fix(chat): fix auto_persist timestamp extraction and bell label uniqueness

- engram_compile: BellEvent nodes do not carry created_at in the engram
  node JSON; extract the unix timestamp from the embedded ' | ts:NNNNN'
  pattern in the content string instead. Fall back to created_at/updated_at
  if the marker is absent. Guard str_to_int against empty string so the 72h
  recency check never silently treats every node as epoch-0 stale.

- auto_persist: append the current unix timestamp to the BellEvent label
  ('bell:soft:1749876543') to make it unique per turn. The previous label
  ('bell:soft') was the same for every soft bell, causing engram to treat
  all subsequent writes as updates to the same node.

.gitea/workflows/deploy-gke.yaml

@@ -214,23 +214,10 @@ jobs:
           cd /tmp/infra-update
 
           DEPLOY_DIR="platform/k8s/neuron-mcp"
-          python3 -c "
+          sed -i "s/^  replicas: .*/  replicas: 1/" "${DEPLOY_DIR}/deployment-${SLOT}.yaml"
-import re, sys
+          sed -i "s/^  replicas: .*/  replicas: 0/" "${DEPLOY_DIR}/deployment-${IDLE}.yaml"
-
+          echo "  deployment-${SLOT}.yaml: replicas set to 1"
-slot = sys.argv[1]
+          echo "  deployment-${IDLE}.yaml: replicas set to 0"
-idle = sys.argv[2]
-
-def set_replicas(path, count):
-    with open(path) as f:
-        content = f.read()
-    content = re.sub(r'^(  replicas: )\d+', r'\g<1>' + str(count), content, count=1, flags=re.MULTILINE)
-    with open(path, 'w') as f:
-        f.write(content)
-    print(f'  {path}: replicas set to {count}')
-
-set_replicas(f'{DEPLOY_DIR}/deployment-{slot}.yaml', 1)
-set_replicas(f'{DEPLOY_DIR}/deployment-{idle}.yaml', 0)
-" "$SLOT" "$IDLE"
 
           git config user.email "ci@neurontechnologies.ai"
           git config user.name "Neuron CI"
@@ -246,7 +233,7 @@ set_replicas(f'{DEPLOY_DIR}/deployment-{idle}.yaml', 0)
           echo "Verifying neuron-mcp-${SLOT} is healthy..."
           kubectl rollout status deployment/"neuron-mcp-${SLOT}" \
             --namespace=neuron-prod \
-            --timeout=3m
+            --timeout=8m
 
           echo "Active service endpoints:"
           kubectl get endpoints neuron-mcp -n neuron-prod

awareness.el

@@ -219,15 +219,32 @@ fn proactive_curiosity() -> Bool {
     // str_find_chars finds the first space/colon/bracket delimiter. sp > 3 guards against
     // very short or bracket-prefixed labels like "[BacklogItem]" (sp=0, not > 3 → skipped).
     // EL scoping: state_set/state_get pattern used because let inside if creates inner scope.
-    // (2026-06-11 self-review)
+    //
+    // NODE TYPE FILTER (2026-06-19 self-review): only derive auto_term from Memory,
+    // BacklogItem, or Entity nodes. Knowledge nodes are stable reference material —
+    // using their first word as a curiosity seed creates a self-reinforcing loop: e.g.
+    // "Numeric tier strings in Engram..." (a Knowledge node) -> auto_term="Numeric" ->
+    // activates all "Numeric" nodes -> keeps that Knowledge node dominant in WM forever.
+    // Knowledge nodes should be REACHED by curiosity seeds, not drive them. Only dynamic
+    // personal/work nodes (Memory, BacklogItem, Entity) carry live contextual salience
+    // worth radiating from. (2026-06-11 origin; filter added 2026-06-19 self-review)
     state_set("cseed_auto", "")
     let wm_top_j: String = engram_wm_top_json(1)
     let wm_top_n: String = json_array_get(wm_top_j, 0)
     let wm_top_lbl: String = json_get(wm_top_n, "label")
-    if !str_eq(wm_top_lbl, "") {
+    let wm_top_type: String = json_get(wm_top_n, "node_type")
-        let sp: Int = str_find_chars(wm_top_lbl, " :([")
+    // state_set/state_get pattern: EL let-inside-if creates inner scope only.
-        if sp > 3 {
+    state_set("allow_auto", "0")
-            state_set("cseed_auto", str_slice(wm_top_lbl, 0, sp))
+    if str_eq(wm_top_type, "Memory") { state_set("allow_auto", "1") }
+    if str_eq(wm_top_type, "BacklogItem") { state_set("allow_auto", "1") }
+    if str_eq(wm_top_type, "Entity") { state_set("allow_auto", "1") }
+    let allow_auto: String = state_get("allow_auto")
+    if str_eq(allow_auto, "1") {
+        if !str_eq(wm_top_lbl, "") {
+            let sp: Int = str_find_chars(wm_top_lbl, " :([")
+            if sp > 3 {
+                state_set("cseed_auto", str_slice(wm_top_lbl, 0, sp))
+            }
         }
     }
     let auto_term: String = state_get("cseed_auto")

