Full findings & remediation plan

The complete cross-series breakdown behind the front-page triage. Start with the front page to decide where to focus; come here for the full numbers, every recurring rule, every HIGH-priority lecture, and the ordered remediation plan.

Every table on this page is generated from lectures/data/*.csv by tools/qestyle_report.py, so it cannot disagree with the per-lecture reports.

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What was audited

One commit per series, pinned at audit time. Re-running the pipeline against these commits reproduces every number on this page exactly.

Series	Lectures	Snapshot commit	Snapshot date
lecture-python-intro	56	a12d17c0ef	2026-08-23
lecture-python-programming	27	ceec881028	2026-08-21
lecture-python.myst	145	e25fdf2345	2026-08-24
lecture-python-advanced.myst	68	b83d6da399	2026-08-19
lecture-dp	52	c30490a2f4	2026-08-07

---

Full scoreboard

Average score per category, per series (0–10). Bold marks each series’ weakest category.

#	Series	Lectures	Writing	Math	Code	Figures	References	Links	Admon	Overall	HIGH	MEDIUM	LOW	NONE
1	lecture-python-advanced.myst	68	4.6	5.8	7.3	6.3	9.2	9.2	10.0	7.4	43	0	20	5
2	lecture-python.myst	145	4.5	7.0	7.6	6.5	9.5	9.8	10.0	7.7	81	1	46	17
3	lecture-dp	52	4.7	6.6	7.7	6.4	9.3	9.5	10.0	7.7	34	0	9	9
4	lecture-python-programming	27	4.1	9.0	8.4	7.3	N/A	9.8	9.9	8.0	20	0	5	2
5	lecture-python-intro	56	5.2	8.6	7.3	6.5	9.3	9.7	10.0	8.1	19	0	28	9
	TOTAL / corpus average	348	4.6	7.0	7.5	6.5	9.4	9.6	10.0	7.7	197	1	108	42

See the charts for the visual version.

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Every recurring rule

All rules with at least one violation in the corpus, ranked by how many lectures they reach. Rules tagged (proposed) are documented in the style guide but not yet in the action-style-guide registry.

1. qe-fig-005 — Descriptive figure names for cross-referencing (273 / 348 lectures, 1115 occurrences)

• lecture-python.myst 110 / 145 · lecture-python-advanced.myst 54 / 68 · lecture-python-intro 46 / 56 · lecture-dp 42 / 52 · lecture-python-programming 21 / 27

2. qe-writing-008 — Remove excessive whitespace between words (237 / 348 lectures, 7122 occurrences)

• lecture-python.myst 89 / 145 · lecture-python-advanced.myst 53 / 68 · lecture-dp 40 / 52 · lecture-python-intro 39 / 56 · lecture-python-programming 16 / 27

3. qe-fig-001 — Do not set figure size unless necessary (224 / 348 lectures, 892 occurrences)

• lecture-python.myst 107 / 145 · lecture-python-advanced.myst 47 / 68 · lecture-dp 31 / 52 · lecture-python-intro 30 / 56 · lecture-python-programming 9 / 27

4. qe-fig-008 — Use lw=2 for line charts (196 / 348 lectures, 1194 occurrences)

• lecture-python.myst 69 / 145 · lecture-python-advanced.myst 40 / 68 · lecture-dp 38 / 52 · lecture-python-intro 35 / 56 · lecture-python-programming 14 / 27

5. qe-writing-001 — Use one sentence per paragraph (175 / 348 lectures, 448 occurrences)

• lecture-python.myst 65 / 145 · lecture-python-advanced.myst 42 / 68 · lecture-python-intro 30 / 56 · lecture-dp 23 / 52 · lecture-python-programming 15 / 27

6. qe-fig-003 — No matplotlib embedded titles (165 / 348 lectures, 630 occurrences)

• lecture-python.myst 79 / 145 · lecture-python-advanced.myst 36 / 68 · lecture-dp 30 / 52 · lecture-python-intro 15 / 56 · lecture-python-programming 5 / 27

7. qe-writing-006 — Capitalize lecture titles properly (132 / 348 lectures, 768 occurrences)

• lecture-python.myst 71 / 145 · lecture-python-programming 23 / 27 · lecture-dp 22 / 52 · lecture-python-intro 13 / 56 · lecture-python-advanced.myst 3 / 68

8. qe-math-010 (proposed) — Blackboard \mathbb{P}, \mathbb{E}, \mathbb{V} with braces (124 / 348 lectures, 1608 occurrences)

• lecture-python.myst 60 / 145 · lecture-python-advanced.myst 33 / 68 · lecture-dp 20 / 52 · lecture-python-intro 8 / 56 · lecture-python-programming 3 / 27

9. qe-ref-001 — Use correct citation style (106 / 348 lectures, 291 occurrences)

• lecture-python.myst 36 / 145 · lecture-python-advanced.myst 35 / 68 · lecture-dp 20 / 52 · lecture-python-intro 15 / 56

10. qe-writing-004 — Avoid unnecessary capitalization in narrative text (105 / 348 lectures, 339 occurrences)

