OS cron fires enrich_wave.sh twice nightly (post 23:00 UTC reset); each wave
caps at ~700 keys (~75% window) via enrichment_wave.py --prepare. Fully
headless: one claude -p per batch via xargs, flock-guarded, idempotent.
DB updated to 9541 activities; .gitignore covers enrichment intermediates.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extraction finished (575/588 chunks; 6 content-filter-blocked, 7 await
re-extraction). DB rebuilt and frozen at 9418 activities — content_keys
are now stable for the enrichment overlay.
Part A (plumbing + UI):
- database.py: name_ro/description_ro/rules_ro/variations_ro, indoor_outdoor,
space_needed, estimated_fields, source_id/source_ids/chunk_key columns;
FTS5 indexes the 4 *_ro columns across CREATE + all 3 triggers; new equality
filters + category counts for both axes.
- activity.py: new fields + bilingual display helpers (get_display_*,
is_estimated, axis displays).
- config_taxonomy.py: INDOOR_OUTDOOR/SPACE_NEEDED enums + normalizers
(None on unrecognised, no fabrication).
- search.py / routes.py / config.py / templates / css: new dropdowns,
RO-primary rendering with "(estimat)" markers and collapsible original
text, and a /source/<id> download route shipped DARK behind
SOURCE_DOWNLOAD_ENABLED (copyright opt-in).
- build_database.py: source_id/chunk_key in dict_to_activity; merge_cluster
unions source_ids without touching enrichment fields.
Part B (enrichment pipeline, built not yet run):
- build_database.py: load_enrichment + apply_enrichment (post-dedup, keyed on
content_key) + --enrichment CLI + stated-vs-estimated QA.
- run_enrichment.py (resumable, --source/--limit pilot scoping, --collect),
ENRICHMENT_PROMPT.md.
Repair: scripts/repair_extractions.py fixes the subagents' systematic
unescaped-ASCII-quote bug with a faithful char-scanner (escapes, never
truncates) + schema validation + a strictly-more-text guard. json_repair was
tried first, truncated silently, and is NOT used. build_database has no repair
dependency.
Tests: tests/test_enrichment.py added; 99 pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
64/588 chunks extracted so far (~1949 activities) but in a fresh
session we should switch the subagent model from Opus to Sonnet —
the task is structured JSON extraction with a fixed schema, no
complex reasoning needed, and Sonnet's 200K context easily fits the
~25k-token prompt and ~20k-token output per chunk. Document captures
the exact resume procedure: pending-chunk discovery, the Agent call
template with model:"sonnet", and the finalization steps
(validate -> build_database -> needs_review bulk merge).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
