Add weekly correction-outcome telemetry snapshot script

.gitignore

@@ -175,6 +175,7 @@ Gradata/scripts/*
 !Gradata/scripts/publish-npm.sh
 !Gradata/scripts/cloud/
 !Gradata/scripts/migrate_legacy_scopes.py
+!Gradata/scripts/weekly_correction_snapshot.py
 
 # npm sub-package build outputs (source tracked, outputs ignored)
 Gradata/packages/npm/node_modules/

Gradata/docs/weekly-correction-snapshot.md

@@ -0,0 +1,36 @@
+# Weekly Correction Snapshot
+
+`scripts/weekly_correction_snapshot.py` builds a deterministic JSON summary from newline-delimited JSON (NDJSON) events. This is intended for weekly correction-outcome trend reporting.
+
+## Usage
+
+From file:
+
+```bash
+python scripts/weekly_correction_snapshot.py --input /path/to/events.jsonl
+```
+
+From stdin:
+
+```bash
+cat /path/to/events.jsonl | python scripts/weekly_correction_snapshot.py
+```
+
+## Output schema
+
+The script always emits one compact JSON object with stable key ordering:
+
+- `total_corrections` (int): count of correction events (`event=correction.created` or `kind=correction`)
+- `accepted_graduations` (int): count of accepted graduation outcomes
+- `rejection_count` (int): count of rejected graduation outcomes
+- `acceptance_rate` (float): `accepted_graduations / (accepted_graduations + rejection_count)`, or `0.0` if denominator is zero
+- `top_rule_categories` (list): up to 5 entries sorted by descending count, then category name
+- `skipped_rows` (int): malformed or non-object rows ignored during parsing
+
+`top_rule_categories` entries use:
+
+```json
+{"category":"tone","count":12}
+```
+
+Category normalization is lowercase + trimmed whitespace. Empty/missing categories normalize to `"unknown"`.

Gradata/scripts/weekly_correction_snapshot.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+"""Compute weekly correction/graduation aggregates from NDJSON events."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from collections import Counter
+from typing import Any
+
+
+def _normalize_category(value: Any) -> str:
+    if value is None:
+        return "unknown"
+    normalized = str(value).strip().lower()
+    return normalized or "unknown"
+
+
+def _is_correction(row: dict[str, Any]) -> bool:
+    event = str(row.get("event", "")).strip().lower()
+    kind = str(row.get("kind", "")).strip().lower()
+    return event == "correction.created" or kind == "correction"
+
+
+def _is_graduation_accepted(row: dict[str, Any]) -> bool:
+    event = str(row.get("event", "")).strip().lower()
+    outcome = str(row.get("outcome", "")).strip().lower()
+    accepted_flag = row.get("accepted")
+    status = str(row.get("status", "")).strip().lower()
+    return (
+        event in {"lesson.graduated", "graduation.accepted"}
+        or outcome == "accepted"
+        or accepted_flag is True
+        or status in {"accepted", "graduated"}
+    )
+
+
+def _is_rejection(row: dict[str, Any]) -> bool:
+    event = str(row.get("event", "")).strip().lower()
+    outcome = str(row.get("outcome", "")).strip().lower()
+    accepted_flag = row.get("accepted")
+    status = str(row.get("status", "")).strip().lower()
+    return (
+        event in {"graduation.rejected", "lesson.rejected"}
+        or outcome == "rejected"
+        or accepted_flag is False
+        or status == "rejected"
+    )
+
+
+def parse_rows(lines: list[str]) -> tuple[list[dict[str, Any]], int]:
+    rows: list[dict[str, Any]] = []
+    skipped = 0
+    for raw in lines:
+        line = raw.strip()
+        if not line:
+            continue
+        try:
+            row = json.loads(line)
+        except json.JSONDecodeError:
+            skipped += 1
+            continue
+        if not isinstance(row, dict):
+            skipped += 1
+            continue
+        rows.append(row)
+    return rows, skipped
+
+
+def aggregate(rows: list[dict[str, Any]]) -> dict[str, Any]:
+    total_corrections = 0
+    accepted_graduations = 0
+    rejection_count = 0
+    categories: Counter[str] = Counter()
+
+    for row in rows:
+        if _is_correction(row):
+            total_corrections += 1
+            categories[_normalize_category(row.get("category"))] += 1
+        if _is_graduation_accepted(row):
+            accepted_graduations += 1
+        if _is_rejection(row):
+            rejection_count += 1
+
+    denominator = accepted_graduations + rejection_count
+    acceptance_rate = round(accepted_graduations / denominator, 6) if denominator else 0.0
+
+    top_categories = [
+        {"category": name, "count": count}
+        for name, count in sorted(categories.items(), key=lambda item: (-item[1], item[0]))[:5]
+    ]
+
+    return {
+        "total_corrections": total_corrections,
+        "accepted_graduations": accepted_graduations,
+        "rejection_count": rejection_count,
+        "acceptance_rate": acceptance_rate,
+        "top_rule_categories": top_categories,
+    }
+
+
+def _read_lines(path: str | None) -> list[str]:
+    if path:
+        with open(path, encoding="utf-8") as handle:
+            return handle.readlines()
+    return sys.stdin.readlines()
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(
+        description="Compute correction-outcome aggregates for weekly trend snapshots."
+    )
+    parser.add_argument("--input", help="Path to newline-delimited JSON input file")
+    args = parser.parse_args(argv)
+
+    lines = _read_lines(args.input)
+    rows, skipped_rows = parse_rows(lines)
+    snapshot = aggregate(rows)
+    snapshot["skipped_rows"] = skipped_rows
+
+    json.dump(snapshot, sys.stdout, sort_keys=True, separators=(",", ":"))
+    sys.stdout.write("\n")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

