api-integration-1-api-availability-checking
Sub-skill of api-integration: 1. API Availability Checking (+2).
Best use case
api-integration-1-api-availability-checking is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of api-integration: 1. API Availability Checking (+2).
Teams using api-integration-1-api-availability-checking should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/1-api-availability-checking/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How api-integration-1-api-availability-checking Compares
| Feature / Agent | api-integration-1-api-availability-checking | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of api-integration: 1. API Availability Checking (+2).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 1. API Availability Checking (+2)
## 1. API Availability Checking
```python
def check_api_availability(api_type: str) -> tuple[bool, str]:
"""
Check if API is available and return status.
Args:
api_type: Type of API to check
Returns:
Tuple of (is_available, message)
Example:
>>> available, msg = check_api_availability('orcaflex')
>>> if available:
... print("OrcaFlex API is ready")
>>> else:
... print(f"Using mock: {msg}")
"""
if api_type == 'orcaflex':
try:
import OrcFxAPI
return True, "OrcaFlex API available"
except ImportError:
return False, "OrcaFlex not installed, using mock API"
elif api_type == 'aqwa':
# AQWA typically accessed via ANSYS Workbench
# Check if ANSYS is available
return False, "AQWA integration via ANSYS Workbench (mock mode)"
else:
return False, f"Unknown API type: {api_type}"
```
## 2. Configuration Management
```python
import yaml
from dataclasses import dataclass, asdict
@dataclass
class APIConfiguration:
"""Configuration for API integration."""
api_type: str
model_file: Optional[Path]
output_dir: Path
simulation_settings: dict
retry_settings: dict
def save_to_yaml(self, file_path: Path) -> None:
"""Save configuration to YAML file."""
with open(file_path, 'w') as f:
yaml.dump(asdict(self), f, default_flow_style=False)
@classmethod
def load_from_yaml(cls, file_path: Path) -> 'APIConfiguration':
"""Load configuration from YAML file."""
with open(file_path) as f:
data = yaml.safe_load(f)
# Convert Path strings back to Path objects
if 'model_file' in data and data['model_file']:
data['model_file'] = Path(data['model_file'])
data['output_dir'] = Path(data['output_dir'])
return cls(**data)
```
## 3. Logging and Monitoring
```python
import logging
from datetime import datetime
def setup_api_logging(
log_dir: Path,
api_type: str
) -> logging.Logger:
"""
Setup logging for API operations.
Args:
log_dir: Directory for log files
api_type: Type of API
Returns:
Configured logger
"""
log_dir.mkdir(parents=True, exist_ok=True)
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
log_file = log_dir / f"{api_type}_api_{timestamp}.log"
logger = logging.getLogger(f"{api_type}_api")
logger.setLevel(logging.DEBUG)
# File handler
fh = logging.FileHandler(log_file)
fh.setLevel(logging.DEBUG)
# Console handler
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# Formatter
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
fh.setFormatter(formatter)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
return logger
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