feat(toolkit): add classification and migration

Implement the standard/non-standard/not-macrolactone classification layer
and integrate it into analyzer, fragmenter, and CLI outputs.

Port the remaining legacy package capabilities into new visualization and
workflow modules, restore batch/statistics/SDF scripts on top of the flat
CSV workflow, and update active docs to the new package API.

tests/test_cli.py

@@ -6,7 +6,12 @@ import sys
 
 import pandas as pd
 
-from .helpers import build_ambiguous_smiles, build_macrolactone
+from .helpers import (
+    build_ambiguous_smiles,
+    build_macrolactone,
+    build_non_standard_ring_atom_macrolactone,
+    build_overlapping_candidate_macrolactone,
+)
 
 
 def run_cli(*args: str) -> subprocess.CompletedProcess[str]:
@@ -24,7 +29,10 @@ def test_cli_smoke_commands():
     analyze = run_cli("analyze", "--smiles", built.smiles)
     assert analyze.returncode == 0, analyze.stderr
     analyze_payload = json.loads(analyze.stdout)
-    assert analyze_payload["valid_ring_sizes"] == [16]
+    assert analyze_payload["classification"] == "standard_macrolactone"
+    assert analyze_payload["ring_size"] == 16
+    assert analyze_payload["primary_reason_code"] is None
+    assert analyze_payload["candidate_ring_sizes"] == [16]
 
     number = run_cli("number", "--smiles", built.smiles)
     assert number.returncode == 0, number.stderr
@@ -40,6 +48,55 @@ def test_cli_smoke_commands():
     assert fragment_payload["fragments"][0]["fragment_smiles_labeled"]
 
 
+def test_cli_analyze_reports_non_standard_classifications():
+    hetero = build_non_standard_ring_atom_macrolactone()
+    overlap = build_overlapping_candidate_macrolactone()
+
+    hetero_result = run_cli("analyze", "--smiles", hetero.smiles)
+    assert hetero_result.returncode == 0, hetero_result.stderr
+    hetero_payload = json.loads(hetero_result.stdout)
+    assert hetero_payload["classification"] == "non_standard_macrocycle"
+    assert hetero_payload["primary_reason_code"] == "contains_non_carbon_ring_atoms_outside_positions_1_2"
+    assert hetero_payload["ring_size"] == 16
+
+    overlap_result = run_cli("analyze", "--smiles", overlap.smiles)
+    assert overlap_result.returncode == 0, overlap_result.stderr
+    overlap_payload = json.loads(overlap_result.stdout)
+    assert overlap_payload["classification"] == "non_standard_macrocycle"
+    assert overlap_payload["primary_reason_code"] == "multiple_overlapping_macrocycle_candidates"
+    assert overlap_payload["ring_size"] == 12
+
+
+def test_cli_analyze_csv_reports_classification_fields(tmp_path):
+    valid = build_macrolactone(14)
+    hetero = build_non_standard_ring_atom_macrolactone()
+    input_path = tmp_path / "molecules.csv"
+    output_path = tmp_path / "analysis.csv"
+
+    pd.DataFrame(
+        [
+            {"id": "valid_1", "smiles": valid.smiles},
+            {"id": "hetero_1", "smiles": hetero.smiles},
+        ]
+    ).to_csv(input_path, index=False)
+
+    completed = run_cli(
+        "analyze",
+        "--input",
+        str(input_path),
+        "--output",
+        str(output_path),
+    )
+
+    assert completed.returncode == 0, completed.stderr
+
+    analysis = pd.read_csv(output_path)
+    assert set(analysis["parent_id"]) == {"valid_1", "hetero_1"}
+    assert set(analysis["classification"]) == {"standard_macrolactone", "non_standard_macrocycle"}
+    assert "primary_reason_code" in analysis.columns
+    assert "ring_size" in analysis.columns
+
+
 def test_cli_fragment_csv_skips_ambiguous_and_records_errors(tmp_path):
     valid = build_macrolactone(14, {4: "methyl"})
     ambiguous = build_ambiguous_smiles()