diff --git a/pipeline/scripts/diagnose_compare_gaps.py b/pipeline/scripts/diagnose_compare_gaps.py
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+++ b/pipeline/scripts/diagnose_compare_gaps.py
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+"""Diagnose the three data gaps blocking the compare-screen redesign.
+
+Run from repo root (network access required, no DB needed):
+ uv run --with singer-sdk --with pandas --with requests \
+ python pipeline/scripts/diagnose_compare_gaps.py
+
+(singer_sdk is a transitive import of tap_uk_ees.tap / tap_uk_ofsted.tap and
+is not part of the repo's default environment, hence the `uv run --with`.)
+"""
+import io
+import re
+import sys
+import zipfile
+
+import pandas as pd
+import requests
+
+sys.path.insert(0, "pipeline/plugins/extractors/tap-uk-ees")
+sys.path.insert(0, "pipeline/plugins/extractors/tap-uk-ofsted")
+from tap_uk_ees.tap import ( # noqa: E402
+ _KS2_NATIONAL_COL_MAP,
+ _KS2_NATIONAL_CSV_URL,
+ download_release_zip,
+ get_all_releases,
+)
+from tap_uk_ofsted.tap import discover_csv_url # noqa: E402
+
+TIMEOUT = 120
+
+
+def check_national_gps_science():
+ print("\n=== (a) National catalogue CSV: GPS/science columns ===")
+ resp = requests.get(_KS2_NATIONAL_CSV_URL, timeout=TIMEOUT)
+ resp.raise_for_status()
+ df = pd.read_csv(io.BytesIO(resp.content), dtype=str, keep_default_na=False)
+ df.columns = [c.strip().lower() for c in df.columns]
+ for csv_col in ("pt_gps_exp", "pt_scita_exp", "avg_readscore", "avg_matscore", "avg_gpsscore"):
+ status = "PRESENT" if csv_col in df.columns else "MISSING"
+ print(f" {csv_col}: {status}")
+ gps_like = [c for c in df.columns if "gps" in c or "scita" in c or "sci" in c]
+ print(f" all gps/science-ish columns: {gps_like}")
+ nat = df[df.get("geographic_level", "").str.strip().str.lower() == "national"]
+ print(f" national rows time_periods: {sorted(nat['time_period'].unique())}")
+ # Sample the values our map would read for the latest year
+ latest = nat[nat["time_period"] == nat["time_period"].max()]
+ for csv_col, field in _KS2_NATIONAL_COL_MAP.items():
+ val = latest.iloc[0].get(csv_col, "") if len(latest) else ""
+ print(f" {field} <- {csv_col} = {val!r}")
+
+
+def check_ks2_attainment_years_subjects():
+ print("\n=== (b) EES KS2 attainment: years & subject labels ===")
+ releases = get_all_releases("key-stage-2-attainment")
+ print(f" releases found: {[r['time_period'] for r in releases]}")
+ for release in releases:
+ try:
+ zf = download_release_zip(release["id"])
+ except Exception as e:
+ print(f" {release['time_period']}: DOWNLOAD FAILED: {e}")
+ continue
+ name = next((n for n in zf.namelist()
+ if "ks2_school_attainment_data" in n and n.endswith(".csv")), None)
+ if not name:
+ print(f" {release['time_period']}: NO school attainment CSV in ZIP")
+ print(f" all CSVs in zip: {[n for n in zf.namelist() if n.endswith('.csv')]}")
+ continue
+ with zf.open(name) as f:
+ df = pd.read_csv(f, dtype=str, keep_default_na=False, nrows=200000)
+ years = sorted(df["time_period"].unique())
+ subjects = sorted(df["subject"].unique())
+ print(f" release {release['time_period']}: time_periods={years}")
+ print(f" subjects={subjects}")
+
+
+def check_ofsted_report_card_columns():
+ print("\n=== (c) Ofsted MI CSV: report-card columns ===")
+ url = discover_csv_url()
+ print(f" MI file: {url}")
+ if url is None or not url.lower().endswith(".csv"):
+ print(f" URL is not a CSV (likely ODS) — stopping this section. url={url!r}")
+ return
+ resp = requests.get(url, timeout=TIMEOUT)
+ resp.raise_for_status()
+ df = pd.read_csv(io.BytesIO(resp.content), dtype=str, keep_default_na=False, nrows=5)
+ rc_like = [c for c in df.columns
+ if re.search(r"report card|inclusion|curriculum|achievement|safeguard|well.?being|governance", c, re.I)]
+ print(f" candidate report-card columns ({len(rc_like)}):")
+ for c in rc_like:
+ print(f" - {c!r}")
+ print(f" all columns ({len(df.columns)}):")
+ for c in df.columns:
+ print(f" - {c!r}")
+
+
+if __name__ == "__main__":
+ check_national_gps_science()
+ check_ks2_attainment_years_subjects()
+ check_ofsted_report_card_columns()
+
+
+# FINDINGS 2026-07-12: run via
+# uv run --with singer-sdk --with pandas --with requests \
+# python pipeline/scripts/diagnose_compare_gaps.py
+#
+# (a) National catalogue CSV (GPS/science) — NOT a source-data problem.
+# pt_gps_exp, pt_scita_exp, avg_readscore, avg_matscore, avg_gpsscore are
+# all PRESENT in the catalogue CSV and hold real numeric values for the
+# latest national row (time_period 202425: pt_gps_exp='72.6' ->
+# gps_expected_pct; pt_scita_exp='81.6' -> science_expected_pct).
