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+---
+name: brain-explain-alphas
+description: >-
+  Provides a step-by-step workflow for analyzing and explaining WorldQuant BRAIN alpha expressions.
+  Use this when the user asks to explain a specific alpha expression, what a datafield does, or how operators work together.
+  Includes steps for data field lookup, operator analysis, and external research.
+---
+
+# Alpha Explanation Workflow
+
+This manual provides a workflow for analyzing and explaining a WorldQuant BRAIN alpha expression.
+For the full detailed workflow and examples, see [reference.md](reference.md).
+
+## Step 1: Deconstruct the Alpha Expression
+Break down the alpha expression into its fundamental components: data fields and operators.
+*Example:* `quantile(ts_regression(oth423_find,group_mean(oth423_find,vec_max(shrt3_bar),country),90))`
+- **Data Fields**: `oth423_find`, `shrt3_bar`
+- **Operators**: `quantile`, `ts_regression`, `group_mean`, `vec_max`
+
+## Step 2: Analyze Data Fields
+Use the `get_datafields` tool to get details about each data field.
+- Identify: Instrument Type, Region, Delay, Universe, Data Type (Matrix/Vector).
+- Note: Vector data requires aggregation (e.g., `vec_max`).
+
+## Step 3: Understand the Operators
+Use the `get_operators` tool to understand what each operator does.
+
+## Step 4: Consult Official Documentation
+Use `get_documentations` and `read_specific_documentation` for deep dives into concepts (e.g., vector data handling).
+
+## Step 5: Synthesize and Explain
+Structure the explanation:
+1.  **Idea**: High-level summary of the strategy.
+2.  **Rationale for data**: Why these fields? What do they represent?
+3.  **Rationale for operators**: How do they transform the data?
+4.  **Further Inspiration**: Potential improvements.
+
+## Appendix: Vector Data
+Vector data records multiple events per day per instrument (e.g., news). It requires aggregation (like `vec_mean`, `vec_sum`) to become a matrix value usable by other operators.