Adds new sub-agent "מנהל ידע" (hermes_local adapter) that runs after
each successful export to analyze the final decision and suggest updates
to skills/decision/SKILL.md and lessons. Read-only on case data, write
only on a single comment per run.
- legal-ceo.md: new stage F2 after F (export). Looks up curator by name
in current company, creates async sub-issue, no waiting. Falls back to
silent skip if no curator configured.
- legal-ceo.md: agents table updated with both curator UUIDs (CMP + CMPA).
- hermes-curator.md: role instructions documenting CMP/CMPA split and
what the curator does/does not do.
Stage 1 POC. End-to-end validated on CMP-68 (case 1130-25) with two
substantive findings on style patterns. CMPA agent created with separate
~/.hermes/profiles/curator-cmpa profile (own MEMORY.md focused on
היטל השבחה / פיצויים).
Known gaps to follow up: curator does not auto-close its issue, does
not auto-persist findings to MEMORY.md, comment attribution falls back
to chaim's user (install-key) — these are tracked separately and do
not block validation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three-layer separation: style learning (style_corpus), appeals-committee decisions
(internal_committee), and court rulings (external_upload).
- SCHEMA_V10: chair_name + district columns on case_law and cases, partial indexes
- create_internal_committee_decision() DB upsert function
- search_precedent_library_semantic() now accepts source_kind/district/chair_name params
- search_precedent_library_hybrid() passes through new params
- services/internal_decisions.py: ingest_internal_decision, migrate_from_style_corpus,
migrate_from_external_corpus (identifies rows via source_type='appeals_committee')
- search_internal_decisions() MCP tool (server.py + tools/search.py)
- internal_decision_migrate() MCP admin tool
- Web endpoints: POST /api/internal-decisions/upload, POST /api/internal-decisions/migrate,
GET /api/internal-decisions
- ingest_final_version auto-ingests finalized decisions into internal corpus
- SKILL.md updated: agents now search internal + external in parallel, present separately
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
paperclip-dev is for maintaining the Paperclip codebase itself — not
relevant to legal work. Removed from all 14 agents (was on CMPA mirror).
paperclip-converting-plans-to-tasks helps decompose a plan into assigned
issues. Useful for the planning-heavy agents (CEO, analyst). Now scoped
to those two — removed from the other 5 in CMPA where it had crept in.
Net effect: zero drift on paperclipai/* skills across all 7 master+mirror
pairs. Verified via the new Agents tab dashboard.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two false positives surfaced after the Agents tab went live:
1. status (running/idle/paused) is runtime state, not config — drops in
and out as agents pick up issues. Removed from _DRIFT_FIELDS.
2. desiredSkills compared raw, but local/* and company/* skills carry
per-company hashes/scopes by design (sync_agents_across_companies.py
filters local skills with a warning). Comparing them flags every
master+mirror pair that has any local skill on master.
Now compares only paperclipai/* skills (vendor-shipped, must match).
UI shows an inline note explaining the filter.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Picks up the new GET /api/admin/paperclip-agents endpoint (Task #29) plus
any other endpoint changes accumulated since the last regeneration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After committing the Paperclip gaps refactor, the .bak-pre-* sentinels
served their purpose. Add a wildcard so future similar backups won't be
tracked. Also ignore data/precedent-library/ (binary PDFs, 11MB) and
data/*.db (sqlite caches).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
block-he (פתיחה ניטרלית) was previously emitted only in final decisions.
For interim drafts shown to the chair before ruling, including a neutral
opening helps the chair confirm framing before approving downstream blocks.
Skipped if empty, so legacy cases without block-he are unaffected.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Brings the legal-ai ↔ Paperclip integration in line with the official
Paperclip skill. Net effect: HEARTBEAT.md -47% (370→195 lines), all 14
agents on uniform runtime_config + budget + instructionsBundleMode, and
two cross-company helpers replacing manual SQL.
Highlights:
- HEARTBEAT.md refactor: project-specific only, delegates to the official
paperclipai/paperclip skill (loaded per agent). Adds heartbeat-context
fast-path (§1.7) and PAPERCLIP_WAKE_PAYLOAD_JSON shortcut (§1.5).
- Issue Thread Interactions API: legal-ceo.md now uses
ask_user_questions / request_confirmation / suggest_tasks instead of
free-text comments — gives chair structured UI with idempotency keys.
- pc.sh + paperclip_api.pc_request: every API call goes through helpers
that inject Authorization + X-Paperclip-Run-Id (audit trail).
