WS7 of the workflow-redesign initiative required closing 7 known leaks along
the way. Cross-code audit verified ALL 7 were already closed in prior cycles
(FU-1/4/5/6/7 + IA waves); the spec still carried stale "known violation" text.
This PR aligns the spec to actual code state and verifies each closure with a
test. No new code path created — this is a spec-alignment audit, not a re-solve.
7 leaks → status (all closed/gated, each with a test):
1. cross-corpus halachot (#56, GAP-10/FU-4) — cl.source_kind in halacha_filters
(db.py:7516,7519); test_precedent_corpus_isolation.py
2. invisible halacha backlog (GAP-14/FU-5) — health halacha_backlog
(app.py:2352-2364) + extraction_status/halachot_pending
3. no eval harness (GAP-11/FU-5, G8) — scripts/eval_retrieval.py
(P/R/MRR/nDCG vs gold-set+baseline); --self-test ALL PASS
4. export gate not hard-block (GAP-15/FU-6, INV-EX3) — export_docx checks
qa_run_exists + get_critical_qa_failures before exporter (drafting.py:462-494);
test_export_qa_gate.py
5. metadata on internal path (GAP-02/FU-1, INV-ING3) — unified ingest.ingest_document
queues metadata+halacha together (ingest.py:233-234); test_unified_ingest.py
6. DOCX creep to source-of-truth (GAP-17/FU-7, INV-EX1) — active_draft_path is
revision-anchor only; drift caught by cases.blocks_stale flag (V22)
7. UI cache-invalidation (GAP-33/FU-10) — qc.invalidateQueries on mutations
Also added forward-pointer notes for the workflow-redesign additions owned by
sibling tasks (02 documents is_primary/doc_category → #200; 04 re-analysis /
summarize_party_claims / analyze_protocol → #200/#202/#203), kept additive to
avoid line conflicts. block-schema.md interim/template edits deferred to #204/#205.
Invariants: maintains G2 (single source of truth, no parallel path — all leak
closures extend the canonical path), G8 (eval harness now exists), G10 (human
gates visible: halacha backlog + hard export gate), INV-ING3 (unified metadata
queue), INV-EX1 (DOCX is derived; blocks_stale drift flag), INV-RET1/G5 (#56
cross-corpus halacha isolation).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
מנגנון-הייצור (claude_session→claude -p) לא העביר model/effort: BLOCK_CONFIG
הגדיר temp+model per-בלוק שהיו מטא-דאטה-מת (block_writer.py:447 העביר רק
prompt/timeout/tools). הייצור רץ על מודל ברירת-המחדל של ה-CLI עם פרומפטים-חופשיים
→ מקור אי-העקביות בטיוטת-הביניים.
תיקון (עקביות-מבנית, WS5):
- נעיצת model="claude-opus-4-8" (GENERATION_MODEL) לכל בלוקי-ה-AI + effort per-בלוק
שמועבר בפועל דרך claude_session.query → `claude -p --model --effort`.
מפת-effort: ה=medium · ו=medium · ז=high · ח=medium · ט=high · יא=high · י=xhigh.
- ניסוח-פתיחה קבוע לבלוק-ה (תמיד "לפנינו ערר…", סוף ל-OR "עניינה של החלטה זו").
- מבנה-פנימי קבוע לבלוק-ה; סדר-בלוקים בייצוא כבר קבוע (_INTERIM_BLOCK_ORDER).
- הסרת שדות temp/model המתים מ-BLOCK_CONFIG; "temp" deprecated (Opus 4.7/4.8 דוחים
temperature→400). decision_blocks.temperature נשמר=0 לתאימות-עמודה בלבד.
- timeout נגזר מ-effort (לא מפיצול sonnet/opus שכבר לא קיים).
- עדכון endpoint reference /api/settings/mcp/blocks + blocks-tab + McpBlock לחשוף effort.
- ספ: docs/block-schema.md §3 (effort מחליף temperature), docs/spec/06-export.md
(דטרמיניזם-מבני של טיוטת-הביניים).
G2 (מקור-אמת יחיד) — אין מסלול-ייצור מקביל: write_interim_draft מתזמר את אותו
write_and_store_block→write_block של ההחלטה המלאה.
claude_session-local-only — הייצור נשאר דרך גשר-ה-CLI, לא SDK/קונטיינר.
INV-EX1 — סדר-ייצוא דטרמיניסטי נשמר; G11 — עקרונות-הכתיבה ל-12 הבלוקים נשמרים.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Belt-and-suspenders to the plugin sweep (plugin-legal-ai #3). Comments that
arrive mid-run aren't re-read automatically — an agent reads comments once at
run start (§2), so a chair comment that lands after that point and before the
run closes the issue is never seen. That is exactly how a chair comment on
CMP-190 (2026-06-30) was dropped.
