feat: "Make It Actually Useful" sprint — observability + Phase 10

Pipeline observability:
- Retrieval harness runs nightly (Step E in batch-extract.sh)
- Pipeline summary persisted to project state after each run
  (pipeline_last_run, pipeline_summary, retrieval_harness_result)
- Dashboard enhanced: interaction total + by_client, pipeline health
  (last_run, hours_since, harness results, triage stats), dynamic
  project list from registry

Phase 10 — reinforcement-based auto-promotion:
- auto_promote_reinforced(): candidates with reference_count >= 3 and
  confidence >= 0.7 auto-graduate to active
- expire_stale_candidates(): candidates unreinforced for 14+ days
  auto-rejected to prevent unbounded queue growth
- Both wired into nightly cron (Step B2)
- Batch script: scripts/auto_promote_reinforced.py (--dry-run support)

Knowledge seeding:
- scripts/seed_project_state.py: 26 curated Trusted Project State
  entries across p04-gigabit, p05-interferometer, p06-polisher,
  atomizer-v2, abb-space, atocore (decisions, requirements, facts,
  contacts, milestones)

Tests: 299 → 303 (4 new Phase 10 tests)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/atocore/memory/service.py

@@ -340,6 +340,84 @@ def reinforce_memory(
     return True, old_confidence, new_confidence
 
 
+def auto_promote_reinforced(
+    min_reference_count: int = 3,
+    min_confidence: float = 0.7,
+    max_age_days: int = 14,
+) -> list[str]:
+    """Auto-promote candidate memories with strong reinforcement signals.
+
+    Phase 10: memories that have been reinforced by multiple interactions
+    graduate from candidate to active without human review. This rewards
+    knowledge that the system keeps referencing organically.
+
+    Returns a list of promoted memory IDs.
+    """
+    from datetime import timedelta
+
+    cutoff = (
+        datetime.now(timezone.utc) - timedelta(days=max_age_days)
+    ).strftime("%Y-%m-%d %H:%M:%S")
+    promoted: list[str] = []
+    with get_connection() as conn:
+        rows = conn.execute(
+            "SELECT id, content, memory_type, project, confidence, "
+            "reference_count FROM memories "
+            "WHERE status = 'candidate' "
+            "AND COALESCE(reference_count, 0) >= ? "
+            "AND confidence >= ? "
+            "AND last_referenced_at >= ?",
+            (min_reference_count, min_confidence, cutoff),
+        ).fetchall()
+
+    for row in rows:
+        mid = row["id"]
+        ok = promote_memory(mid)
+        if ok:
+            promoted.append(mid)
+            log.info(
+                "memory_auto_promoted",
+                memory_id=mid,
+                memory_type=row["memory_type"],
+                project=row["project"] or "(global)",
+                reference_count=row["reference_count"],
+                confidence=round(row["confidence"], 3),
+            )
+    return promoted
+
+
+def expire_stale_candidates(
+    max_age_days: int = 14,
+) -> list[str]:
+    """Reject candidate memories that sat in queue too long unreinforced.
+
+    Candidates older than ``max_age_days`` with zero reinforcement are
+    auto-rejected to prevent unbounded queue growth. Returns rejected IDs.
+    """
+    from datetime import timedelta
+
+    cutoff = (
+        datetime.now(timezone.utc) - timedelta(days=max_age_days)
+    ).strftime("%Y-%m-%d %H:%M:%S")
+    expired: list[str] = []
+    with get_connection() as conn:
+        rows = conn.execute(
+            "SELECT id FROM memories "
+            "WHERE status = 'candidate' "
+            "AND COALESCE(reference_count, 0) = 0 "
+            "AND created_at < ?",
+            (cutoff,),
+        ).fetchall()
+
+    for row in rows:
+        mid = row["id"]
+        ok = reject_candidate_memory(mid)
+        if ok:
+            expired.append(mid)
+            log.info("memory_expired", memory_id=mid)
+    return expired
+
+
 def get_memories_for_context(
     memory_types: list[str] | None = None,
     project: str | None = None,