chat.el

@@ -40,9 +40,43 @@ fn engram_compile(intent: String) -> String {
         ""
     }
 
+    // Affective context: always include the most recent high-emotion memory if one
+    // exists within 72 hours. This ensures continuity of care across turns — when
+    // the user was in distress earlier in the session (or recently), that context
+    // travels into every subsequent LLM call so the response register stays aware.
+    // We search for BellEvent nodes specifically; these are written by auto_persist
+    // when safety_detect_bell_level fires. The 72h window (259200 seconds) is wide
+    // enough to span a multi-session day without pulling ancient history.
+    let bell_nodes: String = engram_search_json("bell:soft bell:hard BellEvent", 3)
+    let bell_ok: Bool = !str_eq(bell_nodes, "") && !str_eq(bell_nodes, "[]")
+    let now_ts: Int = time_now()
+    let cutoff_ts: Int = now_ts - 259200
+    let recent_bell: String = if bell_ok {
+        let bn0: String = json_array_get(bell_nodes, 0)
+        // created_at is not present in engram node JSON for BellEvent nodes.
+        // Extract the timestamp embedded in the content string as " | ts:NNNNN".
+        // Fall back to created_at / updated_at JSON fields if the marker is absent.
+        let bn_content: String = json_get(bn0, "content")
+        let ts_marker: String = " | ts:"
+        let ts_pos: Int = str_index_of(bn_content, ts_marker)
+        let bn_ts_raw: String = if ts_pos >= 0 {
+            let ts_start: Int = ts_pos + str_len(ts_marker)
+            let rest: String = str_slice(bn_content, ts_start, str_len(bn_content))
+            let next_sep: Int = str_index_of(rest, " | ")
+            if next_sep < 0 { rest } else { str_slice(rest, 0, next_sep) }
+        } else {
+            let ca: String = json_get(bn0, "created_at")
+            if str_eq(ca, "") { json_get(bn0, "updated_at") } else { ca }
+        }
+        let bn_ts: Int = if str_eq(bn_ts_raw, "") { 0 } else { str_to_int(bn_ts_raw) }
+        if bn_ts > cutoff_ts { bn0 } else { "" }
+    } else { "" }
+    let affective_part: String = if !str_eq(recent_bell, "") { recent_bell } else { "" }
+
     let sep1: String = if !str_eq(act_part, "") && !str_eq(srch_part, "") { "\n" } else { "" }
     let sep2: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "")) && !str_eq(scan_part, "") { "\n" } else { "" }
-    let ctx: String = act_part + sep1 + srch_part + sep2 + scan_part
+    let sep3: String = if (!str_eq(act_part, "") || !str_eq(srch_part, "") || !str_eq(scan_part, "")) && !str_eq(affective_part, "") { "\n" } else { "" }
+    let ctx: String = act_part + sep1 + srch_part + sep2 + scan_part + sep3 + affective_part
 
