diff --git a/tools/sutra_collector.py b/tools/sutra_collector.py
new file mode 100644
index 0000000..c4917d0
--- /dev/null
+++ b/tools/sutra_collector.py
@@ -0,0 +1,125 @@
+"""
+Sutra Collector — gathers Buddhist texts and metadata for a specific teacher.
+
+Orchestrates FoJin Bridge calls to collect:
+1. Teacher's KG entity and relations (lineage, school, texts)
+2. Core texts associated with the teacher
+3. Text content (selected juans)
+4. Dictionary terms related to the teacher's tradition
+"""
+
+import json
+import os
+from typing import Optional
+
+from fojin_bridge import FojinBridge, create_bridge
+
+
+def collect_teacher_data(
+    teacher_name: str,
+    tradition: Optional[str] = None,
+    bridge: Optional[FojinBridge] = None,
+) -> dict:
+    """Collect all available data about a teacher from FoJin."""
+    if bridge is None:
+        bridge = create_bridge()
+
+    result = {
+        "entity": None,
+        "lineage": [],
+        "texts": [],
+        "content_samples": [],
+        "terms": [],
+    }
+
+    # Step 1: Find teacher in KG
+    kg_results = bridge.search_kg_entities(teacher_name, entity_type="person")
+    if kg_results.get("results"):
+        entity = kg_results["results"][0]
+        result["entity"] = entity
+        detail = bridge.get_kg_entity(entity["id"])
+        result["entity"] = detail
+        for rel in detail.get("relations", []):
+            if rel["predicate"] in (
+                "teacher_of", "student_of", "lineage_holder",
+                "transmitted_to", "received_from",
+            ):
+                result["lineage"].append(rel)
+
+    # Step 2: Search for associated texts
+    text_results = bridge.search_texts(teacher_name, size=50)
+    if text_results.get("results"):
+        result["texts"] = text_results["results"]
+
+    # Step 3: Collect content samples from top texts
+    for text in result["texts"][:5]:
+        text_id = text.get("id")
+        if not text_id:
+            continue
+        try:
+            content = bridge.get_text_content(text_id, juan_num=1)
+            result["content_samples"].append({
+                "text_id": text_id,
+                "title": text.get("title_zh", ""),
+                "content": content.get("content", "")[:3000],
+            })
+        except Exception:
+            continue
+
+    # Step 4: Collect tradition-specific terms
+    if tradition:
+        tradition_terms = {
+            "汉传": ["净土", "禅", "般若", "菩提", "念佛"],
+            "南传": ["vipassana", "satipatthana", "anicca", "dukkha", "anatta"],
+            "藏传": ["菩提道次第", "空性", "菩提心", "止观", "三主要道"],
+        }
+        for term in tradition_terms.get(tradition, []):
+            dict_results = bridge.search_dictionary(term, size=5)
+            if dict_results.get("results"):
+                result["terms"].extend(dict_results["results"])
+
+    return result
+
+
+def collect_specific_texts(
+    cbeta_ids: list,
+    bridge: Optional[FojinBridge] = None,
+) -> list:
+    """Collect full content for specific texts by CBETA ID."""
+    if bridge is None:
+        bridge = create_bridge()
+
+    texts = []
+    id_map = bridge.lookup_cbeta_ids(",".join(cbeta_ids))
+
+    for cbeta_id, text_id in id_map.items():
+        if not text_id:
+            continue
+        text_meta = bridge.get_text(text_id)
+        juan_list = bridge.get_text_juans(text_id)
+
+        text_data = {
+            "cbeta_id": cbeta_id,
+            "text_id": text_id,
+            "title": text_meta.get("title_zh", ""),
+            "juans": [],
+        }
+
+        for juan in juan_list.get("juans", [])[:10]:
+            content = bridge.get_text_content(text_id, juan["juan_num"])
+            text_data["juans"].append({
+                "juan_num": juan["juan_num"],
+                "content": content.get("content", ""),
+            })
+
+        texts.append(text_data)
+
+    return texts
+
+
+def save_collected_data(data: dict, output_path: str) -> str:
+    """Save collected data to JSON file."""
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    with open(output_path, "w", encoding="utf-8") as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+    return os.path.abspath(output_path)