Six-phase upgrade of /training from a read-only dashboard into a full
Style Studio for managing Daphna's style corpus.
- Upload Sheet on /training: file → proofread preview → commit (no more
CLI-only `upload-training` skill).
- Rich corpus metadata: GET /api/training/corpus returns summary, outcome,
key_principles, page_count, parties (regex), legal_citation, lessons_count.
PATCH endpoint for chair edits. CorpusDetailDrawer with 4 tabs (details
/content/lessons/patterns) replaces the bare table row.
- LLM metadata enrichment: style_metadata_extractor + MCP tools
(style_corpus_enrich, style_corpus_pending_enrichment) fill summary
/outcome/key_principles via claude_session (free, host-side).
- Per-decision lessons: new decision_lessons table + 4 REST endpoints +
LessonsTab in drawer; hermes-curator now auto-posts findings as
decision_lessons(source=curator).
- Curator Portrait tab: prompt rendered with link to Gitea, recent
curator findings, style_analyzer training prompts, propose-change
form that writes proposals to data/curator-proposals/ for manual
chair review (no auto-mutation of the agent file).
- Style chat tab: SSE-streamed conversations with the style agent.
New host-side pm2 service (legal-chat-service, port 8770) wraps
claude CLI with stream-json + --resume continuation; FastAPI proxies
via host.docker.internal. Zero API cost — uses chaim's claude.ai
subscription. chat_conversations + chat_messages persist history.
Architecture: keeps the existing rule that claude_session only runs
on the host (not the container). The new legal-chat-service is the
canonical bridge between the container and the local CLI for the chat
feature; everything else (upload, metadata, lessons) stays within the
container's existing capabilities.
Audit script (scripts/audit_training_corpus.py) included for verifying
which corpus rows still need enrichment.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Close out the read-only surface before cutover with three families of
small fixes that the previous phases left unfinished:
- Error boundaries: add src/app/error.tsx (route-segment), global-error.tsx
(root crash fallback with its own minimal html/body — no Providers
dependency since those may be the thing that crashed), and not-found.tsx
for a Hebrew 404 instead of the default Next page.
- Accessibility: wire usePathname() into AppShell so the current nav item
gets aria-current="page" and a gold underline. Add aria-label + aria-hidden
on the icon-only buttons that Phase 5 left text-less (corpus trash,
parties-field Plus). Nav gets an aria-label of its own.
- Metadata template: title on each route now reads "X · עוזר משפטי" via
the layout.tsx title.template. Description localized to Jerusalem.
- README: full E2E smoke test checklist covering all 9 screens, plus a
backend contract table so future phases know which hook wraps which
endpoint. Documents the known Gitea→Coolify webhook issue.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Port the remaining read views from the vanilla UI to Next.js:
- /diagnostics — system health snapshot (DB connected, table counts,
active tasks, failed and stuck documents). Uses the existing
/api/system/diagnostics payload with a 10s refetchInterval so the
page self-updates while the user watches.
- /skills — Paperclip skill inventory with sync status (DB-only,
disk-only, synced, not-synced) as a card grid driven by
/api/admin/skills.
- /training — Dafna's style portrait as three tabs on one page:
* Report: corpus KPIs + CSS conic-gradient subject donut
(SubjectDonut ported from index.html renderHero) + horizontal
anatomy bars + top-12 signature phrases.
* Corpus: TanStack Table of style_corpus rows with an inline
delete mutation (useDeleteCorpusEntry invalidates both the
corpus list and the style report so KPIs update).
* Compare: two-decision selector backed by /api/training/compare,
side-by-side panels plus shared / only-A / only-B pattern
lists.
New API modules: lib/api/system.ts, lib/api/skills.ts,
lib/api/training.ts. All three use TanStack Query with staleTime
profiles tuned per endpoint (10s for diagnostics, 30s for skills,
60s for training reports).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