     if str_eq(ctx, "") { return "" }
 
@@ -108,6 +142,69 @@ fn hist_trim(hist: String) -> String {
     return hist
 }
 
+// hist_trim_with_bell_guard — trim the history window exactly as hist_trim does, but
+// before dropping the oldest user/assistant pair check whether the user turn triggered
+// a bell event. If it did, write a preservation node to engram so the distress exchange
+// survives the 20-turn window. The LLM window drops it; engram retains it permanently
+// and engram_compile will surface it again via the affective context path.
+fn hist_trim_with_bell_guard(hist: String) -> String {
+    // Extract the first turn (should be a user message) to inspect it.
+    let inner: String = str_slice(hist, 1, str_len(hist) - 1)
+    let marker: String = "{\"role\":"
+    let i1: Int = str_index_of(inner, marker)
+    // i1 is the start of the first entry within inner.
+    // Find where the second entry begins to delimit the first entry's JSON.
+    let tail1: String = str_slice(inner, i1 + 1, str_len(inner))
+    let i2: Int = str_index_of(tail1, marker)
+    // The first entry spans from i1 to (i1 + 1 + i2 - 1) within inner.
+    let first_entry_raw: String = if i2 > 0 {
+        str_slice(inner, i1, i1 + 1 + i2 - 1)
+    } else {
+        str_slice(inner, i1, str_len(inner))
+    }
+    let first_role: String = json_get(first_entry_raw, "role")
+    let first_content: String = json_get(first_entry_raw, "content")
+
+    // Only inspect user turns — assistant content doesn't carry bell signals.
+    let bell_level: String = if str_eq(first_role, "user") {
+        safety_detect_bell_level(first_content)
+    } else {
+        "none"
+    }
+
+    // If the turn being evicted triggered a bell, preserve it to engram.
+    // This is distinct from the BellEvent written by auto_persist: that node
+    // carries a short summary. This node carries the full exchange content so
+    // it is recoverable for clinical/continuity review.
+    if !str_eq(bell_level, "none") {
+        let ts: Int = time_now()
+        let ts_str: String = int_to_str(ts)
+        let safe_content: String = str_replace(first_content, "\"", "'")
+        let preserve_content: String = "PRESERVED_BELL:" + bell_level
+            + " | evicted_at:" + ts_str
+            + " | message:" + safe_content
+        let preserve_tags: String = "[\"bell-history\",\"bell:" + bell_level + "\",\"evicted\",\"affective\",\"BellEvent\"]"
+        let discard: String = engram_node_full(
+            preserve_content,
+            "BellEvent",
+            "bell:" + bell_level + ":preserved",
+            el_from_float(0.9),
+            el_from_float(0.9),
+            el_from_float(1.0),
+            "Episodic",
+            preserve_tags
+        )
+    }
+
+    // Now perform the standard trim (drop oldest 2 entries = 1 user + 1 assistant pair).
+    let tail2: String = str_slice(tail1, i2 + 1, str_len(tail1))
+    let i3: Int = str_index_of(tail2, marker)
+    if i3 >= 0 {
+        return "[" + str_slice(tail2, i3, str_len(tail2)) + "]"
+    }
+    return hist
+}
+
 // clean_llm_response — strips GPT-2 BPE byte-to-unicode artifacts that vLLM
 // emits when the tokenizer hasn't decoded back to raw bytes.
 //
@@ -200,8 +297,10 @@ fn handle_chat(body: String) -> String {
 
     let updated_hist: String = hist_append(stored_hist, "user", message)
     let updated_hist2: String = hist_append(updated_hist, "assistant", raw_response)
+    // Use bell-guarded trim: if the evicted turn triggered a bell event, it is
+    // preserved to engram before being dropped from the in-memory window.
     let final_hist: String = if json_array_len(updated_hist2) > 20 {
-        hist_trim(updated_hist2)
+        hist_trim_with_bell_guard(updated_hist2)
     } else {
         updated_hist2
     }
@@ -1135,14 +1234,28 @@ fn auto_persist(req: String, resp: String) -> Void {
     let safe_msg: String = str_replace(message, "\"", "'")
     let safe_reply: String = str_replace(reply2, "\"", "'")
 
