diff --git a/.changeset/review-shed-ranking.md b/.changeset/review-shed-ranking.md
new file mode 100644
index 00000000..54756b81
--- /dev/null
+++ b/.changeset/review-shed-ranking.md
@@ -0,0 +1,5 @@
+---
+"review": minor
+---
+
+Value-ranked shedding and dispatch. The graceful-landing rule's first shed bucket previously said "skip any not-yet-dispatched opt-in reviewers and lenses" with no ordering, so a budget-pressed run shed arbitrarily; the 2026-07-10 production runs showed the consequence (an invocation cap smaller than the roster filled its slots by enablement mechanism, not value, and a matched specialist lens could be shed to afford `conventions`). The shed order is now explicit, lowest value first: conventions, first-principles, holistic, completeness/test-adequacy, and path-triggered specialist lenses last, since a matched lens is the most targeted signal in the run. The same ranking read from the other end (defaults, lenses, targeted opt-ins, generic opt-ins) is now the mandated dispatch order when `maxReviewerInvocations` cannot fit the full roster, with every undispatched reviewer recorded as a planned shed. And reviewer requests (Step 8) are removed from the shed list entirely: pulling a human in matters most on exactly the run whose own coverage is partial. The ranking is a first-cut editorial ordering; replace it with measured per-dimension must-catch contribution from the eval corpus when that data is compiled.
diff --git a/workflows/review/review.md b/workflows/review/review.md
index ecffa4e6..6d69e176 100644
--- a/workflows/review/review.md
+++ b/workflows/review/review.md
@@ -570,7 +570,11 @@ either run by default; a reviewer earns its `enable` line through the eval suite
 not by shipping). Dispatch the default reviewers (`correctness-reviewer`,
 `skill-auditor`, `thread-reconciler`) **plus** every reviewer named in
 `enabledReviewers` **plus** every lens named in `lensesToSpawn`, all **in parallel**
-(one turn), and wait for all.
+(one turn), and wait for all. If `runBudget.maxReviewerInvocations` cannot fit
+that whole set, fill the slots by the dispatch ranking (the budget rule below:
+Step 3, graceful-landing bucket 1): defaults first, then matched lenses, then
+the targeted opt-in dimensions, then the generic ones. Never choose arbitrarily, and record every
+reviewer left undispatched as a planned shed (Step 6 note).
 
 **One candidate contract.** Every finding-producing reviewer returns `findings[]`
 in the same shape (a `label` per finding, from the fixed label set in Step 4); a
@@ -799,10 +803,23 @@ When any proxy passes roughly three-quarters of its soft target (or the trajecto
 is clearly expensive), stop starting new work and shed remaining work in this
 order:
 
-1. Skip any not-yet-dispatched opt-in reviewers and specialist lenses; each becomes
-   a skipped dimension (Step 6 note).
-2. Skip the risks/patterns comment and reviewer requests (Steps 7-8) if they have
-   not happened yet.
+1. Skip not-yet-dispatched opt-in reviewers and specialist lenses in value
+   order, lowest value first; each becomes a skipped dimension (Step 6 note).
+   The ranking, from first-shed to last-shed: `conventions`, then
+   `first-principles`, then `holistic`, then `completeness` and
+   `test-adequacy`, and only then any path-triggered specialist lens from
+   `lensesToSpawn`. A matched lens is the most targeted signal in the run (the
+   router chose it for the specific files this PR touches), so it outranks
+   every generic dimension; shedding `security-auth` on an auth-path diff to
+   afford `conventions` is exactly backwards. This same ranking, read from the
+   other end (defaults, lenses, targeted opt-ins, generic opt-ins), is the
+   dispatch order when the invocation cap cannot fit the roster (Phase 2).
+   The interior order is a first-cut editorial ranking; replace it with
+   measured per-dimension must-catch contribution once the eval corpus
+   yields that data.
+2. Skip the risks/patterns comment (Step 7) if it has not happened yet.
+   Reviewer requests (Step 8) are **never** shed: pulling a human in matters
+   most on exactly the run whose own coverage is partial.
 3. Last, and never at the soft targets alone: the `claim-validator`. It is the
    false-positive gate, and its cost scales with the candidate count (which you
    can already see when deciding), not with the diff, so validating a small