v0.1.9: per-company progress reviews + deck-quality guidance

progress.py reads the whole graded ledger and answers two questions the
per-deck scorecard can't: is the company actually progressing (composite/
BDEF-category/KPI trajectories, recurring vs resolved flags), and is the
material good enough to judge them by — a deterministic gap engine spots
what the decks are NOT showing (no profitability visibility, untargeted
KPIs, no forward guidance, broken forecast chain, silently dropped KPIs,
thin-evidence BDEF categories, no board asks) and renders each gap as a
concrete, paste-ready request for the next deck.

Served live from the ledger (no GPU) at /api/companies/{slug}/progress(.md),
written to /data/ledger/<slug>/PROGRESS.md + /data/reports/latest-progress.md
after each graded deck, and viewable/downloadable from the dashboard company
card ("View progress review"). 18 new tests.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -120,7 +120,7 @@ Canonical repo: `https://gitea.ten31.ai/Ten31AI/boardroom-map`.
 
 ## Status
 
-**v0.1.8 — live in production.** Deployed on a StartOS box driving a DGX Spark
+**v0.1.9.** Deployed on a StartOS box driving a DGX Spark
 in single-spark air-gapped mode (gemma-4-31B panel: munger-lens /
 girdley-operator / buffett-owner). First full grading run completed
 2026-07-29: a three-deck company history graded end-to-end into a running
@@ -142,7 +142,15 @@ scratch with a different model panel; v0.1.8 added the two-Spark pipeline —
 secondary-Spark models work in air-gapped mode (dual-homed proxy; graders
 keep zero egress), extraction and grading run in parallel when their models
 sit on different Sparks, and single-wave jobs keep the vLLMs warm across
-decks instead of reloading the 31B (~6 min) per deck.
+decks instead of reloading the 31B (~6 min) per deck; v0.1.9 added per-company
+progress reviews — `PROGRESS.md` / "View progress review" reads the whole
+graded ledger for the trajectory (composite deltas, improving/declining BDEF
+categories and KPIs, recurring vs resolved flags) plus a deterministic
+deck-quality gap engine that lists what the materials aren't showing (no
+profitability visibility, untargeted KPIs, no forward guidance, dropped KPIs,
+thin-evidence categories, no board asks) and renders them as a paste-ready
+request list for the next deck (`/api/companies/{slug}/progress(.md)`, no GPU
+needed).
 
 Known optimization not yet done: the wave is torn down per deck, so the 31B
 reloads from disk (~6 min) between decks even when the model set is unchanged.