- sync_agents_across_companies.py: master(CMP)→mirror(CMPA) sync via
Paperclip API, idempotent, with --verify and --apply modes.
- skills/new-company-setup: 11-step blueprint distilling all 11 gaps
into a single onboarding runbook for the next company.
- .taskmaster: 12 tasks covering each gap (one already closed: #29).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Task #29: surfaces all 14 agents (7 roles × 2 companies) in /settings as
master+mirror pairs with drift detection. Replaces ad-hoc psql + script
inspection with a single dashboard.
Backend: GET /api/admin/paperclip-agents — fetches via Paperclip API
(not direct DB), groups by name, computes drift across model/effort/
timeoutSec/maxTurnsPerRun/skills/runtime_config.heartbeat/budget/status.
Frontend: new AgentsTab card-per-pair with side-by-side compare,
drift highlighting, expandable details (skills list + instructions path).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Surface issue_thread_interactions (ask_user_questions / request_confirmation /
suggest_tasks) directly inside legal-ai's case detail feed so the user can
answer agent prompts without switching to Paperclip's UI.
Backend (FastAPI):
- paperclip_client.py: 4 new helpers — get_issue_interactions (DB),
respond_to_interaction / accept_interaction / reject_interaction (REST).
- app.py: extends GET /api/cases/{case_number}/agents to include
`interactions`, and adds POST /api/cases/{case_number}/agents/interaction-response
routing to /respond, /accept, /reject in Paperclip.
- paperclip_client.py: also pulls existing httpx calls onto the centralized
pc_request helper (paperclip_api.py) for consistent auth + run-id headers.
Frontend (web-ui, Next.js 16 + TanStack Query):
- agents.ts: Interaction / InteractionPayload / InteractionStatus types,
useSubmitInteraction mutation hook (invalidates the activity query).
- agent-activity-feed.tsx: InteractionCard renders radio (single) /
checkbox (multi) for ask_user_questions, accept/reject + reason for
request_confirmation, task selection for suggest_tasks. Resolved
interactions show a read-only summary. Cards are interleaved with
comments by created_at, so the feed reads chronologically.
Paperclip auto-wakes the issue assignee on a successful response
(queueResolvedInteractionContinuationWakeup) — no explicit wakeup needed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Radix Tabs defaults dir to 'ltr' if not set explicitly, which broke
RTL inside Tab content (cards flowing left-to-right). Set dir='rtl'
on the Tabs root and translate trigger labels to Hebrew (kept
Paperclip in English as a brand name).
Read-only display of BLOCK_CONFIG from block_writer.py with CREAC role
and JWM functional-purpose annotations per block (sourced from
docs/block-schema.md).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Investigation showed legal-ai container has no INFISICAL_TOKEN and there
is no /legal-ai folder in Infisical — all env vars are stored in Coolify
and injected into os.environ at container start.
- Replace _read_infisical_values with _read_coolify_envs
- New: _coolify_authoritative_value picks among Coolify duplicates
- PATCH writes via Coolify API (upsert by key)
- Drift = Coolify-stored vs container-runtime (common: Coolify edited
without redeploy)
- Response field renamed: infisical_value → coolify_value
- New 'has_duplicates' flag per row when Coolify has multiple entries
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces stub RegistrationsTab with a full read-only view grouped by client.
Handles all 4 states: loading skeleton, fetch error, host_path_unavailable,
empty list, and populated data with per-registration detail rows.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replaces stub ToolsTab with a grouped-by-module grid of clickable tool cards.
Adds ToolDetailDrawer (Sheet) showing name, description, module, source_location,
and params_schema for the selected tool.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add drift-badge, env-var-editor, env-var-row components and replace the
environment-tab stub; install shadcn Switch which was missing.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Extracts Paperclip companies + tag-mappings UI into PaperclipTab component,
adds stub tabs for Environment / Tools / Registrations, and replaces the flat
page.tsx with a shadcn Tabs layout to make room for Tasks 8-10.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add infisical_available flag to _build_env_var_row
- Stabilize error code (no exception text in API response)
- Document raw-comparison safety inline
Adds four helper functions (_infisical_client, _infisical_ctx,
_read_infisical_values, _build_env_var_row) and the /api/settings/mcp/env
endpoint that compares Infisical vs container env vars and reports drift.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- reject non-integer floats in int coerce path
- document masking responsibility on to_public_dict
- use tuple for enum_values (immutable)
- treat empty string as None in normalize_for_compare
Static whitelist of 18 env vars (multimodal, rerank, halacha, general,
credentials, connection) with per-key type coercion, secret masking, and
drift-comparison helpers for the upcoming settings UI.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Settings page extension to view and edit MCP server config (env vars,
tools, client registrations) — hybrid edit model: non-secrets editable
through Infisical, secrets read-only with drift detection vs container.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Global search rows linked to /precedents/<case_law_id> but no route
existed, so clicking a result hit a Next 404 and React threw hydration
error #418. New page reads /api/precedent-library/{id} and shows
metadata, summary/headnote/key_quote, subject tags, and the full
halachot roll-up. "ערוך פרטים" opens the existing PrecedentEditSheet
(no duplicate edit UX).