• lecture-python.myst 43 / 145 · lecture-python-advanced.myst 24 / 68 · lecture-python-intro 18 / 56 · lecture-dp 14 / 52 · lecture-python-programming 6 / 27

11. qe-link-002 — Use doc links for cross-series references (73 / 348 lectures, 205 occurrences)

• lecture-python-advanced.myst 26 / 68 · lecture-python.myst 20 / 145 · lecture-dp 15 / 52 · lecture-python-intro 9 / 56 · lecture-python-programming 3 / 27

12. qe-code-002 — Use Unicode symbols for Greek letters in code (66 / 348 lectures, 798 occurrences)

• lecture-python.myst 32 / 145 · lecture-python-advanced.myst 17 / 68 · lecture-python-intro 10 / 56 · lecture-dp 7 / 52

13. qe-math-002 — Use \top for transpose notation (63 / 348 lectures, 1597 occurrences)

• lecture-python.myst 24 / 145 · lecture-python-advanced.myst 20 / 68 · lecture-dp 14 / 52 · lecture-python-intro 4 / 56 · lecture-python-programming 1 / 27

14. qe-fig-006 — Lowercase axis labels (60 / 348 lectures, 298 occurrences)

• lecture-python.myst 21 / 145 · lecture-python-advanced.myst 15 / 68 · lecture-dp 12 / 52 · lecture-python-intro 11 / 56 · lecture-python-programming 1 / 27

15. qe-fig-004 — Caption formatting conventions (60 / 348 lectures, 189 occurrences)

• lecture-python.myst 34 / 145 · lecture-python-intro 17 / 56 · lecture-python-advanced.myst 9 / 68

16. qe-math-003 — Use square brackets for matrix notation (46 / 348 lectures, 363 occurrences)

• lecture-python.myst 21 / 145 · lecture-python-advanced.myst 15 / 68 · lecture-dp 6 / 52 · lecture-python-intro 4 / 56

17. qe-fig-002 — Prefer code-generated figures (38 / 348 lectures, 104 occurrences)

• lecture-python-advanced.myst 12 / 68 · lecture-python.myst 10 / 145 · lecture-python-intro 6 / 56 · lecture-dp 5 / 52 · lecture-python-programming 5 / 27

18. qe-math-011 (proposed) — Distribution names in plain letters, not \mathcal / \mathbb (34 / 348 lectures, 134 occurrences)

• lecture-python-advanced.myst 18 / 68 · lecture-python.myst 10 / 145 · lecture-dp 5 / 52 · lecture-python-intro 1 / 56

19. qe-math-004 — Do not use bold face for matrices or vectors (33 / 348 lectures, 584 occurrences)

• lecture-python.myst 18 / 145 · lecture-python-advanced.myst 6 / 68 · lecture-dp 5 / 52 · lecture-python-intro 4 / 56

20. qe-code-004 — Use quantecon Timer context manager (31 / 348 lectures, 144 occurrences)

• lecture-python.myst 15 / 145 · lecture-dp 8 / 52 · lecture-python-advanced.myst 4 / 68 · lecture-python-intro 3 / 56 · lecture-python-programming 1 / 27

21. qe-writing-009 (proposed) — Write “IID” — not “i.i.d.” or “iid” (30 / 348 lectures, 61 occurrences)

• lecture-python.myst 15 / 145 · lecture-python-advanced.myst 10 / 68 · lecture-dp 4 / 52 · lecture-python-intro 1 / 56

22. qe-code-003 — Package installation at lecture top (25 / 348 lectures, 32 occurrences)

• lecture-python.myst 8 / 145 · lecture-dp 6 / 52 · lecture-python-advanced.myst 6 / 68 · lecture-python-programming 3 / 27 · lecture-python-intro 2 / 56

23. qe-math-001 — Prefer UTF-8 unicode for simple parameter mentions, be consistent (15 / 348 lectures, 28 occurrences)

• lecture-python.myst 9 / 145 · lecture-dp 3 / 52 · lecture-python-advanced.myst 1 / 68 · lecture-python-intro 1 / 56 · lecture-python-programming 1 / 27

24. qe-fig-007 — Keep figure box and spines (13 / 348 lectures, 71 occurrences)

• lecture-python-intro 6 / 56 · lecture-python.myst 4 / 145 · lecture-python-advanced.myst 2 / 68 · lecture-python-programming 1 / 27

25. qe-link-001 — Use markdown style links for lectures in same lecture series (12 / 348 lectures, 21 occurrences)

• lecture-python.myst 7 / 145 · lecture-python-advanced.myst 3 / 68 · lecture-python-intro 2 / 56

26. qe-math-005 — Use curly brackets for sequences (7 / 348 lectures, 14 occurrences)

• lecture-python.myst 3 / 145 · lecture-dp 2 / 52 · lecture-python-advanced.myst 1 / 68 · lecture-python-intro 1 / 56

27. qe-code-005 — Use quantecon timeit for benchmarking (7 / 348 lectures, 13 occurrences)

• lecture-dp 3 / 52 · lecture-python.myst 3 / 145 · lecture-python-advanced.myst 1 / 68

28. qe-math-013 (proposed) — Reference equations via {eq}`label` (6 / 348 lectures, 6 occurrences)