Adds a mandatory gate to HEARTBEAT §4ב: before PATCH status=done, re-read
comments and compare to the run-start list. If a new unaddressed chair comment
exists — do not close. Either handle it (and record a comment) or, if it
widens scope, keep the issue open and hand back to the CEO (§4ג). Never mark
done while a chair comment waits unanswered.
The plugin sweep catches orphaned comments within ~2 min; this gate prevents
the drop at the source. Refs legal-ai #164.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The chair (Dafna/chaim) reviewed and approved the full corpus-refresh proposal
generated by scripts/regenerate_voice_fingerprint.py and committed it to the live
voice-fingerprint — satisfying the INV-LRN1 hard chair-gate (a human approved;
the script only ever proposed). +81/-15 lines: all the deletions are reword-and-
expand of existing lines (no curated insight lost), the additions are style/method
refinements grounded in the 56-decision corpus (INV-LRN5 — no case substance):
- §1.2 conditional-acceptance as an "anחנו" verb (מקובל עלינו vs נציין)
- §1.3 ruling variation "ראשית, צודק [צד]... אלא ש"
- §1.5 emphasis always annotated "(הדגשת הח\"מ)"
- §1.8 bounded appraiser-restraint (professional plane only; legal plane = no deference)
- §1.9 inverse twin "ניתן היה לסיים... אלא ש"
- §1.11 quantitative anchoring as a reframing device (cross-type)
- §1.12 measured block ratios (background 17 / claims 27 / discussion 42 / summary 14)
- §1.13 faithful rendering of a party's internal reasoning
- §2.2 two-sided pendulum for the restraint paragraph + expanded party-verb catalog
- §4.8 new template "ניתן היה לסיים — אלא ש"
- §5.x agent instructions updated to reference the new patterns
Invariants: INV-LRN1 (chair approved before commit — the gate held), INV-LRN5
(style/method only, no substance).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The voice-fingerprint (docs/daphna-voice-fingerprint.md) is the PRIMARY abstract
style channel the writer consumes (07-learning §0.2 channel A), but it was
hand-authored and never regenerated from the growing corpus — the "voice" the
writer adapts stayed frozen while finals accumulated. PR #345 unfroze the
structural half (corpus-measured ratios → writer); this is the prose half.
scripts/regenerate_voice_fingerprint.py reads the corpus (style_corpus stats +
a bounded block-level style_exemplars sample + measured section ratios) and asks
Opus (local) to PROPOSE refinements/additions to the fingerprint — STYLE / METHOD
/ VOICE / LEXICON only, grounded in corpus evidence, never case substance
(INV-LRN5), preserving curated insights rather than replacing them.
CHAIR-GATED, HARD (INV-LRN1): the prose profile is voice-knowledge, not a
low-risk style rule, so it does NOT auto-flow. The script writes a PROPOSAL to
data/curator-proposals/ (the existing curator-proposal surface) and NEVER
overwrites the live fingerprint; the chair reviews and hand-commits accepted parts
— the same manual gate the fingerprint already uses. Runs on the host (claude CLI).
Reuses claude_session + measure_corpus_ratios + the curator-proposals dir (G2 —
no parallel synthesis or gate). SCRIPTS.md + spec §0.2 updated.
Invariants: INV-LRN1 (hard chair gate for voice-knowledge prose), INV-LRN5
(style-only synthesis), G2 (reuses existing synthesis inputs + proposal surface).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The writer, learning panels, and legal_mcp tools run from the HOST tree
(Paperclip agents claude -p with cwd=~/legal-ai; pm2 services import from
mcp-server/src) — not the container. A merge + Coolify deploy only refreshes the
FastAPI container; host-side code stayed on the last manual `git pull`. On
2026-06-28 the host was 16 commits behind, so a day of learning-loop fixes sat
dormant until pulled by hand.
host_sync.sh (run every minute via cron, like the existing data/training pull)
fast-forwards the host tree to origin/main when it falls behind. Fast-forward
ONLY — never forces, so the perpetually-dirty .taskmaster/tasks/tasks.json is
never clobbered (clean ff leaves it; an upstream conflict logs + bails for manual
handling). Agent runs spawn fresh and pick up new code with no restart; only the
long-running pm2 services that import app code (chat_service, court_fetch_service,
reaper) are restarted, and only when their own files changed (precise, minimal
churn). Logs to data/logs/host_sync.log.
