feat: Oracle Canvas Component Schema and Qwen 3.6 integration (#31)

Co-authored-by: Sagnik <sagnik7896@gmail.com>
Reviewed-on: #31

.Agent Context/Oracle Canvas Codebook Production Truth.md

@@ -0,0 +1,209 @@
+# Oracle Canvas Codebook Production Truth
+
+Date: 2026-04-19  
+Repo: `Project_Velocity`
+
+## Purpose
+
+This document freezes the current production truth for the Oracle Canvas template/codebook system, the expanded GPT and Claude corpora, the runtime merge policy, and the current rendering limits that matter for delivery.
+
+This is not a concept note. It is the implementation-facing truth for the Oracle template layer as it exists now.
+
+## Current Source Of Truth
+
+The Oracle template book is split across three layers:
+
+1. Structural database schema
+   - `backend/oracle/schema_extension_v2.sql`
+   - Defines:
+     - `oracle_template_chapters`
+     - `oracle_template_subchapters`
+     - `oracle_template_seed_examples`
+     - chapter/subchapter linkage on `oracle_component_templates`
+     - `oracle_synthetic_generation_jobs`
+
+2. Runtime seed DB
+   - `backend/oracle/oracle_template_seed_db.json`
+   - This is the lightweight fallback DB shipped with the runtime.
+   - It is structurally correct but incomplete relative to the intended corpus.
+
+3. Expanded authoring corpora
+   - GPT pack:
+     - `Project_Velocity/.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/db/oracle_template_seed_db_expanded_v1.pretty.json`
+   - Claude pack:
+   - `Project_Velocity/.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/Claude Sonnet 4.6/oracle_template_expansion/oracle_template_seed_db_expanded.json`
+
+4. Frozen runtime merge artifact
+   - `backend/oracle/oracle_runtime_codebook_merged.json`
+   - This is the deploy-safe merged corpus generated from the GPT and Claude packs.
+   - Production should prefer this file over the authoring packs whenever it is present.
+
+## Corpus Status
+
+The expanded corpora are materially useful and production-relevant.
+
+### GPT 5.4 pack
+
+- Chapters: `6`
+- Subchapters: `24`
+- Seed examples: `1200`
+- Shape: already close to runtime needs
+- Key field for examples: `seed_examples`
+
+### Claude Sonnet 4.6 pack
+
+- Chapters: `6`
+- Subchapters: `24`
+- Examples: `1200`
+- Key field for examples: `examples`
+- Shape: close, but requires normalization into runtime form
+
+### Runtime fallback pack
+
+- Chapters: `6`
+- Subchapters: `24`
+- Seed examples declared in metadata: `36`
+- Seed examples physically present: lower than metadata
+- Useful only as a fallback, not as the primary production corpus
+
+## Super Codebook Policy
+
+The current runtime now treats the codebook as a merged corpus rather than a single-file static DB.
+
+The merge policy is:
+
+1. Load GPT pack first.
+2. Load Claude pack second.
+3. Load runtime fallback pack last.
+4. Normalize all example records to one runtime contract.
+5. Deduplicate by:
+   - `subchapter_id`
+   - `template_name`
+   - `title`
+6. Prefer in this order:
+   - GPT 5.4 examples
+   - canonical examples
+   - fallback records only when no richer example exists
+
+This behavior is implemented in:
+
+- `backend/oracle/codebook_service.py`
+- `backend/scripts/build_oracle_runtime_codebook.py`
+
+That file is now the effective runtime “super codebook” layer.
+
+The generated runtime artifact currently contains the merged deployable corpus and is suitable for Linux-box deployment without requiring `.Agent Context` lookups at request time.
+
+## What The Runtime Actually Uses
+
+The runtime no longer needs to rely on hardcoded template lists in the Oracle v1 router.
+
+The codebook service now provides:
+
+- merged corpus loading
+- search over both corpora
+- normalized template listing
+- best-match template synthesis from a user prompt
+
+Primary runtime functions:
+
+- `codebook_service.stats()`
+- `codebook_service.list_templates(...)`
+- `codebook_service.search_examples(prompt, limit=...)`
+- `codebook_service.synthesize_template(prompt, data_shapes=...)`
+
+## Current Supported Runtime Output Families
+
+The expanded corpora include more component types than the current frontend renderer supports directly.
+
+The current production-safe strategy is:
+
+1. keep the full codebook corpus
+2. map high-variety codebook component families into a smaller supported runtime renderer set
+3. let Oracle render reliably today instead of failing on unsupported component types
+
+### Supported runtime renderers today
+
+- `textCanvas`
+- `kpiTile`
+- `barChart`
+- `lineChart`
+- `geoMap`
+- `table`
+- `pipelineBoard`
+- `timeline`
+- `activityStream`
+- `errorNotice`
+
+### Codebook-to-runtime normalization policy
+
+Examples:
+
+- `summary_card`, `summary_strip`, `metric_card_group`, `gauge_stack`
+  - mapped to `kpiTile`
+- `lead_profile_card`, `property_card`, `data_table`, `leaderboard_table`, `matrix_grid`
+  - mapped to `table`
+- `interaction_timeline`, `message_thread_summary`
+  - mapped to `activityStream`
+- `heatmap`
+  - mapped to `geoMap`
+
+This is deliberate. It keeps the UI stable while preserving the larger design vocabulary inside the template book.
+
+## What Is Production-Ready Now
+
+- Oracle template DB schema exists.
+- Oracle template taxonomy APIs exist.
+- Expanded GPT and Claude corpora are available locally in the repo.
+- Runtime codebook merge and retrieval is implemented in `codebook_service.py`.
+- A frozen merged runtime codebook now exists at `backend/oracle/oracle_runtime_codebook_merged.json`.
+- Oracle v1 template listing/synthesis is being moved to the codebook-backed path.
+- Oracle backend can now emit `textCanvas` planning blocks and the frontend has a renderer for them.
+
+## What Is Still Constrained
+
+- The runtime is not yet rendering all 47+ component families natively.
+- The current system uses safe projection into supported runtime renderers.
+- The template taxonomy routes existed, but were incorrectly using `user.role` as `tenant_id`; that has been corrected toward a fixed Oracle tenant policy.
+- The lightweight fallback JSON DB remains incomplete and should not be treated as the main corpus.
+
+## What Nemoclaw / Oracle Should Use For Retrieval
+
+The correct order for Oracle prompt handling is:
+
+1. Parse prompt.
+2. Retrieve matching codebook examples from the merged corpus.
+3. Build a safe retrieval plan against allowed DB datasets.
+4. Query live CRM/intelligence/inventory datasets.
+5. Build Oracle Canvas JSON with supported runtime component types.
+6. Append to the existing canvas.
+
+The codebook is not the final UI payload by itself.
+
+It is the reference layer that guides:
+
+- component family selection
+- chapter/subchapter intent
+- layout direction
+- data-shape expectations
+- policy hints
+- backend contract hints
+
+## Recommended Near-Term Hardening
+
+1. Materialize a generated runtime codebook file if Linux deployment should not depend on `.Agent Context`.
+2. Add explicit metadata versioning to the merged corpus.
+3. Add a small admin endpoint for codebook stats and source summary.
+4. Expand renderer coverage incrementally rather than trying to support all component families at once.
+5. Add a batch offline export path if the team wants a frozen deploy artifact.
+
+## Operator Bottom Line
+
+The Oracle “book with chapters and JSON schema examples” is real and already useful.
+
+The correct production interpretation is:
+
+- DB schema and APIs are already present
+- GPT and Claude expansion packs are the real high-value corpus
+- `backend/oracle/codebook_service.py` is the runtime super-codebook layer
+- Oracle should retrieve from this merged corpus first, then query live DB data, then render supported JSON Canvas components

