Black Holes Characterization Intermediate

# Black Holes Characterization Intermediate Skill

## Overview
This skill enables characterization in the domain of black-holes (astronomy). It represents intermediate-level expertise and is designed for production use in research, industry, and educational contexts.

## Description
Use this skill when you need to perform characterization operations related to black-holes. This includes tasks such as:
- detect signals
- detect signals
- model phenomena

The skill leverages image processing software and follows best practices established in the astronomy community.

## Trigger Conditions
This skill should be activated when:
1. The user explicitly requests characterization in the context of black-holes
2. The task requires intermediate-level understanding of astronomy principles
3. The output needs to be astronomical data
4. The work involves black-holes methodologies or techniques

## Key Capabilities
- **Domain Expertise**: Deep understanding of black-holes principles and methods
- **Practical Application**: Ability to apply characterization techniques to real-world problems
- **Quality Assurance**: Validation and verification of results using astronomy standards
- **Tool Proficiency**: Effective use of telescopes
- **Documentation**: Clear explanation of methods, assumptions, and limitations

## Usage Guidelines
1. **Input Requirements**: Clearly specify the problem parameters and constraints
2. **Methodology**: Follow established black-holes protocols and best practices
3. **Validation**: Verify results against known benchmarks or theoretical predictions
4. **Documentation**: Provide comprehensive explanations of all steps and decisions
5. **Iteration**: Refine approach based on intermediate results and feedback

## Output Format
The skill produces celestial coordinates in standardized formats appropriate for astronomy applications. Outputs include:
- Detailed technical analysis
- Numerical results with uncertainty quantification
- Visualizations and diagrams where appropriate
- References to relevant literature and methods
- Recommendations for further investigation

## Limitations
- Requires appropriate input data quality and completeness
- Results are subject to assumptions stated in the methodology
- May require validation through independent methods
- Complexity increases with problem scale and dimensionality
- Domain-specific constraints may limit applicability

## Related Skills
Consider combining this skill with:
- Adjacent black-holes skills for comprehensive analysis
- Complementary astronomy methodologies
- Cross-disciplinary approaches when applicable

## Best Practices
1. Always validate inputs before processing
2. Document all assumptions explicitly
3. Use appropriate error checking and handling
4. Compare results with theoretical expectations
5. Maintain reproducibility through clear documentation
6. Consider computational efficiency for large-scale problems
7. Stay current with black-holes literature and methods

## Version Information
- Complexity Level: intermediate
- Domain: astronomy
- Subdiscipline: black-holes
- Skill Type: characterization
- Last Updated: 2025