Cron entry (added on the host, flock-guarded):
* * * * * flock -n /tmp/host_sync.lock bash ~/legal-ai/scripts/host_sync.sh
SCRIPTS.md updated.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Golden ratios were measured from the corpus (style_distance.measure_corpus_ratios)
but never reached the writer — it used only the hardcoded STRUCTURE_GUIDANCE text.
So the structural target was frozen, and drafts kept deviating (e.g. discussion
block 10% vs a 40-47% target) with no corrective signal.
_structure_guidance_with_corpus(outcome, practice_area) now appends the
corpus-measured section ratios for the block's outcome to the structure guidance,
at both writer entry points (write_block + get_block_context) via one shared helper
(G2). Because measure_corpus_ratios reads style_corpus — which grows with every
enrolled final (see exemplar-growth, #343) — the writer's structural target now
reflects Dafna's ACTUAL current distribution and improves as the corpus grows.
Deterministic (no LLM); best-effort.
This is the STRUCTURAL half of the voice-profile-refresh (#7). The remaining half —
LLM-regenerating the abstract voice-fingerprint PROSE from the corpus (chair-gated)
— is a distinct larger initiative, deliberately deferred rather than rushed.
Invariants: G1 (the writer gets the live measured target, not a frozen constant),
G2 (one structure-guidance builder shared by both entry points; reuses the existing
measure_corpus_ratios — no parallel measurement), INV-LRN4 (the corpus the loop
grows now feeds the writer's structural target).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The chair-feedback chain was DEAD: 27 feedback rows captured, 0 ever became a
lesson the writer reads (no weekly-analysis job ever existed). The chair's own
corrections — the most authoritative signal of all, and exactly "learn from every
returned draft" — went nowhere.
decision_lessons can't carry them: it's FK-coupled to a style_corpus row (a signed
final), which a case-in-progress doesn't have. So chair STYLE feedback instead
rides the discussion_rules channel that already reaches the writer for all blocks —
the same path /training promote uses.
- db.append_global_rule: the locked read-modify-write append, extracted from web
`_append_methodology_override` into one shared impl (G2). The web function is now
a thin wrapper that seeds defaults; chair-feedback calls it directly.
- record_chair_feedback (MCP tool): a STYLE-category correction (style/wrong_tone/
wrong_structure) with a lesson_extracted flows immediately to discussion_rules.
The chair IS the gate — no separate approval (INV-LRN1 graduated gate). SUBSTANCE
feedback (missing_content/factual_error/other) is case-specific → recorded only.
Invariants: INV-LRN1 (chair-authored style = highest authority, flows; substance
not auto-flowed), G2 (single append impl shared by promote + feedback), INV-LRN5
(style channel only).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The style-exemplar corpus (channel B — the writer's block-level retrieval of
Dafna's real prose) was FROZEN at the one-time seed backfill: new finals were
enrolled into style_corpus but never broken into exemplars, so the richest style
channel never grew (8137/8126/8174 had 0 exemplars). The writer kept retrieving
only March–April seed paragraphs no matter how many finals were signed.
Extract the per-decision exemplar logic (section→paragraph→Voyage-embed→replace)
into a shared service `legal_mcp.services.style_exemplars.extract_and_store` —
the SINGLE implementation now used by BOTH the one-time backfill and the live
enrollment path (G2; no parallel extractor). `_enroll_final_in_library` calls it
on every final upload (source='internal_committee', the same source the writer's
search_style_exemplars reads). Voyage embeds over REST → container-safe;
best-effort, surfaced in the upload response, never fails the upload.
Effect: every signed final now grows the exemplar corpus, so the writer's
block-level style retrieval improves with each decision — the core "learn from
every decision" fix for channel B. Path A (style_distance_history) will track
whether the larger exemplar pool reduces style-distance over time.
Invariants: G2 (one extraction path shared by backfill + enroll), INV-LRN5
(style/structure prose only — substance routes elsewhere), INV-LRN4 (the
draft↔final loop now feeds the exemplar channel, not just the lesson channel).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The voice-learning panel produced 81 vetted style lessons — all stuck at
review_status='proposed' with 0 ever approved — so the writer (which reads only
'approved') received NONE of them. The system captured learning but never let it
flow. Chair decision (2026-06-28): a GRADUATED gate by content risk.
- STYLE lessons (categories style/structure/lexicon/tabular) the 2/2 panel kept →
created as review_status='approved' → flow to the writer immediately, reversibly
(chair vetoes in /training). The deepseek+gemini panel only emits style_method
and only on 2/2 consensus, so this is exactly the gate's criterion; substance is
already filtered out and skipped, and routes through the strict halacha gate.
