
• Coverage: Uploads to Codecov

release.yml:

• Triggered on version tags (v*)
• Builds and publishes to PyPI using uv
• Creates GitHub release with changelog

docker-publish.yml:

• Builds and publishes Docker images

vector-db-export.yml:

• Tests vector database exports

scheduled-updates.yml:

• Scheduled sync monitoring

quality-metrics.yml:

• Quality metrics tracking

test-vector-dbs.yml:

• Vector database integration tests

Pre-commit Checks (Manual)

# Before committing, run:
ruff check src/ tests/
ruff format --check src/ tests/
pytest tests/ -v -x  # Stop on first failure

---

Security Considerations

API Keys and Secrets

1. Never commit API keys to the repository
2. Use environment variables:
   • ANTHROPIC_API_KEY - Claude AI
   • GOOGLE_API_KEY - Google Gemini
   • OPENAI_API_KEY - OpenAI
   • GITHUB_TOKEN - GitHub API
   • AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY - AWS S3
   • GOOGLE_APPLICATION_CREDENTIALS - GCS
   • AZURE_STORAGE_CONNECTION_STRING - Azure
3. Configuration storage:
   • Stored at ~/.config/skill-seekers/config.json
   • Permissions: 600 (owner read/write only)

Rate Limit Handling

• GitHub API has rate limits (5000 requests/hour for authenticated)
• The tool has built-in rate limit handling with retry logic
• Use --non-interactive flag for CI/CD environments

Custom API Endpoints

Support for Claude-compatible APIs:

export ANTHROPIC_API_KEY=your-custom-api-key
export ANTHROPIC_BASE_URL=https://custom-endpoint.com/v1

---

Common Development Tasks

Adding a New CLI Command

1. Create module in src/skill_seekers/cli/my_command.py
2. Implement main() function with argument parsing
3. Add entry point in pyproject.toml:

   [project.scripts]
   skill-seekers-my-command = "skill_seekers.cli.my_command:main"
   

4. Add subcommand handler in src/skill_seekers/cli/main.py
5. Add argument parser in src/skill_seekers/cli/parsers/
6. Add tests in tests/test_my_command.py

Adding a New Platform Adaptor

1. Create src/skill_seekers/cli/adaptors/my_platform.py
2. Inherit from SkillAdaptor base class
3. Implement required methods: package(), upload(), format_skill_md()
4. Register in src/skill_seekers/cli/adaptors/__init__.py
5. Add optional dependencies in pyproject.toml
6. Add tests in tests/test_adaptors/

Adding an MCP Tool

1. Implement tool logic in src/skill_seekers/mcp/tools/category_tools.py
2. Register in src/skill_seekers/mcp/server_fastmcp.py
3. Add test in tests/test_mcp_fastmcp.py

Adding Cloud Storage Provider

1. Create module in src/skill_seekers/cli/storage/my_storage.py
2. Inherit from BaseStorageAdaptor base class
3. Implement required methods: upload_file(), download_file(), list_files(), delete_file()
4. Register in src/skill_seekers/cli/storage/__init__.py
5. Add optional dependencies in pyproject.toml

---

Documentation

Project Documentation

• README.md - Main project documentation
• README.zh-CN.md - Chinese translation
• CLAUDE.md - Detailed implementation guidance
• QUICKSTART.md - Quick start guide
• CONTRIBUTING.md - Contribution guidelines
• TROUBLESHOOTING.md - Common issues and solutions
• AGENTS.md - This file, for AI coding agents
• docs/ - Comprehensive documentation (80+ files)
  • docs/integrations/ - Integration guides for each platform
  • docs/guides/ - User guides
  • docs/reference/ - API reference
  • docs/features/ - Feature documentation
  • docs/blog/ - Blog posts and articles
  • docs/roadmap/ - Roadmap documents

Configuration Documentation

Preset configs are in configs/ directory:

• godot.json - Godot Engine
• blender.json / blender-unified.json - Blender Engine
• claude-code.json - Claude Code
• httpx_comprehensive.json - HTTPX library
• medusa-mercurjs.json - Medusa/MercurJS
• astrovalley_unified.json - Astrovalley
• configs/integrations/ - Integration-specific configs

---

Key Dependencies

Core Dependencies (Required)

