hub differentiation + refine_target for automatic section targeting

Pattern separation for memory graph: when a file-level node (e.g.
identity.md) has section children, redistribute its links to the
best-matching section using cosine similarity.

- differentiate_hub: analyze hub, propose link redistribution
- refine_target: at link creation time, automatically target the
  most specific section instead of the file-level hub
- Applied refine_target in all four link creation paths (digest
  links, journal enrichment, apply consolidation, link-add command)
- Saturated hubs listed in agent topology header with "DO NOT LINK"

This prevents hub formation proactively (refine_target) and
remediates existing hubs (differentiate command).

Co-Authored-By: ProofOfConcept <poc@bcachefs.org>

src/digest.rs

@@ -9,6 +9,7 @@
 //   5. Extracts links and saves agent results
 
 use crate::capnp_store::{self, Store};
+use crate::neuro;
 
 use regex::Regex;
 use std::fs;
@@ -803,6 +804,11 @@ pub fn apply_digest_links(store: &mut Store, links: &[DigestLink]) -> (usize, us
             }
         };
 
+        // Refine target to best-matching section if available
+        let source_content = store.nodes.get(&source)
+            .map(|n| n.content.as_str()).unwrap_or("");
+        let target = neuro::refine_target(store, source_content, &target);
+
         if source == target { skipped += 1; continue; }
 
         // Check if link already exists
@@ -1070,6 +1076,11 @@ pub fn journal_enrich(
                 None => { println!("  SKIP {} (no matching journal node)", target); continue; }
             };
 
+            // Refine target to best-matching section
+            let source_content = store.nodes.get(&source_key)
+                .map(|n| n.content.as_str()).unwrap_or("");
+            let resolved = neuro::refine_target(store, source_content, &resolved);
+
             let source_uuid = match store.nodes.get(&source_key) {
                 Some(n) => n.uuid,
                 None => continue,
@@ -1301,6 +1312,11 @@ pub fn apply_consolidation(store: &mut Store, do_apply: bool, report_file: Optio
                 Err(e) => { println!("  ? {} → {}: {}", src, tgt, e); skipped += 1; continue; }
             };
 
+            // Refine target to best-matching section
+            let source_content = store.nodes.get(&source)
+                .map(|n| n.content.as_str()).unwrap_or("");
+            let target = neuro::refine_target(store, source_content, &target);
+
             let exists = store.relations.iter().any(|r|
                 r.source_key == source && r.target_key == target && !r.deleted
             );