diff --git a/packages/core/src/perf/perf.test.ts b/packages/core/src/perf/perf.test.ts
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+++ b/packages/core/src/perf/perf.test.ts
@@ -0,0 +1,75 @@
+/**
+ * Performance pass (#32). The deterministic compute path must stay well under the
+ * PLAN.md targets on representative fixtures:
+ *   - scheduler + Monte Carlo forecast < 1s @ 200 open issues.
+ *   - scaling stays roughly linear (no accidental O(n²) in the hot path).
+ *
+ * Reconcile-<5s@500 is network-bound (~2N gitea calls) and is covered by the live
+ * reconcile, not here — this file benchmarks the pure compute the app runs each
+ * turn. Bounds are the actual targets with comfortable headroom so timing jitter
+ * can't flake the suite; actuals are logged.
+ */
+import { describe, expect, it } from 'vitest'
+
+import { forecast } from '../forecast/forecast-v0.js'
+import { type DependencyEdge, schedule, type SchedulableIssue } from '../scheduler/scheduler-v0.js'
+import { scheduleWithCapacity, type Worker } from '../scheduler/scheduler-capacity-v0.js'
+
+const EST = [1, 2, 3, 5, 8]
+const WORKERS: Worker[] = [
+  { person: 'a', speed: 0.8 },
+  { person: 'b', speed: 0.6 },
+  { person: 'c', speed: 1.0 },
+]
+
+/** A representative open backlog: varied estimates/priorities/assignees + a light dependency web. */
+function backlog(n: number): { issues: SchedulableIssue[]; edges: DependencyEdge[] } {
+  const issues: SchedulableIssue[] = Array.from({ length: n }, (_, i) => ({
+    number: i + 1,
+    title: `Issue ${i + 1} with a representative title of some length`,
+    labels: [`est/${EST[i % EST.length]}d`, `p/${(i % 4) + 1}`],
+    estimateDays: EST[i % EST.length],
+    priority: (i % 4) + 1,
+    assignee: WORKERS[i % WORKERS.length].person,
+  }))
+  // ~1 dependency per 3 issues, always on a lower-numbered issue (acyclic)
+  const edges: DependencyEdge[] = []
+  for (let i = 3; i < n; i += 3) edges.push({ issue: i + 1, dependsOn: i - 1 })
+  return { issues, edges }
+}
+
+function ms(fn: () => void): number {
+  const t0 = performance.now()
+  fn()
+  return performance.now() - t0
+}
+
+describe('perf (#32)', () => {
+  it('scheduler + Monte Carlo forecast < 1s @ 200 open issues', () => {
+    const { issues, edges } = backlog(200)
+    const elapsed = ms(() => {
+      schedule(issues, edges)
+      scheduleWithCapacity(issues, edges, WORKERS)
+      forecast(issues, edges, { workers: WORKERS }) // 2000 trials (default)
+    })
+    // eslint-disable-next-line no-console
+    console.log(`[perf] schedule+capacity+forecast @200 = ${elapsed.toFixed(1)}ms`)
+    expect(elapsed).toBeLessThan(1000)
+  })
+
+  it('scales roughly linearly — 400 issues is well under 4x the 100-issue time', () => {
+    const small = backlog(100)
+    const big = backlog(400)
+    const run = (b: typeof small) => () => {
+      schedule(b.issues, b.edges)
+      forecast(b.issues, b.edges, { workers: WORKERS })
+    }
+    // warm up (JIT) so the ratio reflects steady state
+    run(small)()
+    const t100 = Math.max(ms(run(small)), 0.1)
+    const t400 = ms(run(big))
+    // eslint-disable-next-line no-console
+    console.log(`[perf] @100 = ${t100.toFixed(1)}ms · @400 = ${t400.toFixed(1)}ms · ratio ${(t400 / t100).toFixed(1)}x`)
+    expect(t400).toBeLessThan(t100 * 8) // generous: rules out O(n²), tolerant of jitter
+  })
+})