diff --git a/scripts/ingest_claude.py b/scripts/ingest_claude.py
new file mode 100644
index 0000000..4cf2245
--- /dev/null
+++ b/scripts/ingest_claude.py
@@ -0,0 +1,135 @@
+import json
+import sys
+from pathlib import Path
+from datetime import datetime
+from sentence_transformers import SentenceTransformer
+import chromadb
+
+# Paths
+db_path = str(Path.home() / "aaronai" / "db")
+EXPORT_DIR = "/home/aaron/nextcloud/data/data/aaron/files/Archive/Misc/Claude Export"
+
+print("Loading embedding model...")
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+client = chromadb.PersistentClient(path=db_path)
+collection = client.get_or_create_collection(
+    name="aaronai",
+    metadata={"hnsw:space": "cosine"}
+)
+
+def extract_messages(convo):
+    """Extract messages from a Claude conversation object."""
+    messages = []
+    for msg in convo.get("chat_messages", []):
+        role = msg.get("sender", "")
+        if role not in ["human", "assistant"]:
+            continue
+        # Claude export stores content as a list of content blocks
+        content = msg.get("content", [])
+        text = ""
+        if isinstance(content, str):
+            text = content
+        elif isinstance(content, list):
+            for block in content:
+                if isinstance(block, dict) and block.get("type") == "text":
+                    text += block.get("text", "")
+                elif isinstance(block, str):
+                    text += block
+        text = text.strip()
+        if not text:
+            continue
+        created_at = msg.get("created_at", "")
+        messages.append((created_at, role, text))
+    return messages
+
+def chunk_conversation(convo):
+    """Turn a conversation into indexable chunks."""
+    chunks = []
+    title = convo.get("name", "Untitled conversation")
+    uuid = convo.get("uuid", "")
+    created_at = convo.get("created_at", "")
+    messages = extract_messages(convo)
+    if not messages:
+        return chunks
+
+    # Chunk into sliding windows of 3 messages
+    window = []
+    for i, (ts, role, text) in enumerate(messages):
+        label = "You" if role == "human" else "Claude"
+        window.append(f"{label}: {text}")
+        if len(window) >= 3 or i == len(messages) - 1:
+            chunk_text = f"[Claude conversation: {title}]\n\n" + "\n\n".join(window)
+            chunk_id = f"claude_{uuid}_{i}"
+            chunks.append((chunk_id, chunk_text, {
+                "source": f"Claude: {title}",
+                "type": "claude_conversation",
+                "created_at": created_at,
+            }))
+            window = window[-1:]  # overlap by 1
+
+    return chunks
+
+def ingest_file(jsonl_path):
+    print(f"Processing {jsonl_path.name}...")
+    conversations = []
+    with open(jsonl_path, encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                conversations.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+
+    print(f"Found {len(conversations)} conversations")
+    total_chunks = 0
+    skipped = 0
+
+    for convo in conversations:
+        chunks = chunk_conversation(convo)
+        if not chunks:
+            skipped += 1
+            continue
+
+        ids = [c[0] for c in chunks]
+        texts = [c[1] for c in chunks]
+        metas = [c[2] for c in chunks]
+
+        # Check existing
+        existing = collection.get(ids=ids)
+        existing_ids = set(existing["ids"])
+        new = [(id, txt, meta) for id, txt, meta in zip(ids, texts, metas) if id not in existing_ids]
+
+        if not new:
+            continue
+
+        embeddings = embedder.encode([n[1] for n in new]).tolist()
+        collection.add(
+            ids=[n[0] for n in new],
+            documents=[n[1] for n in new],
+            metadatas=[n[2] for n in new],
+            embeddings=embeddings,
+        )
+        total_chunks += len(new)
+
+    print(f"Done. {total_chunks} chunks added, {skipped} conversations skipped.")
+    return total_chunks
+
+# Find the export file
+export_dir = Path(EXPORT_DIR)
+export_dir.mkdir(parents=True, exist_ok=True)
+
+jsonl_files = list(export_dir.glob("*.jsonl")) + list(export_dir.glob("**/*.jsonl"))
+
+if not jsonl_files:
+    print(f"No .jsonl files found in {EXPORT_DIR}")
+    print("Place your Claude export conversations.jsonl file there and run again.")
+    sys.exit(0)
+
+total = 0
+for f in jsonl_files:
+    total += ingest_file(f)
+
+print(f"\nTotal chunks added to corpus: {total}")
+print(f"Database at: {db_path}")