feat: refactor GUI to multi-page Streamlit app with 9 tool pages

Convert single-page deduplicator into a multi-page suite. Home page shows
tool card grid. Deduplicator extracted to its own page (fully working).
8 stub pages added for Text Cleaner, Format Standardizer, Missing Values,
Column Mapper, Outlier Detector, Multi-File Merger, Validator & Reporter,
and Pipeline Runner — each with functional file upload and coming-soon UI.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/gui/pages/6_Outlier_Detector.py

@@ -0,0 +1,88 @@
+"""DataTools Outlier Detector — stub page."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import streamlit as st
+
+_project_root = Path(__file__).resolve().parent.parent.parent.parent
+if str(_project_root) not in sys.path:
+    sys.path.insert(0, str(_project_root))
+
+# ---------------------------------------------------------------------------
+# Header
+# ---------------------------------------------------------------------------
+
+st.title("📊 Outlier Detector")
+st.caption("Detect and handle outliers in numeric columns.")
+
+st.info("This tool is under development.")
+
+# ---------------------------------------------------------------------------
+# What this tool will do
+# ---------------------------------------------------------------------------
+
+st.markdown("""
+**Features:**
+- Z-score detection (configurable threshold)
+- IQR (interquartile range) detection
+- MAD (median absolute deviation) detection
+- Domain-rule violations (e.g., age < 0, price > $1M)
+- Visual outlier highlighting in data preview
+- Handling: flag only, remove, cap/winsorize to bounds
+""")
+
+st.divider()
+
+# ---------------------------------------------------------------------------
+# File upload (functional)
+# ---------------------------------------------------------------------------
+
+uploaded = st.file_uploader(
+    "Upload CSV or Excel file",
+    type=["csv", "tsv", "xlsx", "xls"],
+    help="Upload a file to preview. Processing is not yet available.",
+    key="outlier_file_upload",
+)
+
+if uploaded is not None:
+    import pandas as pd
+    try:
+        if uploaded.name.endswith((".xlsx", ".xls")):
+            df = pd.read_excel(uploaded)
+        else:
+            df = pd.read_csv(uploaded)
+        st.subheader(f"Preview: {uploaded.name}")
+        st.caption(f"{len(df)} rows, {len(df.columns)} columns")
+        st.dataframe(df.head(10), use_container_width=True)
+    except Exception as e:
+        st.error(f"Failed to read file: {e}")
+
+# ---------------------------------------------------------------------------
+# Placeholder options
+# ---------------------------------------------------------------------------
+
+st.subheader("Detection Method")
+
+st.selectbox("Method", ["Z-Score", "IQR (Interquartile Range)", "MAD (Median Absolute Deviation)"], disabled=True)
+st.slider("Z-Score threshold", 1.0, 5.0, 3.0, 0.1, disabled=True)
+st.slider("IQR multiplier", 1.0, 3.0, 1.5, 0.1, disabled=True)
+
+st.subheader("Handling")
+
+st.selectbox("Action", ["Flag only (add column)", "Remove outlier rows", "Cap / Winsorize to bounds"], disabled=True)
+
+st.divider()
+st.button("Detect Outliers", type="primary", use_container_width=True, disabled=True)
+
+# ---------------------------------------------------------------------------
+# Footer
+# ---------------------------------------------------------------------------
+
+st.divider()
+st.caption(
+    "Runs locally. Your data never leaves this computer. "
+    "| DataTools v3.0"
+)