- SUBSTANCE (halacha/precedent/fact) stays a HARD chair gate — unchanged.
style_lesson_panel.py: _review_status_for(category) sets the gate explicitly
(approved for _STYLE_CATEGORIES, else proposed); the apply loop passes it to
db.add_decision_lesson (which already accepts review_status — no DB change).
Spec: INV-LRN1 rewritten as the graduated gate (hard for substance; reversible
auto-flow for consensus style — still "under user control" via veto, per
NCSC/CEPEJ); §0.6 updated to match.
A one-time backfill of the 81 existing panel style lessons to 'approved' runs
separately (DB, post-merge).
Invariants: INV-LRN1 (amended — graduated), INV-LRN5 (style-only, substance never
auto-flows), G10 (human control preserved as reversible veto).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The learning loop measured anti-patterns but never corrected them. style_distance
detects markdown headers / bullet lists / mid-paragraph mini-lists (from the
canonical lessons.ANTI_PATTERNS), but the writer only received anti-pattern
guidance if a chair `anti_patterns` override existed in appeal_type_rules — and
none does. So _build_style_context's `if ov:` branch injected nothing, the writer
was never told to avoid them, and drafts kept emitting them (8137: 28 hits, the
worst, newest — anti-patterns were trending UP, not down).
Anti-patterns are structural invariants of Dafna's voice (continuous legal
narrative — no markdown, no bullets), not overridable preferences. So inject the
canonical ANTI_PATTERNS notes ALWAYS, from the same list style_distance measures
against (single source of truth), with any chair additions layered on top. This
closes the measure-but-don't-correct gap: the next draft should show the markdown/
bullet anti-patterns drop, and Path A (style_distance_history) will confirm it.
Invariants: G1 (correct at source — the writer, not a post-hoc stripper), G2
(one canonical anti-pattern list shared by detection and instruction — no parallel
list), INV-LRN4 (closes the feedback half of the draft↔final loop).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
תיקוני-מבנה ללולאת-הלמידה (TaskMaster #157):
1. כרטיס ה-curator (/training טאב "אוצֵר") ספר decision_lessons WHERE source='curator'
— ערך שאף מסלול-קוד לא כתב → תמיד 0. עוצב-מחדש (דרך שער-העיצוב Claude Design, אושר)
להציג ביושר את שלושת ערוצי-ההזנה לכותב: דיסטילציה→appeal_type_rules (180, זורם),
פאנל→decision_lessons (81, ממתין), אוצֵר→source='curator'. get_curator_stats שוכתב.
2. drift ספ↔סוכן: 07-learning.md §1.1 + INV-LRN3 קבעו שהאוצֵר רושם ממצאים כ-decision_lesson
source='curator', אך הסוכן כתב comments בלבד — הממצאים האיכותיים אבדו. נוסף כלי-MCP
record_curator_findings + §A.5b ב-hermes-curator.md (read-only נשמר; הצעה מגודרת-שער).
3. get_recent_decision_lessons(limit=15) חתך בשקט — נוסף WARN על מה שנחתך (חוקה §6);
הפתרון האמיתי = סינתזת-לקחים (TaskMaster #158).
Invariants: מקיים INV-LRN1/G10 (שער-יו"ר), INV-LRN3 (לכידה מובנית), INV-IA2/IA5 (מקור-אמת
יחיד), G12 (leak-guard עובר), G2 (אין מסלול מקביל — אותם מאגרים). פער-מימוש פתוח מתועד:
§A לא רץ אוטומטית על mark-final (pipeline-wake exits) → נדחה לתכנון נפרד.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Documents the prospective held-out methodology shipped in PR #337: why a clean
retrospective held-out is impossible (lessons stored universal/untagged → no
leave-one-out), and how the system instead captures a clean generalization
datapoint at final-upload — before this case's lessons are folded — via a
style_distance snapshot + lesson-pool size into style_distance_history,
surfaced by GET /api/learning/style-distance-history.
Adds §0.7, wires step [7] MEASUREMENT and INV-LRN4 to it. Notes the
change_percent style/content confound and the one-time clean window.
Invariants documented: INV-LRN4 (this is its trend surface), G2 (reuses
style_distance + appeal_type_rules — no parallel metric path).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A clean held-out test of voice learning was no longer runnable: every
final-uploaded case already has its lessons folded, and lessons are stored
universal/untagged so leave-one-out is impossible. Path A makes the test
prospective instead — capture the generalization datapoint at the one moment
it's clean.