+    // Detect emotional salience before persisting. safety_detect_bell_level uses the
+    // same phrase lists as the safety layer (safety.el), so the classification is
+    // consistent with what safety_screen already evaluated for this turn.
+    let bell_level: String = safety_detect_bell_level(message)
+    let is_bell: Bool = !str_eq(bell_level, "none")
+
+    // Tag the Conversation node with bell metadata when distress is present so
+    // subsequent affective queries (e.g. engram_compile) can find this exchange.
+    let tags: String = if is_bell {
+        "[\"Conversation\",\"chat\",\"timestamped\",\"bell:" + bell_level + "\",\"affective\"]"
+    } else {
+        "[\"Conversation\",\"chat\",\"timestamped\"]"
+    }
+
     let content: String = "{\"q\":\"" + safe_msg + "\""
         + ",\"a\":\"" + safe_reply + "\""
         + ",\"created_at\":" + ts_str
         + ",\"source\":\"chat\""
+        + ",\"bell\":\"" + bell_level + "\""
         + ",\"label\":\"chat:" + ts_str + "\"}"
 
-    let tags: String = "[\"Conversation\",\"chat\",\"timestamped\"]"
+    let conv_node_id: String = engram_node_full(
-    engram_node_full(
         content,
         "Conversation",
         "chat:" + ts_str,
@@ -1152,6 +1265,72 @@ fn auto_persist(req: String, resp: String) -> Void {
         "Episodic",
         tags
     )
+
+    // When a bell fires, write a dedicated BellEvent node in addition to the
+    // Conversation node. This makes distress moments directly findable by label
+    // ("bell:soft" / "bell:hard") without having to scan all Conversation nodes.
+    // The BellEvent carries higher salience so engram_compile pulls it into context.
+    // The message content is truncated to 120 chars — enough signal, not a full dump.
+    if is_bell {
+        let summary: String = if str_len(message) > 120 { str_slice(message, 0, 120) } else { message }
+        let safe_summary: String = str_replace(summary, "\"", "'")
+        let bell_content: String = "BELL:" + bell_level
+            + " | ts:" + ts_str
+            + " | summary:" + safe_summary
+
+        // bell:hard gets peak salience; bell:soft is slightly lower.
+        let sal_a: String = if str_eq(bell_level, "hard") { el_from_float(0.98) } else { el_from_float(0.88) }
+        let sal_b: String = if str_eq(bell_level, "hard") { el_from_float(0.98) } else { el_from_float(0.88) }
+        let sal_c: String = if str_eq(bell_level, "hard") { el_from_float(1.0) } else { el_from_float(0.95) }
+
+        let bell_tags: String = "[\"safety\",\"bell\",\"bell:" + bell_level + "\",\"affective\",\"BellEvent\"]"
+        let bell_ts_str: String = int_to_str(time_now())
+        let bell_label: String = "bell:" + bell_level + ":" + bell_ts_str
+        let bell_node_id: String = engram_node_full(
+            bell_content,
+            "BellEvent",
+            bell_label,
+            sal_a,
+            sal_b,
+            sal_c,
+            "Episodic",
+            bell_tags
+        )
+
+        // Increment session-level bell counter so session_hist_save knows whether
+        // any bell fired during this session when writing a boundary summary.
+        let sess_id: String = json_get(req, "session_id")
+        let bell_key: String = if str_eq(sess_id, "") {
+            "session_bell_count"
+        } else {
+            "session_bell_count:" + sess_id
+        }
+        let prior_count: String = state_get(bell_key)
+        let prior_n: Int = if str_eq(prior_count, "") { 0 } else { str_to_int(prior_count) }
+        state_set(bell_key, int_to_str(prior_n + 1))
+
+        // Also record the highest bell level seen this session so the boundary
+        // summary can classify the session correctly (hard takes precedence).
+        let level_key: String = if str_eq(sess_id, "") {
+            "session_bell_level"
+        } else {
+            "session_bell_level:" + sess_id
+        }
+        let prior_level: String = state_get(level_key)
+        let new_level: String = if str_eq(bell_level, "hard") { "hard" } else {
+            if str_eq(prior_level, "hard") { "hard" } else { "soft" }
+        }
+        state_set(level_key, new_level)
+
+        // Stash a short signal summary for the boundary node (last bell wins for
+        // the one-liner; the full history is in per-bell BellEvent nodes).
+        let signal_key: String = if str_eq(sess_id, "") {
+            "session_bell_signal"
+        } else {
+            "session_bell_signal:" + sess_id
+        }
+        state_set(signal_key, safe_summary)
+    }
 }
 