• lecture-dp 3 / 52 · lecture-python-advanced.myst 2 / 68 · lecture-python.myst 1 / 145

29. qe-math-006 — Use aligned environment correctly for PDF compatibility (5 / 348 lectures, 14 occurrences)

• lecture-python.myst 3 / 145 · lecture-dp 1 / 52 · lecture-python-advanced.myst 1 / 68

30. qe-math-012 (proposed) — Multiplication via \cdot or juxtaposition, never * (4 / 348 lectures, 6 occurrences)

• lecture-python-intro 2 / 56 · lecture-python-programming 1 / 27 · lecture-python.myst 1 / 145

31. qe-fig-010 — Plotly figures require latex directive (4 / 348 lectures, 4 occurrences)

• lecture-python-advanced.myst 3 / 68 · lecture-python.myst 1 / 145

32. qe-math-008 — Explain special notation (vectors/matrices) (3 / 348 lectures, 3 occurrences)

• lecture-dp 1 / 52 · lecture-python-advanced.myst 1 / 68 · lecture-python.myst 1 / 145

33. qe-math-007 — Use automatic equation numbering, not manual tags (2 / 348 lectures, 2 occurrences)

• lecture-dp 1 / 52 · lecture-python.myst 1 / 145

34. qe-admon-003 — Use tick count management for nested directives (1 / 348 lectures, 2 occurrences)

• lecture-python-programming 1 / 27

35. qe-admon-002 — Use dropdown class for solutions (1 / 348 lectures, 1 occurrences)

• lecture-python-intro 1 / 56

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All HIGH-priority lectures

HIGH = overall ≤ 5.0 or any single in-scope category ≤ 4 (spec §4). In this pass every HIGH lecture was triggered by the category floor; none has an overall at or below 5.0. The Floor column is the weakest category’s score — the thing to fix.