.Agent Context/Oracle Canvas Runtime and Ollama Batch Architecture.md

@@ -0,0 +1,382 @@
+# Oracle Canvas Runtime and Ollama Batch Architecture
+
+Date: 2026-04-19  
+Repo: `Project_Velocity`
+
+## Purpose
+
+This document defines the current production Oracle Canvas runtime path, the intended Ollama/Nemoclaw model-routing strategy, and the target batch-processing API shape the team can use if Velocity exposes Oracle or coding-agent capabilities through the local model stack.
+
+This is the operator and engineering artifact. It exists to remove ambiguity.
+
+## Runtime Topology
+
+### Linux origin box
+
+Role:
+
+- hosts Velocity frontend
+- hosts FastAPI backend
+- hosts PostgreSQL and application services
+- terminates app-origin requests under the public site path
+
+Primary concern:
+
+- application routing
+- auth/session enforcement
+- Oracle API execution
+- CRM/intelligence/inventory data access
+
+### GPU box
+
+Role:
+
+- hosts ComfyUI
+- hosts heavy model runtime
+- hosts Ollama / Nemoclaw execution plane
+- stores runtime/model payloads on NVMe only
+
+Primary concern:
+
+- inference
+- media generation
+- model serving
+- agent runtime workloads
+
+### Ingress
+
+Role:
+
+- stable public entry for GPU-backed services
+- hides raw GPU host details from application code
+
+Non-negotiable rule:
+
+- never wire Oracle or frontend code to a raw GPU public IP
+
+## Oracle Canvas Current Execution Path
+
+The production-safe Oracle path is now:
+
+1. User submits prompt from Oracle Canvas frontend.
+2. Frontend calls:
+   - `/api/oracle/v1/canvas-pages/{page_id}/prompts`
+3. FastAPI Oracle orchestrator:
+   - loads user context
+   - retrieves best codebook matches
+   - builds a safe retrieval plan
+   - queries approved datasets from PostgreSQL
+   - produces JSON Canvas components
+   - commits a page revision
+4. Frontend reloads/reconciles the canvas state and renders the new blocks.
+
+## Current Oracle Backend Families
+
+### Live today
+
+- `/api/oracle/v1/me`
+- `/api/oracle/v1/canvas-pages/{page_id}`
+- `/api/oracle/v1/canvas-pages/{page_id}/prompts`
+- `/api/oracle/v1/canvas-pages/{page_id}/forks`
+- `/api/oracle/v1/canvas-pages/{page_id}/rollback`
+- `/api/oracle/v1/canvas-pages/{page_id}/revisions`
+- `/api/oracle/v1/component-templates`
+- `/api/oracle/v1/component-templates/synthesize`
+- `/api/oracle/v1/merge-requests`
+- `/api/oracle/v1/merge-requests/{mr_id}/review`
+- `/ws/oracle/canvas/{page_id}`
+
+### Template taxonomy routes
+
+- `/api/oracle/template-chapters`
+- `/api/oracle/template-subchapters`
+- `/api/oracle/component-templates`
+- `/api/oracle/component-templates/{id}`
+- `/api/oracle/component-templates/{id}/seed`
+- `/api/oracle/component-templates/synthetic-jobs`
+
+## Prompt Analysis Path
+
+Oracle should not rely on one monolithic LLM call.
+
+The correct production split is:
+
+1. codebook retrieval
+2. safe dataset selection
+3. optional LLM planning
+4. live DB fetch
+5. JSON Canvas synthesis
+6. revision commit
+
+### Why this split is correct
+
+- It reduces hallucination in UI structure.
+- It keeps DB access whitelisted and auditable.
+- It allows Oracle to keep working even when the LLM runtime is degraded.
+- It keeps the Oracle Canvas deterministic enough for operational use.
+
+## Current Model Routing Truth
+
+### Present reality
+
+The current Oracle backend has these runtime modes:
+
+- `codebook_retrieval`
+  - preferred when the prompt clearly matches the Oracle template corpus
+- `nemoclaw_hosted`
+  - used when `NEMOCLAW_API_URL` and `NEMOCLAW_API_KEY` are configured and reachable
+- `deterministic_fallback`
+  - used when the LLM planner is unavailable
+
+### What Nemoclaw currently means in code
+
+Current dispatch abstraction:
+
+- `backend/services/nemoclaw_runtime.py`
+
+This file is still a light dispatch envelope, not a fully featured provider router.
+
+### Recommended production provider stack
+
+Provider order:
+
+1. codebook retrieval layer
+2. Nemoclaw planner endpoint
+3. local Ollama fallback
+4. deterministic fallback
+
+## Recommended Ollama Model Policy
+
+### Default planning / Oracle analysis model
+
+Use a local reasoning-capable model behind Ollama when Nemoclaw is not available or when the team wants deterministic private execution.
+
+Recommended candidate:
+
+- `qwen3.6:35b-a3b`
+
+Reason:
+
+- strong agentic coding and structured reasoning profile
+- local execution path through Ollama
+- realistic fit for GPU-box-hosted inference
+
+### Deployment command
+
+Example:
+
+```bash
+ollama run qwen3.6:35b-a3b
+```
+
+### Routing rule
+
+- Oracle prompt planning:
+  - small to medium prompts: local Ollama `qwen3.6:35b-a3b`
+  - larger multi-step analytical plans: Nemoclaw planner if available
+- Coding-agent batch workloads:
+  - Ollama first for local/private jobs
+  - Nemoclaw for heavier orchestration when the runtime is healthy
+
+## Runtime LLM API
+
+The backend now exposes a first-class runtime LLM family:
+
+- `GET /api/runtime/llm/providers`
+- `POST /api/runtime/llm/chat`
+- `POST /api/runtime/llm/batch`
+- `GET /api/runtime/llm/jobs/{job_id}`
+- `GET /api/runtime/llm/jobs/{job_id}/results`
+
+This router is mounted in:
+
+- `backend/api/routes_runtime_llm.py`
+
+The current persistence path uses the existing canonical table:
+
+- `workflow_agent_runs`
+
+That means batch jobs are now persisted against the live Velocity schema without requiring a new table family before the first production rollout.
+
+## Implemented Batch Processing API
+
+This is no longer only a proposal. The following contract family exists now and can be used by Oracle or future coding-agent surfaces.
+
+### Single request inference
+
+- `POST /api/runtime/llm/chat`
+
+Payload:
+
+```json
+{
+  "provider": "ollama",
+  "model": "qwen3.6:35b-a3b",
+  "system_prompt": "You are Oracle Planner.",
+  "messages": [
+    { "role": "user", "content": "Build a CRM pipeline view for high-intent NRI buyers." }
+  ],
+  "temperature": 0.2,
+  "response_format": "json"
+}
+```
+
+### Batch submission
+
+- `POST /api/runtime/llm/batch`
+
+Payload:
+
+```json
+{
+  "provider": "ollama",
+  "model": "qwen3.6:35b-a3b",
+  "job_type": "oracle_canvas_planning",
+  "items": [
+    {
+      "request_id": "req_001",
+      "messages": [
+        { "role": "user", "content": "Show overdue high-QD follow-ups." }
+      ],
+      "response_format": "json"
+    },
+    {
+      "request_id": "req_002",
+      "messages": [
+        { "role": "user", "content": "Build a Kolkata luxury inventory comparison block." }
+      ],
+      "response_format": "json"
+    }
+  ]
+}
+```
+
+### Batch status
+
+- `GET /api/runtime/llm/jobs/{job_id}`
+
+Response:
+
+```json
+{
+  "job_id": "job_123",
+  "status": "running",
+  "provider": "ollama",
+  "model": "qwen3.6:35b-a3b",
+  "submitted_count": 2,
+  "completed_count": 1,
+  "failed_count": 0
+}
+```
+
+### Batch results
+
+- `GET /api/runtime/llm/jobs/{job_id}/results`
+
+### Providers inventory
+
+- `GET /api/runtime/llm/providers`
+
+Example response:
+
+```json
+{
+  "providers": [
+    {
+      "id": "nemoclaw",
+      "status": "online",
+      "models": ["nemotron", "remote_default"]
+    },
+    {
+      "id": "ollama",
+      "status": "online",
+      "models": ["qwen3.6:35b-a3b"]
+    }
+  ]
+}
+```
+
+## Batch Processing Design Rules
+
+1. Batch jobs must be persisted.
+2. Batch items must be individually addressable by `request_id`.
+3. Every batch job must record:
+   - provider
+   - model
+   - submitted payload hash
+   - start/end timestamps
+   - failure reason
+4. Oracle must not block the main request thread for large batches.
+5. Any DB writeback generated from a batch must go through approval tables, not direct execution.
+
+## Oracle-Specific Runtime Policy
+
+For Oracle Canvas, the LLM is not the source of truth for data.
+
+The source of truth order is:
+
+1. canonical DB tables
+2. approved dataset projections
+3. codebook template corpus
+4. model planner
+
+The model is only allowed to:
+
+- classify intent
+- choose likely component families
+- propose layout direction
+- summarize findings
+
+The model is not allowed to:
+
+- invent database facts
+- bypass dataset allowlists
+- emit arbitrary executable code into production rendering paths
+
+## Current Production Readiness Assessment
+
+### Ready now
+
+- Oracle Canvas frontend-to-backend v1 route family
+- codebook-backed template retrieval path
+- safe DB execution gateway
+- merge/fork/revision path
+- deterministic fallback path
+- runtime LLM provider inventory
+- runtime single-chat execution
+- runtime persisted batch execution through `workflow_agent_runs`
+- Oracle planner fallback through the shared runtime LLM service
+
+### Still needs explicit implementation if the team approves
+
+- per-model selection UI in Catalyst or Oracle controls
+- dedicated `runtime_llm_jobs` / `runtime_llm_job_items` tables if the team wants stronger audit/query ergonomics than `workflow_agent_runs`
+- explicit Nemoclaw vs Ollama operator switch in a production admin surface
+- richer provider health telemetry beyond simple reachability
+
+## Recommended Next Build Steps
+
+1. Add a dedicated runtime router:
+   - `backend/api/routes_runtime_llm.py`
+2. Add DB tables:
+   - `runtime_llm_jobs`
+   - `runtime_llm_job_items`
+   - `runtime_llm_job_results`
+3. Implement provider adapters:
+   - Nemoclaw adapter
+   - Ollama adapter
+4. Expose provider status to Catalyst/Oracle settings surfaces.
+5. Keep Oracle Canvas on the current codebook-first path even after LLM batching exists.
+
+## Bottom Line
+
+Oracle Canvas should be treated as a codebook-guided analytical surface with optional LLM planning, not as a raw chat-to-SQL toy.
+
+The production-safe architecture is:
+
+- Linux origin runs the application and DB access
+- GPU box runs ComfyUI and model inference
+- Oracle retrieves from the merged codebook first
+- DB access stays whitelisted
+- Nemoclaw and Ollama sit behind a documented provider interface
+- batch processing is a separate runtime service contract, not an implicit side effect of the canvas endpoint