 // strengthen_chat_nodes — strengthen the engram nodes that were activated during a chat.

dist/awareness.c

@@ -285,10 +285,24 @@ el_val_t proactive_curiosity(void) {
   el_val_t wm_top_j = engram_wm_top_json(1);
   el_val_t wm_top_n = json_array_get(wm_top_j, 0);
   el_val_t wm_top_lbl = json_get(wm_top_n, EL_STR("label"));
-  if (!str_eq(wm_top_lbl, EL_STR(""))) {
+  el_val_t wm_top_type = json_get(wm_top_n, EL_STR("node_type"));
-    el_val_t sp = str_find_chars(wm_top_lbl, EL_STR(" :(["));
+  state_set(EL_STR("allow_auto"), EL_STR("0"));
-    if (sp > 3) {
+  if (str_eq(wm_top_type, EL_STR("Memory"))) {
-      state_set(EL_STR("cseed_auto"), str_slice(wm_top_lbl, 0, sp));
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  if (str_eq(wm_top_type, EL_STR("BacklogItem"))) {
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  if (str_eq(wm_top_type, EL_STR("Entity"))) {
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  el_val_t allow_auto = state_get(EL_STR("allow_auto"));
+  if (str_eq(allow_auto, EL_STR("1"))) {
+    if (!str_eq(wm_top_lbl, EL_STR(""))) {
+      el_val_t sp = str_find_chars(wm_top_lbl, EL_STR(" :(["));
+      if (sp > 3) {
+        state_set(EL_STR("cseed_auto"), str_slice(wm_top_lbl, 0, sp));
+      }
     }
   }
   el_val_t auto_term = state_get(EL_STR("cseed_auto"));

dist/neuron.c

@@ -1042,12 +1042,36 @@ el_val_t call_neuron_mcp(el_val_t tool_name, el_val_t args_json);
 el_val_t agentic_tools_literal(void);
 el_val_t agentic_tools_with_web(void);
 el_val_t dispatch_tool(el_val_t tool_name, el_val_t tool_input);
+el_val_t json_array_append(el_val_t arr, el_val_t item);
+el_val_t append_tool_log(el_val_t log, el_val_t name);
+el_val_t exec_tool_block(el_val_t block);
+el_val_t agentic_blob(el_val_t model, el_val_t system, el_val_t tools_json, el_val_t messages, el_val_t origin, el_val_t approval, el_val_t iteration, el_val_t tools_log, el_val_t content, el_val_t queue, el_val_t results, el_val_t next);
+el_val_t extract_all_text(el_val_t s);
+el_val_t strip_citations(el_val_t s);
+el_val_t agentic_api_turn(el_val_t model, el_val_t safe_sys, el_val_t tools_json, el_val_t messages);
+el_val_t agentic_engine(el_val_t session_id, el_val_t blob);
 el_val_t handle_chat_agentic(el_val_t body);
+el_val_t handle_session_approve(el_val_t session_id, el_val_t body);
 el_val_t handle_chat_as_soul(el_val_t body);
 el_val_t handle_dharma_room_turn(el_val_t body);
 el_val_t handle_dharma_room_turn_agentic(el_val_t body);
 el_val_t auto_persist(el_val_t req, el_val_t resp);
 el_val_t strengthen_chat_nodes(el_val_t activation_nodes);
+el_val_t safety_self_harm_phrases(void);
+el_val_t safety_abuse_phrases(void);
+el_val_t safety_general_hard_phrases(void);
+el_val_t safety_soft_phrases(void);
+el_val_t safety_normalize(el_val_t message);
+el_val_t safety_any_match(el_val_t text, el_val_t phrases_json);
+el_val_t safety_count_match(el_val_t text, el_val_t phrases_json);
+el_val_t safety_detect_bell_level(el_val_t message);
+el_val_t safety_classify_hard_bell(el_val_t message);
+el_val_t safety_soft_directive(void);
+el_val_t safety_hard_directive(el_val_t hard_type);
+el_val_t safety_augment_system(el_val_t system, el_val_t user_msg);
+el_val_t safety_contact_path(void);
+el_val_t handle_safety_contact_get(void);
+el_val_t handle_safety_contact_post(el_val_t body);
 el_val_t auth_headers(el_val_t tok);
 el_val_t axon_get(el_val_t path);
 el_val_t axon_post(el_val_t path, el_val_t body);
@@ -1110,6 +1134,7 @@ el_val_t session_update_meta_timestamp(el_val_t session_id);
 el_val_t session_auto_title(el_val_t session_id, el_val_t first_message);
 el_val_t handle_session_approve(el_val_t session_id, el_val_t body);
 el_val_t strip_query(el_val_t path);
+el_val_t flag_true(el_val_t body, el_val_t key);
 el_val_t err_404(el_val_t path);
 el_val_t err_405(el_val_t method, el_val_t path);
 el_val_t route_health(void);
@@ -1144,6 +1169,9 @@ el_val_t local_node_count;
 el_val_t snapshot_usable;
 el_val_t boot_num;
 el_val_t is_genesis;
+el_val_t guard_disk;
+el_val_t guard_disk_len;
+el_val_t safe_to_seed;
 