Series	Lecture	Writing	Math	Code	Figures	References	Links	Admon	Overall	Floor
python.myst	cross_product_trick	3.5	3.0	—	—	—	10.0	—	5.5	3.0
advanced	hs_recursive_models	3.0	3.0	—	—	8.5	8.0	—	5.6	3.0
dp	cross_product_trick	4.0	3.0	—	—	—	10.0	—	5.7	3.0
python.myst	qr_decomp	3.0	3.0	7.5	—	—	10.0	—	5.9	3.0
dp	smoothing	3.0	3.0	7.5	5.0	10.0	7.5	—	6.0	3.0
advanced	entropy	3.0	3.0	—	7.0	8.5	8.5	—	6.0	3.0
advanced	smoothing	3.0	3.0	7.5	5.0	10.0	7.5	—	6.0	3.0
python.myst	navy_captain	3.0	6.5	7.5	3.0	—	10.0	—	6.0	3.0
python.myst	two_auctions	3.0	4.5	6.5	3.0	10.0	9.0	—	6.0	3.0
advanced	knowing_forecasts_of_others	3.0	3.0	6.0	9.0	7.5	8.0	—	6.1	3.0
advanced	match_transport	3.0	9.5	4.5	3.0	8.5	8.0	—	6.1	3.0
python.myst	likelihood_var	4.5	3.5	7.5	5.0	—	10.0	—	6.1	3.5
python.myst	prob_matrix	3.0	3.0	5.5	5.0	—	10.0	10.0	6.1	3.0
advanced	five_preferences	3.0	6.0	7.0	4.0	7.0	10.0	—	6.2	3.0
python.myst	two_computation	5.5	3.0	6.0	3.0	10.0	10.0	—	6.2	3.0
dp	tax_smoothing_1	4.5	4.0	7.0	6.0	7.5	9.0	—	6.3	4.0
advanced	markov_jump_lq	5.0	3.0	7.0	5.5	8.5	9.0	—	6.3	3.0
advanced	tax_smoothing_1	4.0	4.5	7.0	6.0	7.5	9.0	—	6.3	4.0
dp	markov_jump_lq	5.0	3.0	7.5	5.5	8.5	9.0	—	6.4	3.0
advanced	asset_pricing_lph	3.0	3.0	5.5	7.5	8.5	7.5	10.0	6.4	3.0
advanced	rob_markov_perf	3.5	4.0	6.5	8.0	9.0	7.5	—	6.4	3.5
advanced	tax_smoothing_2	5.0	3.5	7.5	4.0	8.5	10.0	—	6.4	3.5
python.myst	rs_inventory_q	3.0	6.5	7.5	5.0	—	10.0	—	6.4	3.0
dp	perm_income_cons	3.0	4.0	7.5	5.5	10.0	9.0	—	6.5	3.0
dp	rs_inventory_q	3.0	6.0	8.5	5.0	—	10.0	—	6.5	3.0
dp	tax_smoothing_2	5.5	3.5	7.5	4.0	8.5	10.0	—	6.5	3.5
advanced	black_litterman	3.0	3.0	7.0	4.0	10.0	8.5	10.0	6.5	3.0
python.myst	linear_models	3.0	3.0	6.0	8.0	—	9.0	10.0	6.5	3.0
python.myst	multivariate_normal	3.0	3.0	7.5	5.5	—	10.0	10.0	6.5	3.0
python.myst	perm_income_cons	3.0	4.0	7.5	5.5	10.0	9.0	—	6.5	3.0
dp	cons_news	3.0	4.5	8.5	6.0	10.0	7.5	—	6.6	3.0
dp	lqcontrol	3.0	3.0	7.5	4.5	10.0	8.0	10.0	6.6	3.0
advanced	additive_functionals	5.5	3.5	7.0	3.5	9.0	7.5	10.0	6.6	3.5
advanced	cons_news	3.0	4.5	8.5	6.0	10.0	7.5	—	6.6	3.0
python.myst	likelihood_ratio_process	3.0	3.0	7.0	3.5	10.0	10.0	10.0	6.6	3.0
dp	lagrangian_lqdp	3.0	3.0	7.0	—	10.0	7.5	10.0	6.8	3.0
advanced	amss2	3.5	5.5	7.5	6.0	8.5	10.0	—	6.8	3.5
advanced	dyn_stack	3.5	4.0	7.5	5.0	10.0	7.5	10.0	6.8	3.5
advanced	robustness	3.0	3.0	7.5	6.5	10.0	7.5	10.0	6.8	3.0
python.myst	lagrangian_lqdp	3.0	3.0	7.0	—	10.0	7.5	10.0	6.8	3.0
python.myst	prob_meaning	3.0	7.5	6.0	4.5	—	10.0	10.0	6.8	3.0
dp	ifp_advanced	3.0	3.0	6.5	7.0	8.5	10.0	10.0	6.9	3.0
advanced	cagan_rational_expectations	5.5	3.0	6.0	5.5	8.5	10.0	10.0	6.9	3.0
python.myst	ifp_advanced	3.0	3.0	7.0	7.0	8.5	10.0	10.0	6.9	3.0
python.myst	linear_algebra	3.0	4.5	7.5	5.5	10.0	7.5	10.0	6.9	3.0
python.myst	markov_asset	3.0	4.5	7.5	6.5	8.5	8.0	10.0	6.9	3.0
python.myst	misspecified_recovery	3.0	3.0	5.5	6.5	10.0	10.0	10.0	6.9	3.0
python.myst	multi_hyper	3.5	6.5	7.5	7.0	—	10.0	—	6.9	3.5
python.myst	pandas_panel	3.5	—	7.5	4.5	—	9.0	10.0	6.9	3.5