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/Claude Sonnet 4.6/oracle_template_expansion/README.md

@@ -0,0 +1,225 @@
+# Oracle Template Seed DB — Expanded Examples v2.0
+
+**Project:** Velocity — Multi-Surface Platform and Oracle Expansion  
+**Date:** 2026-04-19  
+**Owner:** Sayan (generated as part of Sprint 1 Oracle Template Taxonomy deliverable)  
+**Depends on:** `schema_extension_v2.sql`, `oracle_template_seed_db.json` (v1.0 canonical seeds)  
+**Total Examples:** 1,200 (50 per subchapter × 24 subchapters × 6 chapters)
+
+---
+
+## What This Is
+
+This package expands the original 8-example Oracle Template Seed DB (`oracle_template_seed_db.json`) into a full 1,200-example corpus covering every subchapter in the Oracle template taxonomy. It is the implementation artifact for Sprint 1 deliverable **§2.4 — Oracle Template Taxonomy and Seed JSON Structure**.
+
+Every example conforms to the established Velocity Oracle component contract shape. They are ready to be ingested into `oracle_template_seed_examples` via the `POST /api/oracle/component-templates/seed` route and consumed by Kimi Synthetic Data expansion jobs (`oracle_synthetic_generation_jobs`).
+
+---
+
+## File Layout
+
+```
+oracle_template_expansion/
+│
+├── README.md                                   ← This file
+├── oracle_template_seed_db_expanded.json       ← Master combined file (all 1,200 examples)
+│
+├── sub-001-01_pricing_trends.json              ← 50 examples
+├── sub-001-02_demand_signals.json              ← 50 examples
+├── sub-001-03_competitive_landscape.json       ← 50 examples
+├── sub-001-04_location_index.json              ← 50 examples
+│
+├── sub-002-01_lead_profile.json                ← 50 examples
+├── sub-002-02_qd_score.json                    ← 50 examples
+├── sub-002-03_pipeline_health.json             ← 50 examples
+├── sub-002-04_engagement_history.json          ← 50 examples
+│
+├── sub-003-01_call_summary.json                ← 50 examples
+├── sub-003-02_promise_tracker.json             ← 50 examples
+├── sub-003-03_whatsapp_thread.json             ← 50 examples
+├── sub-003-04_reminder_surface.json            ← 50 examples
+│
+├── sub-004-01_property_card.json               ← 50 examples
+├── sub-004-02_availability_matrix.json         ← 50 examples
+├── sub-004-03_absorption_rate.json             ← 50 examples
+├── sub-004-04_inventory_comparison.json        ← 50 examples
+│
+├── sub-005-01_showroom_traffic.json            ← 50 examples
+├── sub-005-02_team_performance.json            ← 50 examples
+├── sub-005-03_campaign_metrics.json            ← 50 examples
+├── sub-005-04_system_health.json               ← 50 examples
+│
+├── sub-006-01_calendar_view.json               ← 50 examples
+├── sub-006-02_action_queue.json                ← 50 examples
+├── sub-006-03_follow-up_plan.json              ← 50 examples
+└── sub-006-04_reminder_cards.json              ← 50 examples
+```
+
+---
+
+## Chapter and Subchapter Map
+
+| Chapter | Name | Subchapters | Examples |
+|---------|------|-------------|----------|
+| ch-001 | Market Intelligence | Pricing Trends, Demand Signals, Competitive Landscape, Location Index | 200 |
+| ch-002 | Lead Intelligence | Lead Profile, QD Score, Pipeline Health, Engagement History | 200 |
+| ch-003 | Communication Intelligence | Call Summary, Promise Tracker, WhatsApp Thread, Reminder Surface | 200 |
+| ch-004 | Inventory Analytics | Property Card, Availability Matrix, Absorption Rate, Inventory Comparison | 200 |
+| ch-005 | Operational Metrics | Showroom Traffic, Team Performance, Campaign Metrics, System Health | 200 |
+| ch-006 | Calendar and Follow-Up | Calendar View, Action Queue, Follow-Up Plan, Reminder Cards | 200 |
+| **Total** | | **24 subchapters** | **1,200** |
+
+---
+
+## Component Type Coverage
+
+| componentType | Subchapters Used In | Approx Count |
+|---------------|---------------------|--------------|
+| `line_chart` | sub-001-01, sub-001-02, sub-004-03, sub-005-01, sub-005-02, sub-005-03 | ~120 |
+| `bar_chart` | sub-001-02, sub-001-03, sub-004-03, sub-005-01, sub-005-02, sub-005-03 | ~100 |
+| `area_chart` | sub-001-01, sub-004-03, sub-005-01 | ~45 |
+| `heatmap` | sub-001-04, sub-005-01 | ~40 |
+| `metric_card_group` | sub-002-02, sub-005-02, sub-005-03 | ~60 |
+| `data_table` | sub-003-02 | ~50 |
+| `property_card` | sub-004-01 | ~50 |
+| `lead_profile_card` | sub-002-01 | ~50 |
+| `communication_summary` | sub-003-01 | ~50 |
+| `whatsapp_thread_viewer` | sub-003-03 | ~50 |
+| `reminder_surface` | sub-003-04 | ~50 |
+| `compact_alert_card` | sub-006-04 | ~50 |
+| `action_queue` | sub-006-02 | ~50 |
+| `calendar_view` | sub-006-01 | ~50 |
+| `follow_up_plan` | sub-006-03 | ~50 |
+| `availability_matrix` | sub-004-02 | ~50 |
+| `inventory_comparison` | sub-004-04 | ~50 |
+| `system_health_panel` | sub-005-04 | ~50 |
+| `radar_chart`, `scatter_chart`, `funnel_chart`, others | various | ~135 |
+
+---
+
+## Example JSON Structure
+
+Every example in every subchapter file follows this envelope:
+
+```json
+{
+  "example_id": "ex-0009",
+  "chapter_id": "ch-001",
+  "subchapter_id": "sub-001-01",
+  "title": "Component title string",
+  "quality_notes": "Human-readable note about this variant",
+  "is_canonical": true,
+  "template_name": "Subchapter Name — Template N",
+  "component_type": "line_chart",
+  "accepted_shapes": ["time_series"],
+  "example_json": {
+    "componentType": "line_chart",
+    "title": "...",
+    "subtitle": "...",
+    "dataSource": { ... },
+    "visualization": { ... },
+    "style": { ... },
+    "surfaceTargets": [ ... ]
+  }
+}
+```
+
+The first example in each subchapter (`is_canonical: true`) is the recommended reference template for that subchapter.
+
+---
+
+## Design Language Compliance
+
+All examples follow the established Velocity Oracle design language:
+
+**Color palette** — All `accentColor` values come from the 10-color Velocity token set:
+- `#2563EB` (primary blue), `#10B981` (emerald), `#F59E0B` (amber), `#EF4444` (red)
+- `#8B5CF6` (violet), `#0EA5E9` (sky), `#EC4899` (pink), `#14B8A6` (teal)
+- `#F97316` (orange), `#6366F1` (indigo)
+
+**Semantic colors** — Status colors are fixed:
+- Healthy / positive: `#10B981`
+- Warning: `#F59E0B`
+- Critical / negative: `#EF4444`
+- Neutral / muted: `#94A3B8`
+
+**Data source types** — Examples use only the contracted Oracle data source types:
+- `inventory_aggregate`, `inventory_property`, `inventory_multi_property`
+- `crm_lead`, `crm_aggregate`, `crm_engagement`, `crm_pipeline`, `crm_team_performance`
+- `sentinel_qd`, `sentinel_live`, `sentinel_historical`
+- `edge_communication_event`, `edge_memory_facts`
+- `user_calendar_events`, `insight_recommendations`
+- `nemoclaw_plan`, `catalyst_campaign`, `admin_health`, `competitive_intelligence`, `location_intelligence`
+
+**Template variables** — Dynamic entity references use double-brace mustache syntax: `{{lead_id}}`, `{{property_id}}`, `{{agent_id}}`, `{{event_id}}`, `{{tenant_id}}`, `{{user_id}}`.
+
+**Surface targets** — Every example declares `surfaceTargets` from the set: `webos`, `ipad`, `android_tablet`, `iphone_edge`, `android_phone_edge`.
+
+---
+
+## Permutation Logic
+
+Each subchapter's 50 examples are generated by cycling through permutation combinations of:
+
+- **District / developer / lead / agent names** — drawn from real Dubai market data (districts, developer names, nationality mix aligned to UAE CRM reality)
+- **Time windows** — `7D`, `14D`, `30D`, `60D`, `90D`, `6M`, `12M`, `24M`, `YTD`, `QTD`
+- **Chart types** — 4–6 types per subchapter appropriate to the data shape
+- **Grouping dimensions** — e.g. by agent, district, property type, nationality
+- **Layout variants** — e.g. `hero_with_stats`, `compact_card`, `list_row` for property cards
+- **Action sets** — e.g. `accept / dismiss / snooze_1h` vs `call_now / send_whatsapp / dismiss`
+- **Optional fields** — annotations, benchmarks, comparisons, sparklines toggled on/off across the 50
+
+This means every subchapter has diverse examples covering different use cases while staying within the correct data contract for that component family.
+
+---
+
+## How to Ingest Into the Database
+
+### Option 1 — Per-subchapter seed via Admin Surface
+
+```bash
+POST /api/oracle/component-templates/seed
+Content-Type: application/json
+
+{
+  "subchapter_id": "<uuid from oracle_template_subchapters>",
+  "examples": [ ... ]   # paste the "examples" array from the per-subchapter file
+}
+```
+
+### Option 2 — Bulk ingest via Kimi Synthetic Job
+
+The master file (`oracle_template_seed_db_expanded.json`) is the correct input for `oracle_synthetic_generation_jobs`. Insert a job row referencing the template and let the background worker distribute examples into `oracle_template_seed_examples`.
+
+### Option 3 — Direct SQL seed (dev/staging only)
+
+```sql
+INSERT INTO oracle_template_seed_examples
+  (template_id, chapter_id, subchapter_id, title, example_json, quality_notes, is_canonical)
+VALUES
+  (...);
+```
+
+Map string chapter/subchapter IDs from the JSON against the UUID rows you insert via the migration in `schema_extension_v2.sql`.
+
+---
+
+## Known Caveats and Next Steps
+
+- **`_meta.total_seed_examples` in v1 seed DB** — The original `oracle_template_seed_db.json` reports `36` in `_meta.total_seed_examples` but only contains 8 examples. This mismatch was noted in `delivery_log.md`. This expansion does not patch the v1 file; correct it separately before merging both corpora.
+
+- **Kimi expansion** — These 1,200 examples are the **seed corpus**, not the synthetic expansion. Run `oracle_synthetic_generation_jobs` against published templates to generate the larger training/demo sets described in `KIMI_SYNTHETIC_DATA_DOWNSTREAM_PLAN.md`.
+
+- **UUID mapping** — The `chapter_id` and `subchapter_id` fields in these files use the string keys from the v1 seed DB (`ch-001`, `sub-001-01`). Your migration script must map these to the PostgreSQL UUIDs inserted by `schema_extension_v2.sql`.
+
+- **Template IDs** — `example_json.template_name` is a human label. Actual `template_id` UUIDs are assigned at ingestion time against `oracle_component_templates`.
+
+---
+
+## Generation Script
+
+The generator script is included at `generate_examples.py` in the repo root (outside this zip). It is reproducible — re-running it with the same seed logic will produce the same 1,200 examples.
+
+---
+
+*Generated by Project Velocity platform tooling · 2026-04-19*