+# national time_periods present: 201516, 201617, 201718, 201819, 201920,
+# 202021, 202122, 202223, 202324, 202425 (COVID years 201920/202021 are
+# present as rows but suppressed with 'x' per the module docstring, not
+# absent). So _KS2_NATIONAL_COL_MAP is correct and the extractor's own
+# read of the source is fine end-to-end -- the NULLs in
+# marts.fact_ks2_national_averages are NOT caused by a missing/renamed
+# source column. The gap must be introduced downstream of the tap
+# (staging/mart SQL, a stale/incomplete load, or a dbt model not
+# selecting these two columns) -- Task 5/6 should look at the dbt
+# staging model for ees_ks2_national and the mart definition, not the
+# tap/column-map.
+#
+# (b) EES KS2 attainment (school-level, "key-stage-2-attainment" publication)
+# releases found (via get_all_releases): [None, '202425', '202324',
+# '202223', '202122']. The `None` entry is the *current/latest* release
+# (its slug doesn't parse to a 6-digit time_period by _slug_to_time_period,
+# but the CSV inside carries time_period='202425' -- same data as the
+# 202425-labelled release).
+#
+# Only two of the four releases contain a school-level attainment CSV
+# matching "ks2_school_attainment_data*.csv":
+# - release None (latest): HAS IT -> time_periods=['202425']
+# subjects=['Grammar, punctuation and spelling', 'Maths', 'Reading',
+# 'Reading, writing and maths', 'Science', 'Writing']
+# - release 202324: HAS IT -> time_periods=['202324']
+# subjects= same 6 labels as above
+# - release 202223: NO school attainment CSV in ZIP. This
+# release's ZIP instead contains only LA/regional/national/MAT-level
+# files (e.g. ks2_regional_and_local_authority_*, ks2_multi_academy
+# _trusts_*, ks2_national_*); no data/*school*attainment*.csv file
+# exists at all in this release's package. This CONFIRMS the
+# "subject-level 2022/23 is NULL in prod" symptom: the source
+# release literally does not publish a school-level attainment file
+# for 202223 under this filename pattern -- it's not a tap bug.
+# - release 202122: NO school attainment CSV in ZIP. Same
+# situation: ZIP has only LA/regional/national-level files (e.g.
+# ks2_regional_and_local_authority_2016_to_2022_revised.csv,
+# ks2_national_school_characteristics_2016_to_2022_revised.csv);
+# no school-level attainment CSV present. This CONFIRMS "school-level
+# 2021/22 is absent" -- again a genuine source-data absence, not an
+# extractor bug.
+# Implication for Tasks 5/6/7: 202122 and 202223 school-level attainment
+# cannot be backfilled from the "key-stage-2-attainment" EES publication
+# via this filename pattern -- those two years must either be sourced
+# from a different EES dataset/file (e.g. one of the *_school_location_
+# and_pupil_characteristics or *_school_type_and_pupil_characteristics
+# files present in those ZIPs, which may carry school-level rows under a
+# different filename), left NULL with an explicit "source unavailable"
+# note, or backfilled from the legacy DfE "Compare School Performance"
+# wide-format CSVs referenced elsewhere in tap.py. Subject labels to use
+# when a source *is* found for 202324/202425:
+# 'Grammar, punctuation and spelling', 'Maths', 'Reading',
+# 'Reading, writing and maths', 'Science', 'Writing'
+# (Reading, writing and maths spans reading+writing+maths combined --
+# this is the RWM row.)
+#
+# (c) Ofsted MI CSV (report-card columns) — confirmed PRESENT.
+# discover_csv_url() resolved to (as at run time, latest inspections
+# 31 May 2026):
+# https://assets.publishing.service.gov.uk/media/6a27c45be13080622db38815/
+# Management_information_-_state-funded_schools_-_latest_inspections_as_at_31_May_2026.csv
+# This is a real .csv (not .ods) so section (c) ran to completion.
+# Exact report-card column headers (6 grade columns + their paired date
+# columns, all present verbatim, case/spacing exactly as below):
+# 'Safeguarding standards' / 'Safeguarding standards - date of grade'
+# 'Inclusion' / 'Inclusion - date of grade'
+# 'Curriculum and teaching' / 'Curriculum and teaching - date of grade'
+# 'Achievement' / 'Achievement - date of grade'
+# 'Attendance and behaviour' / 'Attendance and behaviour - date of grade'
+# 'Personal development and wellbeing' / 'Personal development and wellbeing - date of grade'
+# 'Leadership and governance' / 'Leadership and governance - date of grade'
+# Plus a related pass/fail-style field:
+# 'Latest OEIF safeguarding is effective?' (note: double space in the
+# header, verbatim from source -- preserve exactly when mapping)
+# These are the new-style "report card" single-word-area grades
+# (introduced alongside the "Attendance and behaviour" split from
+# "Personal development"); they coexist in the same CSV with the legacy
+# 4-judgement OEIF columns ('Latest OEIF overall effectiveness',
+# 'Latest OEIF quality of education', 'Latest OEIF behaviour and
+# attitudes', 'Latest OEIF personal development', 'Latest OEIF
+# effectiveness of leadership and management'). Task 7 should map the 7
+# report-card columns above (grade + date pairs, 6 of them, plus the
+# safeguarding-effective flag) rather than inventing new column names.