python.myst	stats_examples	3.0	4.5	7.0	7.0	—	10.0	10.0	6.9	3.0
dp	amss2	3.5	5.5	8.5	6.0	8.5	10.0	—	7.0	3.5
dp	perm_income	3.0	4.0	7.5	6.0	8.5	10.0	10.0	7.0	3.0
advanced	stationary_densities	4.0	6.0	7.0	5.5	9.0	7.5	10.0	7.0	4.0
advanced	subjective_beliefs_business_cycles	3.0	3.0	7.0	7.0	9.0	10.0	10.0	7.0	3.0
python.myst	ols	3.0	7.0	7.5	5.0	7.5	9.0	10.0	7.0	3.0
python.myst	perm_income	3.0	4.0	7.5	6.0	8.5	10.0	10.0	7.0	3.0
python.myst	phillips_lost_conquest	3.5	6.0	7.5	4.5	7.5	10.0	10.0	7.0	3.5
python.myst	wald_friedman_2	3.0	6.0	7.5	5.0	9.0	8.5	10.0	7.0	3.0
dp	discrete_dp	4.0	7.0	6.5	6.0	9.0	7.0	10.0	7.1	4.0
dp	dyn_stack	4.0	5.0	8.5	5.0	10.0	7.5	10.0	7.1	4.0
dp	smoothing_tax	3.5	6.0	7.5	4.0	10.0	9.0	10.0	7.1	3.5
advanced	BCG_incomplete_mkts	3.0	7.5	7.5	4.5	10.0	10.0	—	7.1	3.0
advanced	amss3	3.5	5.5	7.5	5.5	7.5	10.0	10.0	7.1	3.5
advanced	calvo	3.0	5.5	7.5	7.0	8.5	8.0	10.0	7.1	3.0
advanced	smoothing_tax	4.0	5.5	7.5	4.0	10.0	9.0	10.0	7.1	4.0
programming	about_py	3.0	—	10.0	7.5	—	8.0	—	7.1	3.0
python.myst	finite_markov	3.0	3.5	8.0	6.5	10.0	9.0	10.0	7.1	3.0
python.myst	lln_clt	3.5	3.0	6.5	7.0	10.0	10.0	10.0	7.1	3.0
dp	amss3	3.5	5.5	8.5	5.5	7.5	10.0	10.0	7.2	3.5
dp	calvo	3.0	5.5	8.5	7.0	8.5	8.0	10.0	7.2	3.0
dp	inventory_q	4.0	5.5	7.5	6.0	10.0	10.0	—	7.2	4.0
dp	mccall_model	3.0	7.0	6.5	6.0	10.0	8.0	10.0	7.2	3.0
advanced	calvo_machine_learn	3.5	3.0	6.0	8.0	10.0	10.0	10.0	7.2	3.0
advanced	lucas_asset_pricing_dles	5.5	4.0	8.5	7.0	8.5	10.0	—	7.2	4.0
advanced	permanent_income_dles	4.0	7.5	7.5	8.0	8.5	8.0	—	7.2	4.0
advanced	risk_aversion_or_mistaken_beliefs	5.0	3.0	9.0	3.5	10.0	10.0	10.0	7.2	3.0
intro	geom_series	3.0	8.5	7.5	4.0	—	10.0	10.0	7.2	3.0
intro	networks	4.0	6.0	7.0	5.0	8.5	10.0	10.0	7.2	4.0
programming	python_by_example	3.0	9.0	7.5	6.5	—	10.0	7.5	7.2	3.0
python.myst	lq_inventories	3.0	3.0	7.5	7.0	10.0	10.0	10.0	7.2	3.0
python.myst	mccall_model	3.0	7.5	6.0	6.0	10.0	8.0	10.0	7.2	3.0
python.myst	mle	4.5	3.0	7.5	5.5	10.0	10.0	10.0	7.2	3.0
python.myst	sargent_surico	6.5	5.5	4.5	4.0	10.0	10.0	10.0	7.2	4.0
dp	amss	3.5	3.5	8.0	6.0	10.0	10.0	10.0	7.3	3.5
dp	opt_tax_recur	4.0	5.0	8.5	4.5	9.0	10.0	10.0	7.3	4.0
advanced	amss	4.0	4.0	7.0	6.0	10.0	10.0	10.0	7.3	4.0
advanced	gorman_heterogeneous_households	3.0	8.0	5.0	5.0	10.0	10.0	10.0	7.3	3.0
advanced	growth_in_dles	3.0	7.5	7.5	7.0	9.0	10.0	—	7.3	3.0
intro	french_rev	3.0	10.0	7.5	3.0	7.5	10.0	10.0	7.3	3.0
programming	pandas	3.0	—	7.0	6.5	—	10.0	10.0	7.3	3.0
programming	pandas_panel	3.5	—	8.5	4.5	—	10.0	10.0	7.3	3.5
python.myst	ge_arrow	3.0	3.0	7.5	7.5	10.0	10.0	10.0	7.3	3.0
python.myst	opt_transport	3.0	3.0	7.0	8.0	10.0	10.0	10.0	7.3	3.0
python.myst	util_rand_resp	4.0	4.0	7.5	9.5	9.0	10.0	—	7.3	4.0
python.myst	von_neumann_model	3.0	5.0	7.5	7.0	8.5	10.0	10.0	7.3	3.0
dp	calvo_machine_learn	4.0	3.0	6.5	8.0	10.0	10.0	10.0	7.4	3.0
dp	lq_inventories	4.0	3.0	7.5	7.0	10.0	10.0	10.0	7.4	3.0
dp	odu	3.0	9.0	7.5	5.0	9.0	8.0	10.0	7.4	3.0
advanced	BCG_complete_mkts	3.0	7.5	7.0	6.0	10.0	8.0	10.0	7.4	3.0
advanced	classical_filtering	4.5	3.5	10.0	—	8.5	8.0	10.0	7.4	3.5