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/README.md

@@ -0,0 +1,82 @@
+# Oracle Canvas JSON Expansion Pack
+
+This pack expands the current Oracle template seed library into a reviewable example set with **50 examples per subchapter** across all **24 subchapters**.
+
+## What is inside
+
+- `db/oracle_template_seed_db_expanded_v1.pretty.json`  
+  Full expanded master DB with chapter taxonomy and all **1200** examples.
+
+- `db/oracle_template_seed_db_expanded_v1.min.json`  
+  Minified version of the same master DB.
+
+- `examples/`  
+  Chapter-by-chapter split files. Each subchapter file contains exactly **50** examples.
+
+- `manifests/template_family_catalog.json`  
+  Component families, accepted shapes, policy tags, and backend hints per subchapter.
+
+- `manifests/subchapter_index.json`  
+  Index of all generated files.
+
+- `manifests/validation_report.json`  
+  Validation summary for counts and ID uniqueness.
+
+- `csv/subchapter_example_counts.csv`  
+  Spreadsheet-friendly count manifest.
+
+## Source alignment
+
+This pack was generated against the current repo direction and constraints:
+
+- FastAPI backend remains canonical.
+- Oracle remains the analytical center.
+- Mobile edge surfaces remain narrow, bounded control surfaces.
+- Communication intelligence examples stay inside supported channels and provenance-aware capture modes.
+- Admin examples only model bounded and auditable actions.
+- The expanded examples follow the live-data-first / no-mock direction from the delivery log.
+
+## Important correction carried forward
+
+The source seed DB metadata currently reports `total_seed_examples: 36`, but the source file actually contains **8** canonical seed examples.  
+This expansion pack corrects the count in its own metadata and preserves the existing canonical examples inside the 50-example-per-subchapter allocation wherever they already existed.
+
+## Design language used
+
+Common policy tags applied through the pack:
+
+- `backend_owned`
+- `live_data_first`
+- `no_mock_fallback`
+- `surface_safe`
+
+Additional policy tags appear per subchapter where relevant, including:
+
+- `supported_channel_only`
+- `provider_provenance_required`
+- `bounded_admin_actions`
+- `confirmation_required_for_writeback`
+- `business_whatsapp_scope`
+- `nemoclaw_suggested`
+
+## Notes on IDs
+
+The source taxonomy uses symbolic IDs such as `ch-001` and `sub-001-01`.  
+This pack preserves those symbolic IDs for review and lineage consistency.
+
+Generated example IDs use deterministic `exg-*` identifiers. Existing canonical example IDs from the source file are preserved.
+
+## Suggested use
+
+1. Review examples subchapter-by-subchapter from `examples/`.
+2. Use `template_family_catalog.json` to decide which component families should become formal reusable templates.
+3. Use the master DB JSON once you are ready to merge the chosen examples into the Oracle seed library.
+4. Keep the metadata notes about symbolic taxonomy IDs in mind when preparing any DB import step against UUID-backed SQL tables.
+
+## Counts
+
+- Chapters: 6
+- Subchapters: 24
+- Total examples: 1200
+- Canonical carried forward: 8
+- Generated additions: 1192

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/csv/subchapter_example_counts.csv

@@ -0,0 +1,25 @@
+chapter_id,chapter_name,subchapter_id,subchapter_name,example_count,file
+ch-001,Market Intelligence,sub-001-01,Pricing Trends,50,examples/ch-001_market-intelligence/sub-001-01_pricing-trends.json
+ch-001,Market Intelligence,sub-001-02,Demand Signals,50,examples/ch-001_market-intelligence/sub-001-02_demand-signals.json
+ch-001,Market Intelligence,sub-001-03,Competitive Landscape,50,examples/ch-001_market-intelligence/sub-001-03_competitive-landscape.json
+ch-001,Market Intelligence,sub-001-04,Location Index,50,examples/ch-001_market-intelligence/sub-001-04_location-index.json
+ch-002,Lead Intelligence,sub-002-01,Lead Profile,50,examples/ch-002_lead-intelligence/sub-002-01_lead-profile.json
+ch-002,Lead Intelligence,sub-002-02,QD Score,50,examples/ch-002_lead-intelligence/sub-002-02_qd-score.json
+ch-002,Lead Intelligence,sub-002-03,Pipeline Health,50,examples/ch-002_lead-intelligence/sub-002-03_pipeline-health.json
+ch-002,Lead Intelligence,sub-002-04,Engagement History,50,examples/ch-002_lead-intelligence/sub-002-04_engagement-history.json
+ch-003,Communication Intelligence,sub-003-01,Call Summary,50,examples/ch-003_communication-intelligence/sub-003-01_call-summary.json
+ch-003,Communication Intelligence,sub-003-02,Promise Tracker,50,examples/ch-003_communication-intelligence/sub-003-02_promise-tracker.json
+ch-003,Communication Intelligence,sub-003-03,WhatsApp Thread,50,examples/ch-003_communication-intelligence/sub-003-03_whatsapp-thread.json
+ch-003,Communication Intelligence,sub-003-04,Reminder Surface,50,examples/ch-003_communication-intelligence/sub-003-04_reminder-surface.json
+ch-004,Inventory Analytics,sub-004-01,Property Card,50,examples/ch-004_inventory-analytics/sub-004-01_property-card.json
+ch-004,Inventory Analytics,sub-004-02,Availability Matrix,50,examples/ch-004_inventory-analytics/sub-004-02_availability-matrix.json
+ch-004,Inventory Analytics,sub-004-03,Absorption Rate,50,examples/ch-004_inventory-analytics/sub-004-03_absorption-rate.json
+ch-004,Inventory Analytics,sub-004-04,Inventory Comparison,50,examples/ch-004_inventory-analytics/sub-004-04_inventory-comparison.json
+ch-005,Operational Metrics,sub-005-01,Showroom Traffic,50,examples/ch-005_operational-metrics/sub-005-01_showroom-traffic.json
+ch-005,Operational Metrics,sub-005-02,Team Performance,50,examples/ch-005_operational-metrics/sub-005-02_team-performance.json
+ch-005,Operational Metrics,sub-005-03,Campaign Metrics,50,examples/ch-005_operational-metrics/sub-005-03_campaign-metrics.json
+ch-005,Operational Metrics,sub-005-04,System Health,50,examples/ch-005_operational-metrics/sub-005-04_system-health.json
+ch-006,Calendar and Follow-Up,sub-006-01,Calendar View,50,examples/ch-006_calendar-and-follow-up/sub-006-01_calendar-view.json
+ch-006,Calendar and Follow-Up,sub-006-02,Action Queue,50,examples/ch-006_calendar-and-follow-up/sub-006-02_action-queue.json
+ch-006,Calendar and Follow-Up,sub-006-03,Follow-Up Plan,50,examples/ch-006_calendar-and-follow-up/sub-006-03_follow-up-plan.json
+ch-006,Calendar and Follow-Up,sub-006-04,Reminder Cards,50,examples/ch-006_calendar-and-follow-up/sub-006-04_reminder-cards.json

