feat: 完成P10生产硬化与矩阵收口（P10E1-P10G1）

scripts/validate-feature-matrix.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""Validate docs/feature-matrix.md against execution-contract CSV sources.
+
+Checks:
+- feature-matrix contains exactly one row per ID in CSV fact sources
+- every ID from CSVs appears in matrix
+- matrix does not contain unknown IDs
+- every matrix row status equals DONE
+- every fact-source CSV row is fully DONE on Dev/Review1/Regression axes
+"""
+
+from __future__ import annotations
+
+import csv
+import re
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+MATRIX_PATH = ROOT / "docs" / "feature-matrix.md"
+CSV_FACT_SOURCES = [
+    ROOT / "issues" / "2026-02-09_13-39-02-p5-blueprint-v3.csv",
+    ROOT / "issues" / "2026-02-09_23-30-00-p6-p8-remaining-work-master.csv",
+    ROOT / "issues" / "2026-02-10_09-42-31-p9-maintenance-performance-matrix.csv",
+    ROOT / "issues" / "2026-02-10_10-35-30-p10-production-hardening.csv",
+]
+
+ID_PATTERN = re.compile(r"^[A-Z][0-9][0-9A-Za-z]*$")
+STATUS_INDEX = 6
+
+
+def load_csv_rows(path: Path) -> list[dict[str, str]]:
+    with path.open(encoding="utf-8", newline="") as fp:
+        return list(csv.DictReader(fp))
+
+
+def collect_expected_ids_and_source_status(paths: list[Path]) -> tuple[list[str], list[str]]:
+    ids: list[str] = []
+    source_errors: list[str] = []
+
+    for path in paths:
+        rows = load_csv_rows(path)
+        for row in rows:
+            row_id = (row.get("ID") or "").strip()
+            if not row_id:
+                continue
+            ids.append(row_id)
+
+            dev_status = (row.get("Dev_Status") or "").strip().upper()
+            review_status = (row.get("Review1_Status") or "").strip().upper()
+            regression_status = (row.get("Regression_Status") or "").strip().upper()
+            if not (dev_status == review_status == regression_status == "DONE"):
+                source_errors.append(
+                    f"{path.name}:{row_id} source status not all DONE "
+                    f"(Dev={dev_status}, Review1={review_status}, Regression={regression_status})"
+                )
+
+    return ids, source_errors
+
+
+def load_matrix_rows(path: Path) -> list[list[str]]:
+    rows: list[list[str]] = []
+    for line in path.read_text(encoding="utf-8").splitlines():
+        stripped = line.strip()
+        if not stripped.startswith("|"):
+            continue
+        if stripped.startswith("| ---"):
+            continue
+
+        parts = [part.strip() for part in stripped.strip("|").split("|")]
+        if not parts:
+            continue
+
+        row_id = parts[0]
+        if not ID_PATTERN.match(row_id):
+            continue
+
+        rows.append(parts)
+
+    return rows
+
+
+def main() -> int:
+    errors: list[str] = []
+
+    if not MATRIX_PATH.exists():
+        errors.append(f"Missing matrix file: {MATRIX_PATH}")
+
+    for source in CSV_FACT_SOURCES:
+        if not source.exists():
+            errors.append(f"Missing csv file: {source}")
+
+    if errors:
+        for err in errors:
+            print(f"[ERR] {err}")
+        return 1
+
+    expected_ids, source_status_errors = collect_expected_ids_and_source_status(CSV_FACT_SOURCES)
+    matrix_rows = load_matrix_rows(MATRIX_PATH)
+
+    matrix_ids = [row[0] for row in matrix_rows]
+    expected_id_set = set(expected_ids)
+    matrix_id_set = set(matrix_ids)
+
+    missing_in_matrix = sorted(expected_id_set - matrix_id_set)
+    extra_in_matrix = sorted(matrix_id_set - expected_id_set)
+
+    if missing_in_matrix:
+        errors.append(f"IDs missing in matrix: {missing_in_matrix}")
+    if extra_in_matrix:
+        errors.append(f"Extra IDs in matrix: {extra_in_matrix}")
+
+    duplicate_matrix_ids = sorted({item for item in matrix_ids if matrix_ids.count(item) > 1})
+    if duplicate_matrix_ids:
+        errors.append(f"Duplicate IDs in matrix: {duplicate_matrix_ids}")
+
+    if len(matrix_ids) != len(expected_ids):
+        errors.append(f"Row count mismatch: matrix={len(matrix_ids)} expected={len(expected_ids)}")
+
+    duplicate_expected_ids = sorted({item for item in expected_ids if expected_ids.count(item) > 1})
+    if duplicate_expected_ids:
+        errors.append(f"Duplicate IDs in fact-source CSVs: {duplicate_expected_ids}")
+
+    for row in matrix_rows:
+        if len(row) <= STATUS_INDEX:
+            errors.append(f"Malformed matrix row (too few columns): {row}")
+            continue
+        status = row[STATUS_INDEX].upper()
+        if status != "DONE":
+            errors.append(f"Non-DONE status in matrix for {row[0]}: {row[STATUS_INDEX]}")
+
+    errors.extend(source_status_errors)
+
+    if errors:
+        print("[ERR] feature-matrix validation failed")
+        for err in errors:
+            print(f"  - {err}")
+        return 1
+
+    source_names = ", ".join(path.name for path in CSV_FACT_SOURCES)
+    print(
+        f"[OK] feature-matrix matches fact sources ({source_names}) "
+        f"(IDs/status/count)"
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())