diff --git a/mle/agents/summarizer.py b/mle/agents/summarizer.py
index 5b2abe7..551a2eb 100644
--- a/mle/agents/summarizer.py
+++ b/mle/agents/summarizer.py
@@ -7,7 +7,7 @@
 
 class SummaryAgent:
 
-    def __init__(self, model, github_repo: str, username: str, github_token: str = None, console=None):
+    def __init__(self, model, github_repo: str = None, username: str = None, github_token: str = None, console=None):
         """
         SummaryAgent: summary the workspace provided by the user.
 
@@ -133,3 +133,25 @@ def summarize(self):
             summary.update({"user_activity": user_activity})
 
         return summary
+
+    def kaggle_request_summarize(self, kaggle_overview):
+        """
+        Summarize the kaggle requests.
+        :params: kaggle_overview: the overview json of kaggle competition
+        """
+        system_prompt = """
+        You are a seasoned data science expert in Kaggle competitions. Your task is to summarize the
+        requirements of a specific Kaggle competition in a clear and concise manner. Please ensure that
+        your summary includes the following aspects:
+
+        1. **Overview**: Describe the competition's objective and significance.
+        2. **Data**: Detail the datasets, including file types, structure, and key features.
+        3. **Evaluation**: Explain the judging metric and its calculation.
+        4. **Submission**: Outline the format and requirements for submissions.
+        5. **Rules**: Highlight important rules, including data usage, team composition, and resources.
+        """
+        chat_history = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": str(kaggle_overview)}
+        ]
+        return self.model.query(chat_history)
diff --git a/mle/workflow/kaggle.py b/mle/workflow/kaggle.py
index f342f94..e9e2d6e 100644
--- a/mle/workflow/kaggle.py
+++ b/mle/workflow/kaggle.py
@@ -6,7 +6,7 @@
 from rich.console import Console
 from mle.model import load_model
 from mle.utils import ask_text, WorkflowCache
-from mle.agents import CodeAgent, DebugAgent, AdviseAgent, PlanAgent
+from mle.agents import CodeAgent, DebugAgent, AdviseAgent, PlanAgent, SummaryAgent
 from mle.integration import KaggleIntegration
 
 
@@ -49,7 +49,8 @@ def kaggle(work_dir: str, model=None, kaggle_username=None, kaggle_token=None):
         if ml_requirement is None:
             with console.status("MLE Agent is fetching the kaggle competition overview..."):
                 overview = kaggle.get_competition_overview(competition)
-                ml_requirement =  f"Finish a kaggle competition: {overview}"
+                summary = SummaryAgent(model, console=console)
+                ml_requirement =  summary.kaggle_request_summarize(overview)
         ca.store("ml_requirement", ml_requirement)
 
     # advisor agent gives suggestions in a report
@@ -58,7 +59,7 @@ def kaggle(work_dir: str, model=None, kaggle_username=None, kaggle_token=None):
         if advisor_report is None:
             advisor = AdviseAgent(model, console)
             advisor_report = advisor.interact(
-                f"[green]User Requirement:[/green] {ml_requirement}\n"
+                f"[green]Competition Requirement:[/green] {ml_requirement}\n"
                 f"Dataset is downloaded in path: {dataset}"
             )
         ca.store("advisor_report", advisor_report)