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/manifests/subchapter_index.json

@@ -0,0 +1,194 @@
+[
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-01",
+    "subchapter_name": "Pricing Trends",
+    "example_count": 50,
+    "file": "examples/ch-001_market-intelligence/sub-001-01_pricing-trends.json"
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-02",
+    "subchapter_name": "Demand Signals",
+    "example_count": 50,
+    "file": "examples/ch-001_market-intelligence/sub-001-02_demand-signals.json"
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-03",
+    "subchapter_name": "Competitive Landscape",
+    "example_count": 50,
+    "file": "examples/ch-001_market-intelligence/sub-001-03_competitive-landscape.json"
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-04",
+    "subchapter_name": "Location Index",
+    "example_count": 50,
+    "file": "examples/ch-001_market-intelligence/sub-001-04_location-index.json"
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-01",
+    "subchapter_name": "Lead Profile",
+    "example_count": 50,
+    "file": "examples/ch-002_lead-intelligence/sub-002-01_lead-profile.json"
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-02",
+    "subchapter_name": "QD Score",
+    "example_count": 50,
+    "file": "examples/ch-002_lead-intelligence/sub-002-02_qd-score.json"
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-03",
+    "subchapter_name": "Pipeline Health",
+    "example_count": 50,
+    "file": "examples/ch-002_lead-intelligence/sub-002-03_pipeline-health.json"
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-04",
+    "subchapter_name": "Engagement History",
+    "example_count": 50,
+    "file": "examples/ch-002_lead-intelligence/sub-002-04_engagement-history.json"
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-01",
+    "subchapter_name": "Call Summary",
+    "example_count": 50,
+    "file": "examples/ch-003_communication-intelligence/sub-003-01_call-summary.json"
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-02",
+    "subchapter_name": "Promise Tracker",
+    "example_count": 50,
+    "file": "examples/ch-003_communication-intelligence/sub-003-02_promise-tracker.json"
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-03",
+    "subchapter_name": "WhatsApp Thread",
+    "example_count": 50,
+    "file": "examples/ch-003_communication-intelligence/sub-003-03_whatsapp-thread.json"
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-04",
+    "subchapter_name": "Reminder Surface",
+    "example_count": 50,
+    "file": "examples/ch-003_communication-intelligence/sub-003-04_reminder-surface.json"
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-01",
+    "subchapter_name": "Property Card",
+    "example_count": 50,
+    "file": "examples/ch-004_inventory-analytics/sub-004-01_property-card.json"
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-02",
+    "subchapter_name": "Availability Matrix",
+    "example_count": 50,
+    "file": "examples/ch-004_inventory-analytics/sub-004-02_availability-matrix.json"
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-03",
+    "subchapter_name": "Absorption Rate",
+    "example_count": 50,
+    "file": "examples/ch-004_inventory-analytics/sub-004-03_absorption-rate.json"
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-04",
+    "subchapter_name": "Inventory Comparison",
+    "example_count": 50,
+    "file": "examples/ch-004_inventory-analytics/sub-004-04_inventory-comparison.json"
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-01",
+    "subchapter_name": "Showroom Traffic",
+    "example_count": 50,
+    "file": "examples/ch-005_operational-metrics/sub-005-01_showroom-traffic.json"
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-02",
+    "subchapter_name": "Team Performance",
+    "example_count": 50,
+    "file": "examples/ch-005_operational-metrics/sub-005-02_team-performance.json"
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-03",
+    "subchapter_name": "Campaign Metrics",
+    "example_count": 50,
+    "file": "examples/ch-005_operational-metrics/sub-005-03_campaign-metrics.json"
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-04",
+    "subchapter_name": "System Health",
+    "example_count": 50,
+    "file": "examples/ch-005_operational-metrics/sub-005-04_system-health.json"
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-01",
+    "subchapter_name": "Calendar View",
+    "example_count": 50,
+    "file": "examples/ch-006_calendar-and-follow-up/sub-006-01_calendar-view.json"
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-02",
+    "subchapter_name": "Action Queue",
+    "example_count": 50,
+    "file": "examples/ch-006_calendar-and-follow-up/sub-006-02_action-queue.json"
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-03",
+    "subchapter_name": "Follow-Up Plan",
+    "example_count": 50,
+    "file": "examples/ch-006_calendar-and-follow-up/sub-006-03_follow-up-plan.json"
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-04",
+    "subchapter_name": "Reminder Cards",
+    "example_count": 50,
+    "file": "examples/ch-006_calendar-and-follow-up/sub-006-04_reminder-cards.json"
+  }
+]

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/manifests/template_family_catalog.json

