diff --git a/apps/agent-zero/configmaps-bluejay.yaml b/apps/agent-zero/configmaps-bluejay.yaml
index 513cd1c..75d80bc 100644
--- a/apps/agent-zero/configmaps-bluejay.yaml
+++ b/apps/agent-zero/configmaps-bluejay.yaml
@@ -13150,27 +13150,19 @@ data:
     - PowerShell 5.1 compatibility is assumed (no PowerShell 7+ features).
     - All commands run with `-NoProfile -NonInteractive` flags for clean execution.
     """
-kind: ConfigMap
-metadata:
-  name: bluejay-tools-c
-  namespace: agent-zero
-
----
-apiVersion: v1
-data:
   corpus_search.py: |
     # FlowerCore Fleet Corpus Vector Search Tool
     #
     # Queries the AiStation-built SqliteVecVectorStore DB at /a0/usr/vectors/fleet.db
     # (bind-mounted read-only from /var/lib/flowercore/vector-stores/ on the host).
     # Embeds the query through Ollama's nomic-embed-text model, computes cosine
-    # similarity against every stored chunk in pure Python (no numpy — not present
+    # similarity against every stored chunk in pure Python (no numpy — not present
     # in the container), and returns the top-K nearest neighbors with source metadata.
     #
     # This is the offline-friendly counterpart to `intranet_search` (which hits the
     # Intranet's live REST API). Use it for Bible/Greek/Hebrew/Strong's lookups and
     # anywhere the workstation has a newer DB than the Intranet one. The store is
-    # refreshed by `aistation-indexer build <edition>` — see the FlowerCore.Knowledge
+    # refreshed by `aistation-indexer build <edition>` — see the FlowerCore.Knowledge
     # ADR at docs/ai-agents/flowercore-knowledge-service-plan.md.
 
     import json
@@ -13188,7 +13180,7 @@ data:
         "/a0/usr/vectors",
     )
     # When the caller doesn't pick an explicit DB, prefer the biggest fleet tier
-    # present on disk. Workstation → pi-edge → bmo-bot.
+    # present on disk. Workstation → pi-edge → bmo-bot.
     PREFERRED_DB_ORDER = [
         os.environ.get("FLOWERCORE_FLEET_VECTOR_DB", ""),
         "fleet-workstation-full.db",
@@ -13291,10 +13283,10 @@ data:
                 passage = h.get("passage") or ""
                 lang = h.get("language") or ""
                 meta_bits = [x for x in (h["index"], h["repo"], passage, lang) if x]
-                meta = "  ·  ".join(meta_bits)
+                meta = "  ·  ".join(meta_bits)
                 preview = h["text"]
                 if len(preview) > 320:
-                    preview = preview[:320].rstrip() + "…"
+                    preview = preview[:320].rstrip() + "…"
                 lines.append(f"{rank}. **{h['score']:.3f}**  {meta}")
                 lines.append(f"   `{h['source']}`")
                 lines.append(f"   {preview}")
@@ -13349,8 +13341,8 @@ data:
 
 
     def _cosine(a: list, b: list) -> float:
-        """Cosine similarity in pure Python — no numpy in the A0 container."""
-        # zip() stops at the shorter — AiStation DB guarantees same dim per index.
+        """Cosine similarity in pure Python — no numpy in the A0 container."""
+        # zip() stops at the shorter — AiStation DB guarantees same dim per index.
         dot = 0.0
         na = 0.0
         nb = 0.0
@@ -13373,8 +13365,8 @@ data:
                         EstimatedTokens, EmbeddingJson)
 
         Embeddings are stored as JSON arrays in EmbeddingJson; similarity is computed
-        in Python. For ~100k chunks × 768 dims this takes a couple seconds on a
-        workstation — acceptable for interactive A0 use.
+        in Python. For ~100k chunks × 768 dims this takes a couple seconds on a
+        workstation — acceptable for interactive A0 use.
         """
         conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
         try:
@@ -13398,7 +13390,7 @@ data:
             cursor = conn.execute("".join(sql), params)
 
             # Min-heap by (score, ...) would be faster but for interactive use we
-            # just sort at the end — simpler and readable.
+            # just sort at the end — simpler and readable.
             scored = []
             for row in cursor:
                 idx, chunk_id, text, repo, source_file, book, chapter, verses, lang, emb_json = row
@@ -13455,7 +13447,7 @@ data:
                         ).fetchall())
                         for name, dim, updated in idx_rows:
                             count = counts.get(name, 0)
-                            lines.append(f"- **{name}** — {count:,} chunks × {dim}d  (built {updated})")
+                            lines.append(f"- **{name}** — {count:,} chunks × {dim}d  (built {updated})")
                 finally:
                     conn.close()
             except Exception as e:
@@ -13477,11 +13469,12 @@ data:
                 ).fetchall()
                 if not rows:
                     return "(no indexes)"
-                return ", ".join(f"{r[0]}({r[2]}×{r[1]}d)" for r in rows)
+                return ", ".join(f"{r[0]}({r[2]}×{r[1]}d)" for r in rows)
             finally:
                 conn.close()
         except Exception as e:
             return f"(couldn't list: {e})"
+
   intranet_search.py: |
     # Intranet Vector Search Tool
     # Queries the Blue Jay Lab Intranet's Shared.Indexing RAG corpus over its
@@ -13597,6 +13590,15 @@ data:
                 lines.append("")
 
             return Response(message="\n".join(lines), break_loop=False)
+
+kind: ConfigMap
+metadata:
+  name: bluejay-tools-c
+  namespace: agent-zero
+
+---
+apiVersion: v1
+data:
   agent.json: |
     {
       "title": "Blue Jay",