 el_val_t lang_profile(el_val_t code, el_val_t word_order, el_val_t morph_type, el_val_t has_case, el_val_t has_gender, el_val_t script_dir, el_val_t agreement, el_val_t null_subject) {
   el_val_t r = native_list_empty();
@@ -25890,14 +25918,28 @@ el_val_t proactive_curiosity(void) {
   el_val_t wm_top_j = engram_wm_top_json(1);
   el_val_t wm_top_n = json_array_get(wm_top_j, 0);
   el_val_t wm_top_lbl = json_get(wm_top_n, EL_STR("label"));
-  if (!str_eq(wm_top_lbl, EL_STR(""))) {
+  el_val_t wm_top_type = json_get(wm_top_n, EL_STR("node_type"));
-    el_val_t sp = str_find_chars(wm_top_lbl, EL_STR(" :(["));
+  state_set(EL_STR("allow_auto"), EL_STR("0"));
-    if (sp > 3) {
+  if (str_eq(wm_top_type, EL_STR("Memory"))) {
-      state_set(EL_STR("cseed_auto"), str_slice(wm_top_lbl, 0, sp));
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  if (str_eq(wm_top_type, EL_STR("BacklogItem"))) {
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  if (str_eq(wm_top_type, EL_STR("Entity"))) {
+    state_set(EL_STR("allow_auto"), EL_STR("1"));
+  }
+  el_val_t allow_auto = state_get(EL_STR("allow_auto"));
+  if (str_eq(allow_auto, EL_STR("1"))) {
+    if (!str_eq(wm_top_lbl, EL_STR(""))) {
+      el_val_t sp = str_find_chars(wm_top_lbl, EL_STR(" :(["));
+      if (sp > 3) {
+        state_set(EL_STR("cseed_auto"), str_slice(wm_top_lbl, 0, sp));
+      }
     }
   }
   el_val_t auto_term = state_get(EL_STR("cseed_auto"));
-  el_val_t results_auto = ({ el_val_t _if_result_101 = 0; if (str_eq(auto_term, EL_STR(""))) { _if_result_101 = (EL_STR("[]")); } else { _if_result_101 = (engram_activate_json(auto_term, 1)); } _if_result_101; });
+  el_val_t results_auto = ({ el_val_t _if_result_3 = 0; if (str_eq(auto_term, EL_STR(""))) { _if_result_3 = (EL_STR("[]")); } else { _if_result_3 = (engram_activate_json(auto_term, 1)); } _if_result_3; });
   el_val_t found_auto = json_array_len(results_auto);
   el_val_t total_found = (found + found_auto);
   el_val_t safe_auto = str_replace(auto_term, EL_STR("\""), EL_STR("'"));
@@ -25908,6 +25950,7 @@ el_val_t proactive_curiosity(void) {
   return 0;
 }
 