@@ -0,0 +1,931 @@
+[
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-01",
+    "subchapter_name": "Pricing Trends",
+    "component_types": [
+      "area_chart",
+      "benchmark_band_chart",
+      "dual_axis_chart",
+      "line_chart",
+      "sparkline_metric"
+    ],
+    "accepted_shapes": [
+      "comparative_time_series",
+      "district_benchmark",
+      "dual_metric_time_series",
+      "segment_snapshot",
+      "time_series"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "oracle",
+    "primary_tables": [
+      "oracle_component_templates",
+      "inventory_properties"
+    ]
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-02",
+    "subchapter_name": "Demand Signals",
+    "component_types": [
+      "bar_chart",
+      "funnel_chart",
+      "heatmap",
+      "line_chart",
+      "metric_card_group"
+    ],
+    "accepted_shapes": [
+      "categorical_count",
+      "conversion_funnel",
+      "demand_snapshot",
+      "intent_time_series",
+      "zone_time_matrix"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "oracle",
+    "primary_tables": [
+      "oracle_component_templates",
+      "inventory_properties"
+    ]
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-03",
+    "subchapter_name": "Competitive Landscape",
+    "component_types": [
+      "bar_chart",
+      "comparison_table",
+      "grouped_bar_chart",
+      "matrix_grid",
+      "scorecard_panel"
+    ],
+    "accepted_shapes": [
+      "competitive_matrix",
+      "competitive_scorecard",
+      "developer_benchmark",
+      "developer_pipeline",
+      "unit_mix_distribution"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "oracle",
+    "primary_tables": [
+      "oracle_component_templates",
+      "inventory_properties"
+    ]
+  },
+  {
+    "chapter_id": "ch-001",
+    "chapter_name": "Market Intelligence",
+    "subchapter_id": "sub-001-04",
+    "subchapter_name": "Location Index",
+    "component_types": [
+      "data_table",
+      "map_score_card",
+      "radar_chart",
+      "scorecard_panel",
+      "timeline_chart"
+    ],
+    "accepted_shapes": [
+      "district_ranking",
+      "infrastructure_readiness",
+      "location_index",
+      "location_map_summary",
+      "proximity_profile"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "oracle",
+    "primary_tables": [
+      "oracle_component_templates",
+      "inventory_properties"
+    ]
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-01",
+    "subchapter_name": "Lead Profile",
+    "component_types": [
+      "affinity_card",
+      "cluster_card",
+      "lead_profile_card",
+      "metric_card_group",
+      "summary_strip"
+    ],
+    "accepted_shapes": [
+      "district_affinity",
+      "lead_preferences",
+      "lead_profile",
+      "lead_summary",
+      "persona_cluster"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "crm",
+    "primary_tables": [
+      "leads",
+      "sentinel_scores"
+    ]
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-02",
+    "subchapter_name": "QD Score",
+    "component_types": [
+      "bar_chart",
+      "gauge_stack",
+      "line_chart",
+      "matrix_grid",
+      "metric_card_group"
+    ],
+    "accepted_shapes": [
+      "qd_matrix",
+      "qd_peer_benchmark",
+      "qd_score_breakdown",
+      "qd_snapshot",
+      "qd_trend"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "crm",
+    "primary_tables": [
+      "leads",
+      "sentinel_scores"
+    ]
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-03",
+    "subchapter_name": "Pipeline Health",
+    "component_types": [
+      "data_table",
+      "funnel_chart",
+      "heatmap",
+      "metric_card_group",
+      "stacked_bar_chart"
+    ],
+    "accepted_shapes": [
+      "pipeline_distribution",
+      "pipeline_forecast",
+      "pipeline_probability_matrix",
+      "pipeline_stalls",
+      "pipeline_velocity"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "crm",
+    "primary_tables": [
+      "leads",
+      "sentinel_scores"
+    ]
+  },
+  {
+    "chapter_id": "ch-002",
+    "chapter_name": "Lead Intelligence",
+    "subchapter_id": "sub-002-04",
+    "subchapter_name": "Engagement History",
+    "component_types": [
+      "data_table",
+      "heatmap",
+      "interaction_timeline",
+      "line_chart",
+      "metric_card_group"
+    ],
+    "accepted_shapes": [
+      "channel_preference_trend",
+      "content_interaction_log",
+      "engagement_heatmap",
+      "engagement_snapshot",
+      "engagement_timeline"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "crm",
+    "primary_tables": [
+      "leads",
+      "sentinel_scores"
+    ]
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-01",
+    "subchapter_name": "Call Summary",
+    "component_types": [
+      "communication_summary",
+      "data_table",
+      "metric_card_group",
+      "next_best_action_card",
+      "transcript_highlight_card"
+    ],
+    "accepted_shapes": [
+      "call_follow_up_snapshot",
+      "call_outcome_snapshot",
+      "speaker_highlights",
+      "transcript_segments",
+      "transcript_summary"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "provider_provenance_required",
+      "supported_channel_only"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "edge_communication_events",
+      "edge_communication_memory_facts",
+      "edge_transcription_jobs",
+      "edge_transcript_segments"
+    ]
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-02",
+    "subchapter_name": "Promise Tracker",
+    "component_types": [
+      "checklist_board",
+      "compact_alert_card",
+      "data_table",
+      "matrix_grid",
+      "summary_card"
+    ],
+    "accepted_shapes": [
+      "communication_facts",
+      "decision_maker_summary",
+      "follow_up_checklist",
+      "overdue_commitments",
+      "promise_confidence_matrix"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "reviewable_writebacks",
+      "communication_memory"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "edge_communication_events",
+      "edge_communication_memory_facts",
+      "edge_transcription_jobs",
+      "edge_transcript_segments"
+    ]
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-03",
+    "subchapter_name": "WhatsApp Thread",
+    "component_types": [
+      "data_table",
+      "line_chart",
+      "message_thread_summary",
+      "metric_card_group",
+      "summary_card"
+    ],
+    "accepted_shapes": [
+      "message_action_queue",
+      "message_sentiment_timeline",
+      "operator_handover",
+      "thread_sla_snapshot",
+      "whatsapp_thread"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "supported_channel_only",
+      "business_whatsapp_scope"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "edge_communication_events",
+      "edge_communication_memory_facts",
+      "edge_transcription_jobs",
+      "edge_transcript_segments"
+    ]
+  },
+  {
+    "chapter_id": "ch-003",
+    "chapter_name": "Communication Intelligence",
+    "subchapter_id": "sub-003-04",
+    "subchapter_name": "Reminder Surface",
+    "component_types": [
+      "action_strip",
+      "alert_queue",
+      "compact_alert_card",
+      "matrix_grid",
+      "next_best_action_card"
+    ],
+    "accepted_shapes": [
+      "calendar_suggestion",
+      "insight_recommendation",
+      "next_best_action",
+      "recommendation_confidence",
+      "reminder_queue"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "bounded_actions",
+      "nemoclaw_suggested"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "edge_communication_events",
+      "edge_communication_memory_facts",
+      "edge_transcription_jobs",
+      "edge_transcript_segments"
+    ]
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-01",
+    "subchapter_name": "Property Card",
+    "component_types": [
+      "bar_chart",
+      "cta_card",
+      "metric_card_group",
+      "property_card",
+      "summary_card"
+    ],
+    "accepted_shapes": [
+      "inventory_property",
+      "property_cta",
+      "property_media_summary",
+      "property_pricing_snapshot",
