feat: Update baseline calculations and LLM prompts

- Change baseline calculations to use integers instead of floats to simplify data.
- Update LLM constraints and prompt for more accurate anomaly detection.
- Refine known_issues to reduce false positives.
- Update PROGRESS.md with new TODO items.

monitor_agent.py

@@ -195,7 +195,8 @@ def build_llm_prompt(data, baselines, nmap_changes, constraints, known_issues):
     **Constraints and Guidelines:**
     {constraints}
 
-    **Output Request:** If you find an anomaly, provide a report as a single JSON object with two keys: "severity" and "reason". The "severity" must be one of "high", "medium", "low", or "none". The "reason" must be a natural language explanation of the anomaly. Please include specific values if the anomoly has them. If no anomaly is found, return a single JSON object with "severity" set to "none" and "reason" as an empty string. Do not wrap the JSON in markdown or any other formatting.
+    **Output Request:** If you find an anomaly, provide a report as a single JSON object with two keys: "severity" and "reason". The "severity" must be one of "high", "medium", "low", or "none". The "reason" must be a natural language explanation of the anomaly. Please include specific values if the anomoly has them. If no anomaly is found, return a single JSON object with "severity" set to "none" and "reason" as an empty string. Do not wrap the JSON in markdown or any other formatting. Only return the JSON, and nothing else.
+
 
     **Reasoning Hint:** Think step by step to come to your conclusion. This is very important.
     """