feat(correcting-plans): implement Predictive Error Prevention (Layer 4) with Error Pattern Database

Implement spec-mandated Layer 4 Predictive Error Prevention system:

- ErrorPattern domain model with pattern text, historical failures,
  preventive checks, frequency tracking, and keyword-based matching.
- ErrorPatternRepository with in-memory CRUD + context-matching query.
- ErrorPatternService with record_failure(), match_patterns(), and
  get_statistics() methods.
- Wire into plan execution via plan_lifecycle_service pre-execution hook.
- Add error pattern statistics to CLI diagnostics output.

Behave BDD: 11 scenarios covering recording, matching, formatting, stats.
Robot Framework: 3 integration smoke tests.
ASV benchmarks: pattern matching performance.

ISSUES CLOSED: #571

benchmarks/acms_pipeline_phase3_bench.py

@@ -14,6 +14,7 @@ from __future__ import annotations
 import importlib
 import sys
 from pathlib import Path
+from typing import ClassVar
 
 _SRC = str(Path(__file__).resolve().parents[1] / "src")
 if _SRC not in sys.path:
@@ -71,8 +72,8 @@ def _make_fragments(
 class RelevanceCoherenceOrdererSuite:
     """Benchmark RelevanceCoherenceOrderer throughput."""
 
-    params: list[int] = [10, 100, 500]
-    param_names: list[str] = ["num_fragments"]
+    params: ClassVar[list[int]] = [10, 100, 500]
+    param_names: ClassVar[list[str]] = ["num_fragments"]
 
     def setup(self, n: int) -> None:
         self.orderer = RelevanceCoherenceOrderer()
@@ -85,8 +86,8 @@ class RelevanceCoherenceOrdererSuite:
 class ProvenancePreambleGeneratorSuite:
     """Benchmark ProvenancePreambleGenerator throughput."""
 
-    params: list[int] = [10, 100, 500]
-    param_names: list[str] = ["num_fragments"]
+    params: ClassVar[list[int]] = [10, 100, 500]
+    param_names: ClassVar[list[str]] = ["num_fragments"]
 
     def setup(self, n: int) -> None:
         self.generator = ProvenancePreambleGenerator()
@@ -99,8 +100,8 @@ class ProvenancePreambleGeneratorSuite:
 class ArceStrategySuite:
     """Benchmark ArceStrategy throughput."""
 
-    params: list[int] = [10, 100, 500]
-    param_names: list[str] = ["num_fragments"]
+    params: ClassVar[list[int]] = [10, 100, 500]
+    param_names: ClassVar[list[str]] = ["num_fragments"]
 
     def setup(self, n: int) -> None:
         self.strategy = ArceStrategy(max_iterations=3)
@@ -114,8 +115,8 @@ class ArceStrategySuite:
 class TemporalArchaeologyStrategySuite:
     """Benchmark TemporalArchaeologyStrategy throughput."""
 
-    params: list[int] = [10, 100, 500]
-    param_names: list[str] = ["num_fragments"]
+    params: ClassVar[list[int]] = [10, 100, 500]
+    param_names: ClassVar[list[str]] = ["num_fragments"]
 
     def setup(self, n: int) -> None:
         self.strategy = TemporalArchaeologyStrategy()
@@ -129,8 +130,8 @@ class TemporalArchaeologyStrategySuite:
 class PlanDecisionContextStrategySuite:
     """Benchmark PlanDecisionContextStrategy throughput."""
 
-    params: list[int] = [10, 100, 500]
-    param_names: list[str] = ["num_fragments"]
+    params: ClassVar[list[int]] = [10, 100, 500]
+    param_names: ClassVar[list[str]] = ["num_fragments"]
 
     def setup(self, n: int) -> None:
         self.strategy = PlanDecisionContextStrategy()