Package	Version	Purpose
requests	>=2.32.5	HTTP requests
beautifulsoup4	>=4.14.2	HTML parsing
PyGithub	>=2.5.0	GitHub API
GitPython	>=3.1.40	Git operations
httpx	>=0.28.1	Async HTTP
anthropic	>=0.76.0	Claude AI API
PyMuPDF	>=1.24.14	PDF processing
Pillow	>=11.0.0	Image processing
pytesseract	>=0.3.13	OCR
pydantic	>=2.12.3	Data validation
pydantic-settings	>=2.11.0	Settings management
click	>=8.3.0	CLI framework
Pygments	>=2.19.2	Syntax highlighting
pathspec	>=0.12.1	Path matching
networkx	>=3.0	Graph operations
schedule	>=1.2.0	Scheduled tasks
python-dotenv	>=1.1.1	Environment variables
jsonschema	>=4.25.1	JSON validation

Optional Dependencies

Feature	Package	Install Command
MCP Server	mcp>=1.25,<2	pip install -e ".[mcp]"
Google Gemini	google-generativeai>=0.8.0	pip install -e ".[gemini]"
OpenAI	openai>=1.0.0	pip install -e ".[openai]"
AWS S3	boto3>=1.34.0	pip install -e ".[s3]"
Google Cloud Storage	google-cloud-storage>=2.10.0	pip install -e ".[gcs]"
Azure Blob Storage	azure-storage-blob>=12.19.0	pip install -e ".[azure]"
Chroma DB	chromadb>=0.4.0	pip install -e ".[chroma]"
Weaviate	weaviate-client>=3.25.0	pip install -e ".[weaviate]"
Embedding Server	fastapi>=0.109.0, uvicorn>=0.27.0, sentence-transformers>=2.3.0	pip install -e ".[embedding]"

Dev Dependencies (in dependency-groups)

Package	Version	Purpose
pytest	>=8.4.2	Testing framework
pytest-asyncio	>=0.24.0	Async test support
pytest-cov	>=7.0.0	Coverage
coverage	>=7.11.0	Coverage reporting
ruff	>=0.14.13	Linting/formatting
mypy	>=1.19.1	Type checking
psutil	>=5.9.0	Process utilities for testing
numpy	>=1.24.0	Numerical operations
starlette	>=0.31.0	HTTP transport testing
boto3	>=1.26.0	AWS S3 testing
google-cloud-storage	>=2.10.0	GCS testing
azure-storage-blob	>=12.17.0	Azure testing

---

Troubleshooting

Common Issues

ImportError: No module named 'skill_seekers'

• Solution: Run pip install -e .

Tests failing with "package not installed"

• Solution: Ensure you ran pip install -e . in the correct virtual environment

MCP server import errors

• Solution: Install with pip install -e ".[mcp]"

Type checking failures

• MyPy is configured to be lenient (gradual typing)
• Focus on critical paths, not full coverage

Docker build failures

• Ensure you have BuildKit enabled: DOCKER_BUILDKIT=1
• Check that all submodules are initialized: git submodule update --init

Rate limit errors from GitHub

• Set GITHUB_TOKEN environment variable for authenticated requests
• Improves rate limit from 60 to 5000 requests/hour

Getting Help

• Check TROUBLESHOOTING.md for detailed solutions
• Review docs/FAQ.md for common questions
• Visit https://skillseekersweb.com/ for documentation
• Open an issue on GitHub with:
  • Clear title and description
  • Steps to reproduce
  • Expected vs actual behavior
  • Environment details (OS, Python version)
  • Error messages and stack traces

---

Environment Variables Reference

Variable	Purpose	Required For
ANTHROPIC_API_KEY	Claude AI API access	Claude enhancement/upload
GOOGLE_API_KEY	Google Gemini API access	Gemini enhancement/upload
OPENAI_API_KEY	OpenAI API access	OpenAI enhancement/upload
GITHUB_TOKEN	GitHub API authentication	GitHub scraping (recommended)
AWS_ACCESS_KEY_ID	AWS S3 authentication	S3 cloud storage
AWS_SECRET_ACCESS_KEY	AWS S3 authentication	S3 cloud storage
GOOGLE_APPLICATION_CREDENTIALS	GCS authentication path	GCS cloud storage
AZURE_STORAGE_CONNECTION_STRING	Azure Blob authentication	Azure cloud storage
ANTHROPIC_BASE_URL	Custom Claude endpoint	Custom API endpoints
SKILL_SEEKERS_HOME	Data directory path	Docker/runtime
SKILL_SEEKERS_OUTPUT	Output directory path	Docker/runtime

---

Version Management

The version is defined in pyproject.toml and dynamically read by src/skill_seekers/_version.py:

# _version.py reads from pyproject.toml
__version__ = get_version()  # Returns version from pyproject.toml

To update version:

1. Edit version in pyproject.toml
2. The _version.py file will automatically pick up the new version

---

This document is maintained for AI coding agents. For human contributors, see README.md and CONTRIBUTING.md.

Last updated: 2026-02-15