On final upload, after the draft↔final pair is created but BEFORE this
case's lessons are folded (folding is a separate manual /training step), we
snapshot style_distance (anti_pattern_total, golden-ratio max-deviation,
change_percent) alongside the current voice-lesson pool size. Because the
draft was written with only the PRIOR pool, each row is a clean "with N
accumulated lessons, our draft on this unseen case scored X" datapoint. As
the pool grows over cases, a downward trend = learning generalizes.
- db: SCHEMA_V45 style_distance_history (append-only) + helpers
voice_lesson_pool_sizes / record_style_distance_snapshot /
get_style_distance_history.
- app: best-effort capture in api_upload_final_decision (never fails the
upload); GET /api/learning/style-distance-history for the trend.
Reuses the existing style_distance service + appeal_type_rules pool — no
parallel metric path. The 8 existing cases are already folded, so the table
starts empty and fills from the next final (their clean window is past).
Invariants: G2 (reuse style_distance/appeal_type_rules — one path),
INV-LRN4 (measure the draft↔final gap; this is its trend surface). LLM-free
(style_distance is deterministic) so it runs in the container.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The two stage indicators in the compose page's "השלמה והעברה" rail
("הרץ למידת-קול" / "הרץ אימות-הלכות") were static placeholder text
translated from mockup 03 and never wired to data — they always read
"ממתין להעלאת הסופי", even after the final was uploaded and both
pipelines completed. (The real status was already shown correctly by
LearningStatusBadges on the case page.)
Wire them to useCaseLearningStatus (/api/cases/{n}/learning-status) — the
same source the drafts-panel badges use (G2). The trailing text is now
derived: "ממתין להעלאת הסופי" until the final is uploaded, then the live
state ("✓ הושלם · 12 לקחים הופקו · 12 הוצעו לאישור" / "✓ הושלם · חולצו 48
· 33 אושרו · 15 נדחו", or running/queued/failed).
Data-binding bugfix only — identical markup/classes (mockup-03 layout
preserved), so no visual redesign; within the design-gate bugfix exception
(chair-approved approach). tsc + eslint clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
When a chair's signed final decision is uploaded it is enrolled into the
precedent library, but _enroll_final_in_library only set deterministic
fields (citation, proceeding_type, date) and copied subject_tags from the
case's subject_categories — which is usually empty. The Gemini metadata
pass (subject_tags / summary / headnote / key_quote) was never triggered,
so the row sat at metadata_extraction_status='pending' with no subject
tags until a drain happened to pick it up. In practice a freshly-enrolled
final showed an empty "תגיות נושא" in the precedent edit UI.
Fix: after enrollment + citation, call the existing reextract_metadata
path inline (G2 — one path, full status lifecycle). It runs on Gemini
Flash over REST (GOOGLE_GEMINI_API_KEY, already in Coolify), so it is
container-safe — unlike the halacha path (claude CLI, host-only).
apply_to_record fills only empty fields, so the deterministic seeds and
any chair-curated subject_categories are preserved. Result surfaced in the
upload response (out["metadata"]); failures logged, upload still succeeds.
Also corrects two now-stale comments: the "Gemini returns no_metadata for
internal decisions" note in the enroll loop, and the "MCP-tool-only path"
docstring on reextract_metadata (true for halacha, not for Gemini REST).
Invariants: G1 (fill at source on enroll, not a read-time workaround),
G2 (reuse reextract_metadata — no parallel extraction path). LLM call is
Gemini REST, not claude_session, so the container LLM-call constraint holds.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
renumber_cases.py step 6 only renamed the Paperclip project, leaving the
case↔issue linkage on the OLD case number. get_case_issues() keys on two
text surfaces the script never touched:
• plugin_state.legal-case-number (value_json) — the authoritative linkage
• issues.title — the '[ערר {cn}] …' tag the title-path lookup matches
Result: after a renumber the issues kept the old number, get_case_issues
returned [], and post-final actions (run-learning / run-halacha) silently
skipped with reason "no_issue" — surfaced in the UI as "לא הופעלה למידה".
Discovered on case 8137-11-24 (renamed from 8137-24).
Now the Paperclip step rewrites all three surfaces (projects.name +
plugin_state + issue titles); inspect_paperclip + the dry-run report show
the linkage-row and issue-title counts that will be rewritten.
The 11-case migration already ran; the matching DB rows were fixed
manually (incl. this commit's logic) for all stale cases. This change is
for any future renumber.
Invariants: G1 (normalize at source — the linkage, not a read-time
workaround). G12 note: this is a one-time host-side migration script that
already connects directly to the Paperclip DB by design (below the platform
port, like the existing projects/MinIO/Gitea surgery) — it does not add a
parallel runtime path through agent_platform_port.py.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