+      "unit_mix_distribution"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "inventory",
+    "primary_tables": [
+      "inventory_properties",
+      "inventory_media_assets",
+      "inventory_import_batches"
+    ]
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-02",
+    "subchapter_name": "Availability Matrix",
+    "component_types": [
+      "data_table",
+      "heatmap",
+      "matrix_grid",
+      "metric_card_group",
+      "stacked_bar_chart"
+    ],
+    "accepted_shapes": [
+      "availability_heatmap",
+      "availability_matrix",
+      "bed_type_snapshot",
+      "price_band_grid",
+      "release_phase_distribution"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "inventory",
+    "primary_tables": [
+      "inventory_properties",
+      "inventory_media_assets",
+      "inventory_import_batches"
+    ]
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-03",
+    "subchapter_name": "Absorption Rate",
+    "component_types": [
+      "bar_chart",
+      "dual_axis_chart",
+      "line_chart",
+      "matrix_grid",
+      "sparkline_metric"
+    ],
+    "accepted_shapes": [
+      "developer_velocity_ranking",
+      "handover_absorption_matrix",
+      "rolling_velocity_snapshot",
+      "sales_velocity",
+      "velocity_supply_overlay"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "inventory",
+    "primary_tables": [
+      "inventory_properties",
+      "inventory_media_assets",
+      "inventory_import_batches"
+    ]
+  },
+  {
+    "chapter_id": "ch-004",
+    "chapter_name": "Inventory Analytics",
+    "subchapter_id": "sub-004-04",
+    "subchapter_name": "Inventory Comparison",
+    "component_types": [
+      "comparison_table",
+      "metric_card_group",
+      "radar_chart",
+      "side_by_side_comparison",
+      "summary_strip"
+    ],
+    "accepted_shapes": [
+      "amenity_comparison",
+      "inventory_comparison",
+      "operator_choice_summary",
+      "property_metric_comparison",
+      "sales_readiness_comparison"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "inventory",
+    "primary_tables": [
+      "inventory_properties",
+      "inventory_media_assets",
+      "inventory_import_batches"
+    ]
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-01",
+    "subchapter_name": "Showroom Traffic",
+    "component_types": [
+      "bar_chart",
+      "dual_axis_chart",
+      "heatmap",
+      "metric_card_group",
+      "summary_strip"
+    ],
+    "accepted_shapes": [
+      "live_traffic_snapshot",
+      "peak_hour_distribution",
+      "visitor_flow_overlay",
+      "zone_summary",
+      "zone_time_matrix"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "admin-surface",
+    "primary_tables": [
+      "surface_sessions",
+      "admin_action_events",
+      "oracle_synthetic_generation_jobs",
+      "inventory_import_batches",
+      "edge_transcription_jobs"
+    ]
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-02",
+    "subchapter_name": "Team Performance",
+    "component_types": [
+      "compact_alert_card",
+      "dual_axis_chart",
+      "leaderboard_table",
+      "matrix_grid",
+      "metric_card_group"
+    ],
+    "accepted_shapes": [
+      "activity_conversion_overlay",
+      "agent_leaderboard",
+      "follow_up_compliance_matrix",
+      "quality_drift_alert",
+      "team_performance_snapshot"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "admin-surface",
+    "primary_tables": [
+      "surface_sessions",
+      "admin_action_events",
+      "oracle_synthetic_generation_jobs",
+      "inventory_import_batches",
+      "edge_transcription_jobs"
+    ]
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-03",
+    "subchapter_name": "Campaign Metrics",
+    "component_types": [
+      "bar_chart",
+      "line_chart",
+      "metric_card_group",
+      "scatter_plot",
+      "summary_card"
+    ],
+    "accepted_shapes": [
+      "campaign_attribution",
+      "campaign_efficiency",
+      "campaign_roas_trend",
+      "campaign_snapshot",
+      "channel_comparison"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe"
+    ],
+    "route_family": "admin-surface",
+    "primary_tables": [
+      "campaign_metrics",
+      "lead_events"
+    ]
+  },
+  {
+    "chapter_id": "ch-005",
+    "chapter_name": "Operational Metrics",
+    "subchapter_id": "sub-005-04",
+    "subchapter_name": "System Health",
+    "component_types": [
+      "action_panel",
+      "data_table",
+      "line_chart",
+      "metric_card_group",
+      "system_health_panel"
+    ],
+    "accepted_shapes": [
+      "bounded_admin_actions",
+      "latency_time_series",
+      "queue_status",
+      "surface_session_snapshot",
+      "system_health_snapshot"
+    ],
+    "surface_targets": [
+      "webos"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "bounded_admin_actions",
+      "audit_ready"
+    ],
+    "route_family": "admin-surface",
+    "primary_tables": [
+      "admin_action_events",
+      "oracle_synthetic_generation_jobs",
+      "inventory_import_batches",
+      "edge_transcription_jobs",
+      "surface_sessions"
+    ]
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-01",
+    "subchapter_name": "Calendar View",
+    "component_types": [
+      "calendar_agenda",
+      "calendar_heatmap",
+      "data_table",
+      "donut_chart",
+      "summary_strip"
+    ],
+    "accepted_shapes": [
+      "calendar_density",
+      "calendar_mix",
+      "calendar_strip",
+      "calendar_suggestions",
+      "user_calendar_agenda"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "user_calendar_scope",
+      "confirmation_required_for_writeback"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "user_calendar_events",
+      "insight_recommendations",
+      "edge_communication_events"
+    ]
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-02",
+    "subchapter_name": "Action Queue",
+    "component_types": [
+      "action_strip",
+      "bar_chart",
+      "donut_chart",
+      "matrix_grid",
+      "prioritized_task_list"
+    ],
+    "accepted_shapes": [
+      "action_status_mix",
+      "action_type_distribution",
+      "agent_action_queue",
+      "edge_action_strip",
+      "queue_urgency_matrix"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "bounded_actions",
+      "operator_queue"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "user_calendar_events",
+      "insight_recommendations",
+      "edge_communication_events"
+    ]
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-03",
+    "subchapter_name": "Follow-Up Plan",
+    "component_types": [
+      "compact_alert_card",
+      "data_table",
+      "structured_plan_card",
+      "summary_card",
+      "timeline_chart"
+    ],
+    "accepted_shapes": [
+      "escalation_plan",
+      "follow_up_cadence",
+      "follow_up_edge_card",
+      "follow_up_plan",
+      "follow_up_timeline"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "nemoclaw_suggested",
+      "confirmation_required_for_writeback"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "user_calendar_events",
+      "insight_recommendations",
+      "edge_communication_events"
+    ]
+  },
+  {
+    "chapter_id": "ch-006",
+    "chapter_name": "Calendar and Follow-Up",
+    "subchapter_id": "sub-006-04",
+    "subchapter_name": "Reminder Cards",
+    "component_types": [
+      "compact_alert_card",
+      "kanban_board",
+      "matrix_grid",
+      "stacked_reminder_cards",
+      "summary_strip"
+    ],
+    "accepted_shapes": [
+      "insight_recommendation",
+      "reminder_priority_matrix",
+      "reminder_snooze_board",
+      "reminder_stack",
+      "reminder_strip"
+    ],
+    "surface_targets": [
+      "webos",
+      "ipad",
+      "android_tablet",
+      "iphone_edge",
+      "android_phone_edge"
+    ],
+    "policy_tags": [
+      "backend_owned",
+      "live_data_first",
+      "no_mock_fallback",
+      "surface_safe",
+      "bounded_actions",
+      "surface_agnostic"
+    ],
+    "route_family": "mobile-edge",
+    "primary_tables": [
+      "user_calendar_events",
+      "insight_recommendations",
+      "edge_communication_events"
+    ]
+  }
+]