Extracted ExtractedHalachotSection + ReviewStatusPill from the edit
sheet into a shared component so both surfaces render the same block.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The "ספרייה" tab only exposed approved/total counts in a status pill;
to inspect the actual extracted halachot per case the chair had to use
the global "ממתין לאישור" tab, which only surfaces pending items, or
the MCP tool. Now the per-precedent edit sheet renders a read-only
roll-up of every halacha (approved + pending + rejected) with status
filter tabs and counts. Review actions intentionally stay in the
review tab to avoid duplicate approve/reject UX.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
`rerank.maybe_rerank` calls `base_search(limit=…, **base_kwargs)` on both
the rerank-on and rerank-off paths. Commit 242f668 moved the closure into
hybrid_search.py and renamed its parameter to `limit_inner`, so every call
to `/api/precedent-library/search` raised TypeError 500 regardless of the
VOYAGE_RERANK_ENABLED flag. Sibling `search_documents_hybrid` was unaffected
because it uses `lambda **kw:` which absorbs the kwarg.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Observed 2026-05-03: a `precedent_process_pending(halacha)` run that
chained two precedents (1110/20 → 317/10) succeeded for the first
(9 halachot, 129 chunks) and produced status=`no_halachot` for the
second despite it being a 47KB Supreme Court ruling with rich legal
analysis. A manual single-precedent re-run on 317/10 immediately
extracted 53 halachot. Diagnosis: every chunk's claude_session call
in the back-to-back run silently failed (likely Anthropic rate-limit
storm after the 1110/20 token burn), and the empty list was reported
as "Claude looked and found nothing" — same code path as a real
0-halacha ruling. The user couldn't tell the difference.
Three changes:
1. Surface chunk-level failures (halacha_extractor.py)
`_extract_chunk` now returns `(halachot, succeeded)` so the caller
can count how many chunks crashed. `extract()` uses this to
distinguish:
- `no_halachot` — chunks ran cleanly, Claude found nothing
- `extraction_failed` — ≥50% of chunks crashed AND zero halachot
came back (rate limit, subprocess crash, etc.)
When `extraction_failed`, DB status is left as 'processing' so the
request stays in the queue for the caller to retry — instead of
the old behaviour where it got marked 'completed' and silently
dropped from the queue.
2. Inter-precedent cooldown (precedent_library.py)
`process_pending_extractions` now sleeps 30s between precedents.
Anthropic rate-limits per-org, and back-to-back large rulings
(~4M tokens for 1110/20, immediately followed by another 2-3M)
was the empirical trigger. 30s gives the per-minute counter time
to drain.
3. Auto-retry on extraction_failed (precedent_library.py)
When a precedent comes back as `extraction_failed`, retry once
after a 60s cooldown before giving up. Rate-limit storms are
transient — the manual re-run of 317/10 minutes later succeeded
with 53 halachot and zero chunk failures, confirming a single
retry is sufficient. Only retries `extraction_failed`; never
`no_halachot` (Claude looked and there genuinely is no holding).
The DB status now ends up as 'failed' only after retries are
exhausted, matching the UI's terminal-failure chip.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When Paperclip wakes the CEO and the model issues an mcp__legal-ai__*
call within ~10s of session init, Claude Code sometimes returns
"No such tool available" because the legal-ai MCP server hasn't
finished bringing up its tool catalog yet. Observed twice today on
CMPA precedent-extraction wakeups (sessions 9989fbaf and a9c61801);
the agent fell back to bash + .venv/bin/python and finished the work,
but the race needed fixing on the server side.
Three changes that close the window:
1. Lazy schema init (services/db.py + server.py)
`init_schema()` was awaited inside the FastMCP lifespan, blocking
the `initialize`/`tools/list` handshake until ~10 CREATE TABLE IF
NOT EXISTS statements ran. Under contention (two CEOs waking at
once for different companies) this stretched. Now the lifespan
returns immediately and `get_pool()` runs the schema migrations
exactly once on first DB access, guarded by an asyncio.Lock.
tools/list is answered in milliseconds regardless of DB state.
