diff --git a/docs/Cyclomatic-Complexity-Analysis.md b/docs/Cyclomatic-Complexity-Analysis.md
index 0d32497..8cee9c8 100644
--- a/docs/Cyclomatic-Complexity-Analysis.md
+++ b/docs/Cyclomatic-Complexity-Analysis.md
@@ -49,3 +49,16 @@ The calculator counts the following complexity factors:
 4. **Extract conditions**: Move complex conditions to separate methods
 5. **Use strategy pattern**: Replace complex switch statements
 6. **Limit logical operators**: Avoid deeply nested AND/OR conditions
+
+## How Are Cyclomatic Metrics Used in This Tool?
+
+Cyclomatic complexity is calculated for each class and method in your codebase. The results are shown alongside other metrics to help you identify complex, hard-to-test, or risky code.
+
+To enable cyclomatic complexity in the output, set the following in your configuration file:
+
+```yaml
+cognitive:
+  showCyclomaticComplexity: true
+```
+
+When enabled, the tool will display cyclomatic complexity scores in the analysis report, allowing you to spot methods and classes that may need refactoring or additional testing.
diff --git a/docs/Halstead-Analysis.md b/docs/Halstead-Analysis.md
new file mode 100644
index 0000000..497985e
--- /dev/null
+++ b/docs/Halstead-Analysis.md
@@ -0,0 +1,53 @@
+# Halstead Complexity Analysis
+
+Halstead metrics are a set of software metrics introduced by Maurice Halstead to measure the complexity of code based on operators and operands. These metrics help estimate code maintainability, understandability, and potential error rates.
+
+## What are Halstead Metrics?
+
+Halstead metrics are calculated using the following quantities:
+
+- **n₁**: Number of distinct operators
+- **n₂**: Number of distinct operands
+- **N₁**: Total number of operators
+- **N₂**: Total number of operands
+
+From these, several derived metrics are calculated:
+
+| Metric         | Formula                                              | Description                                      |
+|----------------|-----------------------------------------------------|--------------------------------------------------|
+| Vocabulary     | n = n₁ + n₂                                         | Number of unique operators and operands          |
+| Length         | N = N₁ + N₂                                         | Total number of operators and operands           |
+| Volume         | V = N × log₂(n)                                     | Size of the implementation                       |
+| Difficulty     | D = (n₁ / 2) × (N₂ / n₂)                            | Effort required to understand the code           |
+| Effort         | E = D × V                                           | Mental effort to develop or maintain the code    |
+| Bugs           | B = V / 3000                                        | Estimated number of errors                       |
+| Time           | T = E / 18                                          | Estimated time to implement (seconds)            |
+
+## Why Use Halstead Metrics?
+
+- **Maintainability**: High Halstead volume or effort may indicate code that is hard to maintain.
+- **Understandability**: Difficulty and effort metrics help identify code that may be hard to understand.
+- **Error Prediction**: The bugs metric provides a rough estimate of potential defects.
+
+## How Are Halstead Metrics Used in This Tool?
+
+When enabled in the configuration, Halstead metrics are calculated for each class and method. The results can be displayed alongside other complexity metrics to give a more complete picture of code quality.
+
+To enable Halstead metrics in the output, set the following in your configuration file:
+
+```yaml
+cognitive:
+  showHalsteadComplexity: true
+```
+
+## Interpretation
+
+- **Low Volume/Effort**: Code is likely simple and easy to maintain.
+- **High Volume/Effort**: Consider refactoring; code may be hard to understand or error-prone.
+- **Bugs**: Use as a rough indicator, not an absolute prediction.
+
+## References
+
+- [Wikipedia: Halstead complexity measures](https://en.wikipedia.org/wiki/Halstead_complexity_measures)
+
+