feat(ledger): write-path stamping — a pulled canon the session then instantiates lands as a hook instance row (#2791, milestone 294 step 5)

The prior-art hook now names the shapes being written (shapes=kind:name —
every definition in the payload, or the one enclosing an Edit found by
walking the file upward) and the server stamps them as instance rows when
the session PULLED a snippet inside PULL_WINDOW that the payload references
by symbol or that the semantic arm scored for this very payload.
classified_by=hook, evidence in reason; never overrides a judgment or a
canonical row, overridable by classify_shapes. Offered-but-unopened stamps
nothing. Pulled-and-already-seen snippets stay in the semantic query as
evidence without re-entering the deduped menu. A brand-new shape gets a
provisional row the next sync confirms or vanishes. Read-scoped keys get
the hint, never the stamp. Plugin 0.1.34.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/scribe/mcp/tools/shapes.py

@@ -77,10 +77,17 @@ async def list_shapes(
         limit/offset: page through big ledgers (limit caps at 500).
 
     Returns {"shapes": [...], "total": N} — total counts every match, not
-    just this page. Classify what you can judge with classify_shapes; a
-    repeating shape with NO recorded canon is a derive-one-first moment
-    (consolidate onto a reference, create_snippet it, then classify the
-    rest against it), never N loose classifications.
+    just this page. Each row's `classified_by` says who judged: agent /
+    audit / import are judgments; `mechanical` is the canonical stamp the
+    sync applies; `hook` is write-path EVIDENCE (#2791) — the session pulled
+    a snippet and then wrote code referencing/resembling it, so the shape
+    was stamped an instance with the evidence in `reason`. A hook row is
+    overridable by any classify_shapes call; it never overrides yours.
+
+    Classify what you can judge with classify_shapes; a repeating shape
+    with NO recorded canon is a derive-one-first moment (consolidate onto a
+    reference, create_snippet it, then classify the rest against it), never
+    N loose classifications.
     """
     uid = current_user_id()
     rows, total = await shape_ledger_svc.list_project_shapes(