2. Lazy heavy imports
- services/embeddings.py: voyageai (~450ms) loaded only inside
_get_client()
- services/extractor.py: google.cloud.vision (~550ms) loaded only
inside _get_vision_client() and _ocr_with_google_vision()
These two were being imported at module top from
legal_mcp.tools.documents -> services.processor -> services.{
extractor,embeddings}, so the FastMCP server couldn't even start
responding until both finished. Cold start dropped from 2.7s to
1.17s end-to-end (init + tools/list response).
3. Agent-side warmup + retry guidance (.claude/agents/legal-ceo.md)
Even with a fast server, the model can still race on the very
first call. The precedent-extraction section now tells the CEO
to call workflow_status as a warmup probe and to retry after a
short sleep if it sees "No such tool available", before falling
back to the python bypass.
Also expanded the precedent-tool whitelists on the sub-agents that
delegate halacha/library work (commits 4a9a6b7 + 7ee90dc added the
tools to the MCP server but only the CEO got them in its allowed
list). Added to: legal-researcher (full extraction set), legal-analyst
(library_get/list + halacha review), legal-writer (library lookups +
halacha_review), legal-qa (library_get + halacha_review), and the two
that the CEO was already missing (halacha_review, halachot_pending).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage C of the voyage-upgrades-plan shipped to production on
2026-05-03. The doc now leads with the final state and the two
empirical corrections vs the original plan:
1. Reciprocal Rank Fusion replaces weighted-sum hybrid merge.
voyage-3 cosines (~0.4-0.5) systematically outscale
voyage-multimodal-3 cosines (~0.20-0.25); a weighted sum lets
text dominate even when image is the better signal. RRF is
rank-based and robust to scale differences.
2. Chunker now propagates page_number end-to-end (extractor returns
per-page offsets, chunker tags each chunk by its first character's
page). A retrofit script backfills page_number on existing
document_chunks without re-OCR — uses the stored
documents.extracted_text plus PyMuPDF direct text reads as page
anchors (linear interpolation for OCR-only pages).
Production state on cases 8174-24 + 8137-24: 419 page-image
embeddings, 819 chunks tagged with page_number, MULTIMODAL_ENABLED=true
in Coolify env, hybrid search verified A/B against text-only baseline.
The original stage C plan section is retained below for reference.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The first-pass retrofit re-extracted via extractor.extract_text, which
re-runs Google Vision OCR on scanned pages. OCR is non-deterministic,
so the new text didn't match the chunk content stored in the DB
(produced by the original OCR run) — only ~7% of chunks were located.
New approach (no OCR cost):
1. Use the stored documents.extracted_text from the DB — the exact
text the chunks were produced from, so chunk lookups match.
2. Anchor page boundaries via PyMuPDF direct text reads (free, no
OCR). Pages with usable direct text are anchored by snippet match;
OCR-only pages are linearly interpolated between anchors.
3. Search each chunk in extracted_text using a whitespace-tolerant
helper — needed because the chunker joins paragraphs with single
'\\n' while extracted_text uses '\\n\\n' as page separators.
Verified on 8174-24 (5 docs, 307 chunks) + 8137-24 (9 docs, 512
chunks): 100% chunks tagged, 13s total, $0 cost.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The legacy chunker did not track which PDF page each chunk came from.
Stored chunks had page_number=NULL, which blocked the multimodal
hybrid retriever's text+image boost — it joins (chunk, image) on
(document_id, page_number) and the join could never fire.
This change:
- extractor.extract_text now returns (text, page_count, page_offsets);
page_offsets[i] is the start char offset of page (i+1) in the joined
text. None for non-PDFs.
- chunker.chunk_document accepts an optional page_offsets and tags
each chunk with the page that contains its first character (uses
the existing chunker logic; pages assigned post-hoc by content
search to keep the diff minimal).
- processor.process_document and precedent_library.ingest_precedent
forward page_offsets through the chunker. New uploads now carry
accurate page_number on every chunk.
- Other extract_text callers (tools/documents, tools/workflow,
web/app.py) updated to unpack the third element (ignored).
- scripts/backfill_chunk_pages.py: per-case retrofit. Re-extracts each
PDF (re-OCRs via Google Vision if needed, ~$0.0015/page), computes
page_offsets, and updates page_number on every chunk by content
search. Idempotent; --force re-runs on already-tagged docs.