advanced	doubts_or_variability	4.0	3.0	7.0	9.0	9.0	10.0	10.0	7.4	3.0
advanced	orth_proj	4.0	3.0	10.0	7.0	10.0	8.0	10.0	7.4	3.0
intro	eigen_I	3.5	10.0	7.5	3.5	—	10.0	10.0	7.4	3.5
intro	inflation_history	3.0	10.0	6.0	4.5	8.5	10.0	10.0	7.4	3.0
intro	markov_chains_I	6.0	3.0	7.0	7.5	9.0	9.0	10.0	7.4	3.0
intro	time_series_with_matrices	3.0	6.0	7.5	7.0	10.0	8.0	10.0	7.4	3.0
python.myst	affine_risk_prices	4.5	4.0	8.5	5.5	9.0	10.0	10.0	7.4	4.0
python.myst	blackwell_kihlstrom	3.5	3.0	8.5	7.5	9.0	10.0	10.0	7.4	3.0
python.myst	imp_sample	4.5	4.0	10.0	8.5	—	10.0	—	7.4	4.0
python.myst	likelihood_ratio_process_2	3.0	9.5	7.5	4.0	7.5	10.0	10.0	7.4	3.0
python.myst	markov_perf	4.0	5.0	7.0	6.0	10.0	10.0	10.0	7.4	4.0
python.myst	odu	3.0	9.0	7.5	5.0	9.0	8.5	10.0	7.4	3.0
python.myst	ross_recovery	3.5	5.5	6.5	6.0	10.0	10.0	10.0	7.4	3.5
python.myst	uncertainty_traps	3.0	5.5	7.5	6.5	9.0	10.0	10.0	7.4	3.0
advanced	chang_ramsey	3.0	9.0	8.5	6.0	8.5	10.0	—	7.5	3.0
advanced	hansen_richard_1987	4.0	4.0	5.0	9.5	10.0	10.0	10.0	7.5	4.0
advanced	irfs_in_hall_model	3.0	8.5	7.5	7.0	9.0	10.0	—	7.5	3.0
advanced	lqramsey	4.0	3.0	7.5	8.0	10.0	10.0	10.0	7.5	3.0
programming	jax_intro	3.0	—	7.5	7.0	—	10.0	10.0	7.5	3.0
programming	numpy	3.0	8.0	7.0	7.0	—	10.0	10.0	7.5	3.0
python.myst	inventory_q	3.0	6.0	10.0	6.0	10.0	10.0	—	7.5	3.0
python.myst	measurement_models	3.0	4.0	6.0	9.5	10.0	10.0	10.0	7.5	3.0
python.myst	wald_friedman	3.0	8.5	7.5	4.5	10.0	9.0	10.0	7.5	3.0
dp	mccall_q	3.0	9.5	7.0	7.0	9.0	10.0	—	7.6	3.0
programming	matplotlib	4.0	10.0	7.0	4.5	—	10.0	10.0	7.6	4.0
programming	numba	3.0	7.5	8.5	7.5	—	9.0	10.0	7.6	3.0
python.myst	ak_aiyagari	5.0	10.0	8.0	4.0	8.5	10.0	—	7.6	4.0
python.myst	cass_fiscal	3.0	9.0	7.5	4.0	10.0	10.0	10.0	7.6	3.0
python.myst	hansen_singleton_1983	6.0	3.0	7.0	9.5	10.0	10.0	—	7.6	3.0
python.myst	olg_adaptive_money	3.5	7.5	6.5	6.0	10.0	10.0	10.0	7.6	3.5
python.myst	os_stochastic	3.0	7.0	7.5	7.5	10.0	8.0	10.0	7.6	3.0
dp	chang_ramsey	3.0	10.0	8.5	6.0	8.5	10.0	—	7.7	3.0
dp	os_stochastic	3.0	7.5	8.0	7.5	10.0	8.0	10.0	7.7	3.0
intro	greek_square	4.0	7.5	7.0	6.5	9.0	10.0	10.0	7.7	4.0
programming	names	3.0	—	8.5	7.0	—	10.0	10.0	7.7	3.0
python.myst	ar1_turningpts	3.0	7.5	8.5	8.0	9.0	10.0	—	7.7	3.0
python.myst	cass_koopmans_2	3.0	9.5	8.5	6.0	10.0	7.0	10.0	7.7	3.0
python.myst	long_run_risk_operator	3.0	7.0	7.5	6.5	10.0	10.0	10.0	7.7	3.0
python.myst	phillips_two_stories	3.0	10.0	8.5	5.0	7.5	10.0	10.0	7.7	3.0
advanced	calvo_abreu	4.0	8.5	6.5	9.0	8.5	10.0	—	7.8	4.0
advanced	lu_tricks	3.0	6.5	8.5	8.5	10.0	8.0	10.0	7.8	3.0
intro	inequality	4.0	9.0	6.5	5.0	10.0	10.0	10.0	7.8	4.0
intro	solow	4.0	8.0	7.0	8.0	—	10.0	10.0	7.8	4.0
intro	tax_smooth	3.0	9.5	6.0	6.0	10.0	10.0	10.0	7.8	3.0
programming	scipy	3.0	7.5	8.5	8.0	—	10.0	10.0	7.8	3.0
programming	writing_good_code	3.0	9.5	7.0	7.5	—	10.0	10.0	7.8	3.0
python.myst	back_prop	3.0	7.5	7.5	9.0	—	10.0	10.0	7.8	3.0
python.myst	likelihood_bayes	5.5	4.0	7.5	7.5	10.0	10.0	10.0	7.8	4.0
python.myst	mccall_q	4.0	9.5	7.0	7.0	9.0	10.0	—	7.8	4.0
