feat(convert): add task 05 sqlite converter with variant B keys" -m "- convert: convert_sqlite.py builds tribo.db (composite PKs, enforced FKs, Q1-Q7 indexes, track_summary)

- tools: common/track_summary.py fixes the cross-engine steady-state algorithm
- rules: db-sql-schema records the FK enforcement decision and track_summary definition
- specs: 05 aligned with data-naming-units, simtra_profiles added
- docs: FK key-schema study added under docs/research/, README updated
- replace manual CSV reading with CorpusReader for better data handling
- streamline argument parsing and dependency checks using common pipeline functions
- enhance marker writing for task completion tracking
- remove unused regex and validation error classes for cleaner code

common/track_summary.py

@@ -0,0 +1,48 @@
+"""Per-track steady-state summary (docs/rules/db-sql-schema.md section 5).
+
+Both engines must produce identical values: SQLite calls this function
+during load (task 05); the PostgreSQL materialized view (task 06)
+implements the same three steps in SQL. Plain sequential float arithmetic
+keeps results within the 1e-9 checksum tolerance across implementations.
+"""
+
+from __future__ import annotations
+
+import math
+
+TAIL_START_CYCLE = 501  # steady-state proxy window: cycles 501..N
+
+
+def summarize_track(cofs: list[float]) -> tuple[float, float, int]:
+	"""Return (cof_ss_mean, cof_ss_std, run_in_cycles) for cofs[i] = cof at cycle i+1."""
+	n = len(cofs)
+	tail = cofs[TAIL_START_CYCLE - 1:]
+	m = _mean(tail)
+	s = _stddev_pop(tail, m)
+
+	run_in = TAIL_START_CYCLE - 1  # fallback when no cycle enters the 2-sigma band
+	band = 2.0 * s
+	for c in range(1, n + 1):
+		if abs(cofs[c - 1] - m) < band:
+			run_in = c
+			break
+
+	ss = cofs[run_in:]  # cycles strictly greater than run_in
+	ss_mean = _mean(ss)
+	ss_std = _stddev_pop(ss, ss_mean)
+	return ss_mean, ss_std, run_in
+
+
+def _mean(values: list[float]) -> float:
+	total = 0.0
+	for v in values:
+		total += v
+	return total / len(values)
+
+
+def _stddev_pop(values: list[float], mean: float) -> float:
+	acc = 0.0
+	for v in values:
+		d = v - mean
+		acc += d * d
+	return math.sqrt(acc / len(values))