Forward-only would leave the 419 image embeddings backfilled on
cases 8174-24 + 8137-24 unable to boost their corresponding text
chunks. The retrofit script closes that gap (cost ~$0.60).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previous push to main did not trigger a workflow run; act-runner
went silent after task 112. Empty commit to re-fire the webhook.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Cosine scores in voyage-3 (~0.4-0.5) and voyage-multimodal-3
(~0.2-0.25) live on different scales. The previous weighted-sum
merge let text always dominate — verified empirically: 0 image-only
hits across 7 queries on case 8174-24, image side contributed nothing.
RRF combines by *rank* in each list rather than raw score, robust
to scale differences. Per-item score:
rrf_score = text_weight / (k + text_rank)
+ image_weight / (k + image_rank)
A row that appears in both lists (joined on (id_field, page_number))
gets both terms — surfaced as match_type='text+image'.
After fix on 8174-24 (146 image rows): 2 image-only hits land in
top-5 across all 7 test queries, surfacing actual table/diagram/
signature pages (p12, p13 of שומת המשיבה for 'טבלת השוואת ערכי שומה',
p25 of שומת השגה for 'תרשים גוש וחלקה', etc).
On 8137-24 (273 image rows): 'חישוב היוון של דמי החכירה' goes from
0 baseline results → 5 hybrid results (3 text + 2 image), opening
recall on scanned content the OCR layer misses.
Default MULTIMODAL_TEXT_WEIGHT 0.65 → 0.5 (vanilla RRF) since the
prior 0.65 was tuned for raw cosine scales that no longer apply.
New env knob MULTIMODAL_RRF_K (default 60, standard literature).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage C: per-page image embeddings via voyage-multimodal-3 + hybrid
text+image search. Off by default; enable with MULTIMODAL_ENABLED=true.
- Schema V9: document_image_embeddings + precedent_image_embeddings
(vector(1024), page_number, image_thumbnail_path)
- extractor.render_pages_for_multimodal renders PDF pages at
MULTIMODAL_DPI (144) for embedding + JPEG thumbnails at
MULTIMODAL_THUMB_DPI (96) for UI preview, in one pass
- embeddings.embed_images calls voyage-multimodal-3 in 50-page batches
- services/hybrid_search.py orchestrator: rerank applied to text side
first (rerank-2 is text-only); image side cosine; weighted merge
with text_weight 0.65 (env-tunable); image-only pages surface as
match_type='image' so dense scanned content still appears
- processor.process_document and precedent_library.ingest_precedent
gated by flag — non-fatal on multimodal failure
- scripts/multimodal_backfill.py — idempotent per-case CLI to embed
existing documents without re-extracting text
Validated locally on a 5-page response brief: render 0.31s, embed 8.32s,
hybrid merge surfaces image rows correctly. Production rollout starts
with flag=false (no behavior change), then per-case A/B.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The halacha-review panel was rendering raw slugs (`betterment_levy`,
`rishuy_uvniya`, `compensation_197`) as English badges. Pipe them through
the existing `practiceAreaLabel()` helper so the chair sees
"היטל השבחה", "רישוי ובניה", "פיצויים לפי ס' 197".
All other UI sites (library-list-panel, library-stats-panel,
precedent-edit-sheet) were already using the helper — this was the
sole place left rendering the raw slug.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PATCH /api/halachot/{id} was returning 500 because the row included
``embedding`` as a numpy.ndarray of np.float32, which FastAPI's
jsonable_encoder cannot serialize (vars() and dict() both fail on it).
The bug had been latent — it triggered for the first time today after
the auto-approve batch left only low-confidence halachot for the chair
to review manually, and her first PATCH hit the unserializable response.
Replace ``RETURNING *`` with an explicit column list (everything except
``embedding``). Callers that need the embedding can re-fetch via
``get_halacha`` — but no current caller does.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Halachot extracted by halacha_extractor with confidence >= 0.80 are now
inserted with review_status='approved' instead of 'pending_review' —
they appear in search_precedent_library immediately. Halachot below the
threshold still require manual chair approval.
Threshold tunable via env (HALACHA_AUTO_APPROVE_THRESHOLD), defaults to
0.80. Rationale: 89% of historical extractions (356/400) score 0.80+,
spot-checks confirmed quality, and the manual review backlog was the
single biggest reason rerank-2 was returning passages-only on
ההבחנה-style queries.
After this change + the one-time backfill UPDATE, search now returns
9/10 halachot for "ההבחנה בין השבחה לפיצויים" instead of 0 — and the
top-3 are exact-match rules, not adjacent passages.
Reviewer field records "auto-approved (confidence ≥ X.XX)" with the
threshold value at insert time, for traceability.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