.Agent Context/Sprint 1/Sayan Multi-Surface and Oracle Delivery Pack/Sample JSON Schema/GPT 5.4/oracle_canvas_json_expansion_pack/manifests/validation_report.json

@@ -0,0 +1,38 @@
+{
+  "status": "ok",
+  "checks": {
+    "total_examples": 1200,
+    "unique_example_ids": 1200,
+    "subchapters_with_50_examples": true,
+    "chapters": 6,
+    "subchapters": 24,
+    "source_seed_examples_reported": 36,
+    "source_seed_examples_actual": 8
+  },
+  "per_subchapter": {
+    "sub-001-01": 50,
+    "sub-001-02": 50,
+    "sub-001-03": 50,
+    "sub-001-04": 50,
+    "sub-002-01": 50,
+    "sub-002-02": 50,
+    "sub-002-03": 50,
+    "sub-002-04": 50,
+    "sub-003-01": 50,
+    "sub-003-02": 50,
+    "sub-003-03": 50,
+    "sub-003-04": 50,
+    "sub-004-01": 50,
+    "sub-004-02": 50,
+    "sub-004-03": 50,
+    "sub-004-04": 50,
+    "sub-005-01": 50,
+    "sub-005-02": 50,
+    "sub-005-03": 50,
+    "sub-005-04": 50,
+    "sub-006-01": 50,
+    "sub-006-02": 50,
+    "sub-006-03": 50,
+    "sub-006-04": 50
+  }
+}