try callouts

docs/_config.yml

@@ -29,7 +29,7 @@ callouts:
   note:
     title: Note
     color: blue
-  best_practice:
+  best-practice:
     title: Best Practice
     color: green
 

docs/agent.md

@@ -15,7 +15,7 @@ To express an agent, create a Node (the agent) with [branching](./flow.md) to ot
 > 1. **Context Management:** Provide *clear, relevant context* so agents can understand the problem.E.g., Rather than dumping an entire chat history or entire files, use a [Workflow](./decomp.md) that filters out and includes only the most relevant information.
 >
 > 2. **Action Space:** Define *a well-structured, unambiguous, and easy-to-use* set of actions. For instance, avoid creating overlapping actions like `read_databases` and `read_csvs`. Instead, unify data sources (e.g., move CSVs into a database) and design a single action. The action can be parameterized (e.g., string for search) or  programmable (e.g., SQL queries).
-{: .best_practice }
+{: .best-practice }
 
 ### Example: Search Agent
 

docs/communication.md

@@ -23,7 +23,7 @@ Nodes and Flows **communicate** in two ways:
 If you know memory management, think of the **Shared Store** like a **heap** (shared by all function calls), and **Params** like a **stack** (assigned by the caller).
 
 > Use `Shared Store` for almost all cases. It's flexible and easy to manage. It separates *Data Schema* from *Compute Logic*, making the code easier to maintain. `Params` is more a syntax sugar for [Batch](./batch.md).
-{: .best_practice }
+{: .best-practice }
 
 ---
 

docs/decomp.md

@@ -14,7 +14,7 @@ Many real-world tasks are too complex for one LLM call. The solution is to decom
 > - You don't want to make each task **too granular**, because then *the LLM call doesn't have enough context* and results are *not consistent across nodes*.
 > 
 > You usually need multiple *iterations* to find the *sweet spot*. If the task has too many *edge cases*, consider using [Agents](./agent.md).
-{: .best_practice }
+{: .best-practice }
 
 ### Example: Article Writing
 

docs/multi_agent.md

@@ -11,7 +11,7 @@ Multiple [Agents](./flow.md) can work together by handling subtasks and communic
 Communication between agents is typically implemented using message queues in shared storage.
 
 > Most of time, you don't need Multi-Agents. Start with a simple solution first.
-{: .best_practice }
+{: .best-practice }
 
 ### Example Agent Communication: Message Queue