The Phase 0 cleanup removed 18 scripts as deprecated, but 6 of them have
unique-capability patterns not yet covered by the modern tooling. Restoring
them as reference patterns, not active scripts.
What's restored and why:
gemini_backfill_question.py
Idempotent corpus-walk + Gemini batch + thread-pool + JSON YAML
round-trip. The "fix one field across thousands of YAMLs" pattern.
To be mined in CORPUS_HARDENING_PLAN.md Phase 5.
gpt_backfill_question.py
OpenAI variant of the above. Cross-provider template.
gemini_cli_generate_questions.py (35K)
BATCHED generation: 12 cells per call with balanced track × area ×
zone × level round-robin. `vault generate` does NOT batch — it calls
once per question. This script's batching pattern is what we want
when generating > 100 questions in bulk.
generate.py (30K)
Coverage-survey-driven generation engine: surveys the corpus, finds
empty cells, generates to fill the emptiest first, stops when
saturated. `vault generate` lacks this auto-balance loop.
gemini_fix_errors.py
Batch error-fixer with hardware-reference grounding (V100 / A100 /
H100 / B200 / T4 specs as ground-truth context). To be mined for
audit_corpus_batched.py --propose-fixes in Phase 5.
deep_verify.py
Claude Opus + extended thinking; SHOWS ITS WORK on every napkin-math
claim. Useful as a tiebreaker on borderline math findings from the
lightweight audit.
Each restored file has a 5-line STATUS comment block at the top
documenting what to adapt before running. DEPRECATED.md is restructured
to make the three categories explicit (removed / preserved-for-adaptation
/ active-migration), and adds an adaptation checklist that applies to
all preserved scripts (replace corpus.json loading, verify SDK pins,
update output paths, re-validate prompts, sample first).
Validation:
vault check --strict — 10,711 loaded, 0 invariant failures
pytest — 74/74
ruff — clean