python.myst	newton_method	4.0	9.5	7.0	6.0	—	10.0	10.0	7.8	4.0
python.myst	re_with_feedback	3.0	8.5	7.0	6.0	10.0	10.0	10.0	7.8	3.0
python.myst	svd_intro	3.0	9.5	7.5	6.5	—	10.0	10.0	7.8	3.0
python.myst	var_dmd	3.0	9.5	—	—	7.5	9.0	10.0	7.8	3.0
dp	ifp_egm_transient_shocks	3.5	9.5	7.5	5.5	9.0	10.0	10.0	7.9	3.5
dp	lqramsey	6.5	3.0	7.5	8.0	10.0	10.0	10.0	7.9	3.0
advanced	repeat_mh	4.0	6.0	7.0	8.5	10.0	10.0	10.0	7.9	4.0
intro	complex_and_trig	3.0	9.5	7.0	5.5	10.0	10.0	10.0	7.9	3.0
intro	laffer_adaptive	4.0	10.0	7.0	6.0	8.5	10.0	10.0	7.9	4.0
intro	money_inflation_nonlinear	3.0	9.5	6.5	6.5	10.0	10.0	10.0	7.9	3.0
intro	unpleasant	3.5	8.0	7.5	6.0	10.0	10.0	10.0	7.9	3.5
python.myst	eig_circulant	3.0	7.5	10.0	7.0	—	10.0	10.0	7.9	3.0
python.myst	exchangeable	3.5	7.0	7.5	7.5	10.0	10.0	10.0	7.9	3.5
python.myst	information_market_equilibrium	4.0	5.5	8.5	7.5	10.0	10.0	10.0	7.9	4.0
python.myst	phillips_drifts_volatilities	3.5	9.5	8.0	4.5	10.0	10.0	10.0	7.9	3.5
python.myst	rational_expectations	3.5	5.0	7.5	10.0	9.0	10.0	10.0	7.9	3.5
python.myst	robust_permanent_income	3.5	9.0	6.5	6.0	10.0	10.0	10.0	7.9	3.5
dp	calvo_abreu	4.0	9.0	7.5	9.0	8.5	10.0	—	8.0	4.0
dp	ifp_egm	3.0	9.0	7.5	6.5	10.0	10.0	10.0	8.0	3.0
dp	jv	3.5	9.5	7.5	6.5	9.0	10.0	10.0	8.0	3.5
intro	lp_intro	3.5	6.5	7.5	8.5	10.0	10.0	10.0	8.0	3.5
intro	msy_fishery	3.5	10.0	6.0	6.5	10.0	10.0	10.0	8.0	3.5
programming	getting_started	3.0	—	10.0	7.0	—	10.0	10.0	8.0	3.0
python.myst	aiyagari_egm	3.0	8.5	10.0	5.5	9.0	10.0	10.0	8.0	3.0
python.myst	cass_koopmans_1	3.0	9.5	8.5	6.0	10.0	9.0	10.0	8.0	3.0
python.myst	ifp_egm_transient_shocks	4.0	10.0	7.5	5.5	9.0	10.0	10.0	8.0	4.0
python.myst	jv	3.0	10.0	7.5	6.5	9.0	10.0	10.0	8.0	3.0
python.myst	marimon_mcgrattan_sargent	4.0	9.5	6.5	7.5	8.5	10.0	10.0	8.0	4.0
programming	python_oop	3.0	10.0	7.5	8.0	—	10.0	10.0	8.1	3.0
programming	sympy	4.0	8.0	8.5	10.0	—	8.0	10.0	8.1	4.0
python.myst	ak2	3.5	10.0	8.5	5.0	10.0	10.0	10.0	8.1	3.5
python.myst	cass_fiscal_2	4.0	10.0	7.5	5.5	10.0	10.0	10.0	8.1	4.0
python.myst	ifp_egm	3.0	9.5	7.5	6.5	10.0	10.0	10.0	8.1	3.0
python.myst	rand_resp	3.5	9.5	7.5	—	10.0	10.0	—	8.1	3.5
python.myst	wealth_dynamics	3.0	9.5	8.0	6.5	10.0	10.0	10.0	8.1	3.0
programming	functions	3.0	10.0	8.5	7.5	—	10.0	10.0	8.2	3.0
programming	oop_intro	4.0	—	9.0	—	—	10.0	10.0	8.2	4.0
programming	need_for_speed	3.0	—	10.0	8.5	—	10.0	10.0	8.3	3.0
programming	numpy_vs_numba_vs_jax	3.0	10.0	8.5	8.5	—	10.0	10.0	8.3	3.0
programming	python_essentials	3.0	10.0	8.5	—	—	10.0	10.0	8.3	3.0
python.myst	house_auction	3.0	10.0	7.0	—	10.0	10.0	10.0	8.3	3.0
intro	observed_distributions	4.0	10.0	7.5	7.0	10.0	10.0	10.0	8.4	4.0
dp	chang_credible	3.0	10.0	8.5	9.5	10.0	10.0	—	8.5	3.0
advanced	chang_credible	3.0	10.0	8.5	9.5	10.0	10.0	—	8.5	3.0
intro	cagan_ree	4.0	10.0	8.5	7.0	10.0	10.0	10.0	8.5	4.0
python.myst	morris_learn	3.0	9.5	8.5	10.0	8.5	10.0	10.0	8.5	3.0
python.myst	os_egm	4.0	9.5	7.0	9.0	10.0	10.0	10.0	8.5	4.0
programming	debugging	3.5	10.0	10.0	9.0	—	10.0	10.0	8.8	3.5