Gradata/tests/test_weekly_correction_snapshot.py

@@ -0,0 +1,76 @@
+from __future__ import annotations
+
+import json
+
+from scripts import weekly_correction_snapshot as snapshot
+
+
+def test_parse_rows_skips_malformed_and_non_object_rows():
+    rows, skipped = snapshot.parse_rows(
+        [
+            '{"event":"correction.created","category":"tone"}',
+            "not-json",
+            '["array-row"]',
+            "",
+            "   ",
+        ]
+    )
+    assert skipped == 2
+    assert len(rows) == 1
+
+
+def test_aggregate_empty_input_has_zero_division_safe_defaults():
+    data = snapshot.aggregate([])
+    assert data["total_corrections"] == 0
+    assert data["accepted_graduations"] == 0
+    assert data["rejection_count"] == 0
+    assert data["acceptance_rate"] == 0.0
+    assert data["top_rule_categories"] == []
+
+
+def test_aggregate_counts_and_top_categories_deterministically():
+    rows = [
+        {"event": "correction.created", "category": "Tone"},
+        {"event": "correction.created", "category": "tone"},
+        {"event": "correction.created", "category": "factual"},
+        {"event": "correction.created", "category": "  PROCESS  "},
+        {"kind": "correction", "category": ""},
+        {"event": "lesson.graduated"},
+        {"event": "graduation.accepted"},
+        {"outcome": "accepted"},
+        {"event": "graduation.rejected"},
+        {"accepted": False},
+    ]
+    data = snapshot.aggregate(rows)
+    assert data["total_corrections"] == 5
+    assert data["accepted_graduations"] == 3
+    assert data["rejection_count"] == 2
+    assert data["acceptance_rate"] == 0.6
+    assert data["top_rule_categories"] == [
+        {"category": "tone", "count": 2},
+        {"category": "factual", "count": 1},
+        {"category": "process", "count": 1},
+        {"category": "unknown", "count": 1},
+    ]
+
+
+def test_main_emits_deterministic_json_with_skipped_rows(capsys, monkeypatch):
+    payload = (
+        '{"event":"correction.created","category":"tone"}\n'
+        '{"event":"lesson.graduated"}\n'
+        '{"event":"graduation.rejected"}\n'
+        "bad-row\n"
+    )
+    monkeypatch.setattr("sys.stdin.readlines", lambda: payload.splitlines(keepends=True))
+    rc = snapshot.main([])
+    assert rc == 0
+    out = capsys.readouterr().out
+    result = json.loads(out)
+    assert result == {
+        "acceptance_rate": 0.5,
+        "accepted_graduations": 1,
+        "rejection_count": 1,
+        "skipped_rows": 1,
+        "top_rule_categories": [{"category": "tone", "count": 1}],
+        "total_corrections": 1,
+    }