docs: generate comprehensive CLAUDE.md reference documentation across codebase

Create a hierarchical CLAUDE.md documentation system for the entire Open Notebook
codebase with focus on concise, pattern-driven reference cards rather than
comprehensive tutorials.

## Changes

### Core Documentation System
- Updated `.claude/commands/build-claude-md.md` to distinguish between leaf and
  parent modules, with special handling for prompt/template modules
- Established clear patterns:
  * Leaf modules (40-70 lines): Components, hooks, API clients
  * Parent modules (50-150 lines): Architecture, cross-layer patterns, data flows
  * Template modules: Pattern focus, not catalog listings

### Generated Documentation
Created 15 CLAUDE.md reference files across the project:

**Frontend (React/Next.js)**
- frontend/src/CLAUDE.md: Architecture overview, data flow, three-tier design
- frontend/src/lib/hooks/CLAUDE.md: React Query patterns, state management
- frontend/src/lib/api/CLAUDE.md: Axios client, FormData handling, interceptors
- frontend/src/lib/stores/CLAUDE.md: Zustand state persistence, auth patterns
- frontend/src/components/ui/CLAUDE.md: Radix UI primitives, CVA styling

**Backend (Python/FastAPI)**
- open_notebook/CLAUDE.md: System architecture, layer interactions
- open_notebook/ai/CLAUDE.md: Model provisioning, Esperanto integration
- open_notebook/domain/CLAUDE.md: Data models, ObjectModel/RecordModel patterns
- open_notebook/database/CLAUDE.md: Repository pattern, async migrations
- open_notebook/graphs/CLAUDE.md: LangGraph workflows, async orchestration
- open_notebook/utils/CLAUDE.md: Cross-cutting utilities, context building
- open_notebook/podcasts/CLAUDE.md: Episode/speaker profiles, job tracking

**API & Other**
- api/CLAUDE.md: REST layer, service architecture
- commands/CLAUDE.md: Async command handlers, job queue patterns
- prompts/CLAUDE.md: Jinja2 templates, prompt engineering patterns (refactored)

**Project Root**
- CLAUDE.md: Project overview, three-tier architecture, tech stack, getting started

### Key Features
- Zero duplication: Parent modules reference child CLAUDE.md files, don't repeat them
- Pattern-focused: Emphasizes how components work together, not component catalogs
- Scannable: Short bullets, code examples only when necessary (1-2 per file)
- Practical: "How to extend" guides, quirks/gotchas for each module
- Navigation: Root CLAUDE.md acts as hub pointing to specialized documentation

### Cleanup
- Removed unused `batch_fix_services.py`
- Removed deprecated `open_notebook/plugins/podcasts.py`
- Updated .gitignore for documentation consistency

## Impact
New contributors can now:
1. Read root CLAUDE.md for system architecture (5 min)
2. Jump to specific layer documentation (frontend, api, open_notebook)
3. Dive into module-specific patterns in child CLAUDE.md files (1 min per module)
All documentation is lean, reference-focused, and avoids duplication.

open_notebook/graphs/CLAUDE.md

@@ -0,0 +1,61 @@
+# Graphs Module
+
+LangGraph-based workflow orchestration for content processing, chat interactions, and AI-powered transformations.
+
+## Key Components
+
+- **`chat.py`**: Conversational agent with message history, notebook context, and model override support
+- **`source_chat.py`**: Source-focused chat with ContextBuilder for insights/content injection and context tracking
+- **`ask.py`**: Multi-search strategy agent (generates search terms, retrieves results, synthesizes answers)
+- **`source.py`**: Content ingestion pipeline (extract → save → transform with content-core)
+- **`transformation.py`**: Single-node transformation executor with prompt templating via ai_prompter
+- **`prompt.py`**: Generic pattern chain for arbitrary prompt-based LLM calls
+- **`tools.py`**: Minimal tool library (currently just `get_current_timestamp()`)
+
+## Important Patterns
+
+- **Async/sync bridging in graphs**: Both `chat.py` and `source_chat.py` use `asyncio.new_event_loop()` workaround because LangGraph nodes are sync but `provision_langchain_model()` is async
+- **State machines via StateGraph**: Each graph compiles to stateful runnable; conditional edges fan out work (ask.py, source.py do parallel transforms)
+- **Prompt templating**: `ai_prompter.Prompter` with Jinja2 templates referenced by path ("chat/system", "ask/entry", etc.)
+- **Model provisioning via context**: Config dict passed to node via `RunnableConfig`; defaults fall back to state overrides
+- **Checkpointing**: `chat.py` and `source_chat.py` use SqliteSaver for message history (LangGraph's built-in persistence)
+- **Content extraction**: `source.py` uses content-core library with provider/model from DefaultModels; URLs and files both supported
+
+## Quirks & Edge Cases
+
+- **Async loop gymnastics**: ThreadPoolExecutor workaround needed because LangGraph invokes sync nodes but we call async functions; fragile if event loop state changes
+- **`clean_thinking_content()` ubiquitous**: Strips `<think>...</think>` tags from model responses (handles extended thinking models)
+- **source_chat.py builds context twice**: ContextBuilder runs during node execution to fetch source/insights; rebuilds list from context_data (inefficient but safe)
+- **source.py embedding is async**: `source.vectorize()` returns job command ID; not awaited (fire-and-forget)
+- **transformation.py nullable source**: Accepts `input_text` or `source.full_text` (falls back to second if first missing)
+- **ask.py hard-coded vector_search**: No fallback to text search despite commented code suggesting it was planned
+- **SqliteSaver location**: Checkpoints stored in path from `LANGGRAPH_CHECKPOINT_FILE` env var; connection shared across graphs
+
+## Key Dependencies
+
+- `langgraph`: StateGraph, Send, END, START, SqliteSaver checkpoint persistence
+- `langchain_core`: Messages, OutputParser, RunnableConfig
+- `ai_prompter`: Prompter for Jinja2 template rendering
+- `content_core`: `extract_content()` for file/URL processing
+- `open_notebook.ai.provision`: `provision_langchain_model()` (async factory with fallback logic)
+- `open_notebook.domain.notebook`: Domain models (Source, Note, SourceInsight, vector_search)
+- `loguru`: Logging
+
+## Usage Example
+
+```python
+# Invoke a graph with config override
+config = {"configurable": {"model_id": "model:custom_id"}}
+result = await chat_graph.ainvoke(
+    {"messages": [HumanMessage(content="...")], "notebook": notebook},
+    config=config
+)
+
+# Source processing (content → save → transform)
+result = await source_graph.ainvoke({
+    "content_state": {...},  # ProcessSourceState from content-core
+    "apply_transformations": [t1, t2],
+    "source_id": "source:123",
+    "embed": True
+})
+```