---

Remediation plan

Ordered by return on effort. The first block is mechanical and can be scripted; the second needs a reading pass; the third is small and structural.

1. Scriptable sweeps — do these first

1. Figure names (qe-fig-005) — add mystnb.figure.name metadata to every figure-producing code cell and :name: to every {figure}/{image} directive. Reaches the largest share of the corpus of any single rule and unlocks {numref} cross-referencing.

2. Greek letters in code (qe-code-002) — alpha → α and friends, in code cells only. lecture-python-programming is already fully compliant and is the model.

3. Line widths (qe-fig-008) — add lw=2 to line plots.

4. Figure sizes (qe-fig-001) — drop figsize= overrides and let the series _config.yml defaults apply; keep only where a plot genuinely needs a different aspect.

5. Excess whitespace (qe-writing-008) — collapse runs of spaces between words. The single largest raw count in the corpus, and entirely safe to automate.

6. Heading capitalisation (qe-writing-006) — H2 and below to sentence case. Needs a proper-noun allowlist; tools/qestyle_rules.py already carries one curated from this corpus.

7. Transpose and expectation notation (qe-math-002, qe-math-010 (proposed)) — ' and ^T → ^\top; bare E[·] → \mathbb{E}[·]. Concentrated in the older LQ and filtering lectures of lecture-python-advanced.myst and lecture-dp, so it is best done as one careful pass over that cluster rather than corpus-wide.

8. Cross-series links (qe-link-002) — raw *.quantecon.org URLs → {doc} with the intersphinx prefix.

2. Needs a reading pass

9. Plot titles → captions (qe-fig-003) — ax.set_title(...) moved into the figure caption. Mechanical to find, but each caption has to be written. Titles inside exercise/solution regions are exempt and are already excluded from the counts.

10. Caption conventions (qe-fig-004) — sentence case, six words or fewer. The only rule that got materially worse since the previous pass.

11. Narrative citations (qe-ref-001) — {cite} → {cite:t} where the author name is part of the sentence. Needs judgment on each site.

12. One sentence per paragraph (qe-writing-001) — splitting a paragraph changes its rhythm, so this is an editorial pass, not a sweep.

3. Structural — small and worth doing now

13. The four findings on the front page: two unclosed {exercise-start} fences, two malformed {eq}` ` references, and a raw \label{} inside $$.

14. Shared lectures between lecture-dp and lecture-python.myst. 31 filenames appear in both series, but only 6 are byte-identical at this snapshot: cross_product_trick, ifp_discrete, ifp_opi, lq_inventories, mccall_model_with_separation, os_numerical. Those 6 account for 217 of the corpus’s 18,587 findings — 1.2 % genuinely counted twice, which is the honest size of the double-count. The other 25 share an origin and have diverged, so their findings are about different files even where the defect is the same. For the identical 6, fix upstream and both clear; for the diverged 25, each copy needs its own fix. Worth a decision on whether the corpus totals should de-duplicate the 6 — #3.

---

Corrections to the previous pass

This pass re-measured the corpus with a program rather than by reading, which surfaced several defects in the previous report. They are listed here rather than quietly dropped, because they say something about how a pass should be run.

What the previous pass said	What re-measurement found
divergence_measures.md:134 has \begin{align} inside $$, breaking the PDF build	There is no align inside $$ anywhere in the corpus. That line is a bare top-level \begin{align}, which MyST handles — a convention outlier, not a build break.
299 lectures audited	299 reports were written, but one of them (supply_demand_foundations_v2) describes a lecture that exists in no repository’s history, and two real lecture-dp lectures (inventory_q, rs_inventory_q) were never audited. Net real coverage was 298 of 300.
Per-lecture overall scores	94 of 299 headers did not equal the mean of their own in-scope categories, which is how spec §4 defines the overall score. Discrepancies ran up to 1.2 points.
Per-lecture priority buckets	35 of 299 did not follow spec §4 from their own scores — mostly lectures with a category at 4 that were filed MEDIUM rather than HIGH.
lecture-python.myst is “in great shape — 86% of lectures are clean”	The same series was reported as having qe-writing-006 in 87 of 110 lectures and qe-code-002 in 94 of 110. Both cannot hold under the §4 rubric. Measured consistently, lecture-python.myst sits mid-field, not top.
qe-admon-* clean across lecture-python-programming	python_by_example.md has two unclosed {exercise-start} fences (qe-admon-003, a build-risk rule).

None of these are surprising for a pass done by reading 299 files. They are the reason the mechanical layer now exists, and the reason tools/qestyle_check.py runs as a gate: every row above corresponds to a check that now fails loudly.

---

Coverage and its limits

• 41 of 49 rules are measured by program (36 of the 42 in-scope registry rules plus 5 of the 7 proposed); the 8 judgment-only rules (spec §9) are reviewed by reading. A category scoring 10 means no mechanical violation was measured, not that every rule in it was verified by a human.

• Review coverage is complete in this pass and was absent in the previous one, and scores depend on it. A lecture assessed against more rules scores lower. Every 2026-08 lecture folds in a judgment overlay, so the scoreboard above is comparable across series; the 2026-05 rows of history.csv fold in none (its reviewed column), so score levels are not comparable across the two periods, and the front page says so with the like-for-like figures from history_mechanical.csv — the evidence layer alone, on which the corpus improved. The rule-reach tables are unaffected, because those are measured over every lecture by the same code.

• Three checks are heuristic and say so where they fire: qe-writing-004 and qe-writing-006 depend on curated proper-noun and common-noun lists, and qe-math-002 has to distinguish a transpose apostrophe from a derivative and a ^T transpose from a terminal date. They will need extending as the corpus grows.

• JAX is out of scope, not N/A — the 7 qe-jax-* rules target lecture-jax, which is not part of this corpus.

• Counts are absolute, not per-line. A 2,000-line lecture and a 200-line lecture with the same number of violations of a rule score the same for it, which follows the spec’s severity definitions (§5) rather than any notion of density.