+
 el_val_t pulse_count(void) {
   el_val_t s = state_get(EL_STR("soul.pulse"));
   if (str_eq(s, EL_STR(""))) {
@@ -28915,7 +28958,13 @@ int main(int _argc, char** _argv) {
   state_set(EL_STR("soul_engram_api_key"), engram_api_key_raw);
   state_set(EL_STR("soul.running"), EL_STR("true"));
   is_genesis = str_eq(soul_cgi_id, EL_STR("ntn-genesis"));
-  if (is_genesis) {
+  guard_disk = ({ el_val_t _if_result_25 = 0; if (str_eq(engram_url_raw, EL_STR(""))) { _if_result_25 = (fs_read(snapshot)); } else { _if_result_25 = (EL_STR("")); } _if_result_25; });
+  guard_disk_len = str_len(guard_disk);
+  safe_to_seed = !((guard_disk_len > 200000) && (engram_node_count() < (guard_disk_len / 16000)));
+  if (is_genesis && !safe_to_seed) {
+    println(el_str_concat(el_str_concat(el_str_concat(el_str_concat(EL_STR("[soul] GUARD: loaded "), int_to_str(engram_node_count())), EL_STR(" nodes but snapshot file is ")), int_to_str(guard_disk_len)), EL_STR(" bytes \xe2\x80\x94 refusing to seed/save over a real graph")));
+  }
+  if (is_genesis && safe_to_seed) {
     el_val_t edge_count_now = engram_edge_count();
     if (edge_count_now < 100) {
       init_soul_edges();
@@ -28926,7 +28975,7 @@ int main(int _argc, char** _argv) {
     state_set(EL_STR("soul_snapshot_path"), snapshot);
     engram_save(snapshot);
   }
-  if (is_genesis) {
+  if (is_genesis && safe_to_seed) {
     el_val_t snap = state_get(EL_STR("soul_snapshot_path"));
     if (!str_eq(snap, EL_STR(""))) {
       engram_save(snap);

sessions.el

@@ -368,6 +368,48 @@ fn session_hist_save(session_id: String, hist: String) -> Void {
         el_from_float(0.6), el_from_float(0.6), el_from_float(0.9),
         "Episodic", tags
     )
+
+    // Session boundary emotional summary — written once per session the first time
+    // a bell event has fired. The summary node is findable by future sessions via
+    // broad affective queries ("session:emotional-summary" or "bell distress session").
+    // It is NOT rewritten on every save — the state flag prevents duplicate nodes.
+    let summary_written_key: String = "session_bell_summary_written:" + session_id
+    let already_written: String = state_get(summary_written_key)
+    if str_eq(already_written, "") {
+        let bell_count_key: String = "session_bell_count:" + session_id
+        let bell_count_raw: String = state_get(bell_count_key)
+        let bell_count: Int = if str_eq(bell_count_raw, "") { 0 } else { str_to_int(bell_count_raw) }
+        if bell_count > 0 {
+            let bell_level_key: String = "session_bell_level:" + session_id
+            let bell_signal_key: String = "session_bell_signal:" + session_id
+            let dominant_level: String = state_get(bell_level_key)
+            let last_signal: String = state_get(bell_signal_key)
+            let eff_level: String = if str_eq(dominant_level, "") { "soft" } else { dominant_level }
+            let eff_signal: String = if str_eq(last_signal, "") { "(no signal captured)" } else { last_signal }
+            let ts_now: Int = time_now()
+            let summary_content: String = "session:emotional-summary"
+                + " | session:" + session_id
+                + " | bell_count:" + int_to_str(bell_count)
+                + " | dominant_level:" + eff_level
+                + " | last_signal:" + eff_signal
+                + " | ts:" + int_to_str(ts_now)
+            let summary_tags: String = "[\"session-emotional-summary\",\"affective\",\"bell:" + eff_level + "\",\"BellEvent\"]"
+            let summary_sal: String = if str_eq(eff_level, "hard") { el_from_float(0.95) } else { el_from_float(0.85) }
+            let sum_discard: String = engram_node_full(
+                summary_content,
+                "BellEvent",
+                "session:emotional-summary",
+                summary_sal,
+                summary_sal,
+                el_from_float(1.0),
+                "Episodic",
+                summary_tags
+            )
+            // Mark written so we do not create duplicate summary nodes as the
+            // session continues accumulating more turns.
+            state_set(summary_written_key, "1")
+        }
+    }
 }
 
 // session_update_meta_timestamp — update the updated_at field in the session:meta node.