feat: Add 8 domain papers and RULEBOOK.md

Domain papers distilled from python-numbers-everyone-should-know:
- async-overhead: 1,400x sync vs async overhead
- collection-membership: 200x set vs list at 1000 items
- json-serialization: 8x orjson vs stdlib
- exception-flow: 6.5x exception overhead (try/except free)
- string-formatting: f-strings > % > .format()
- memory-slots: 69% memory reduction with __slots__
- import-optimization: 100ms+ for heavy packages
- database-patterns: 98% commit overhead in SQLite

RULEBOOK.md: ~200 token distillation for coding subagents

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

papers/import-optimization.md

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+# Import Optimization
+
+**Domain Paper: Python Performance ADRs**
+**Date:** 2026-01-03
+**Source:** python-numbers-everyone-should-know benchmarks
+
+---
+
+## Executive Summary
+
+Import costs range from **sub-microsecond (cached) to 100+ milliseconds** (large frameworks). For CLI tools and short-lived scripts, import time can dominate total execution.
+
+---
+
+## Benchmark Data (First Import, Fresh Process)
+
+### Built-in Modules
+
+| Module | First Import |
+|--------|-------------|
+| `sys` | 0.2 us |
+| `os` | 0.2 us |
+| `math` | 24 us |
+
+### Standard Library
+
+| Module | First Import |
+|--------|-------------|
+| `datetime` | 72 us |
+| `typing` | 2.0 ms |
+| `json` | 2.9 ms |
+| `dataclasses` | 6.0 ms |
+| `logging` | 10.5 ms |
+| `asyncio` | 17.7 ms |
+
+### External Packages
+
+| Package | First Import |
+|---------|-------------|
+| `pydantic` | 15.8 ms |
+| `flask` | 47.3 ms |
+| `fastapi` | 104.4 ms |
+
+**Key insight:** FastAPI takes 100ms just to import. For a CLI tool that runs in 50ms, this is unacceptable overhead.
+
+---
+
+## Lazy Import Patterns
+
+### Pattern 1: Function-Level Import
+```python
+def process_data(data):
+    import pandas as pd  # Only when needed
+    return pd.DataFrame(data)
+```
+
+### Pattern 2: TYPE_CHECKING Guard
+```python
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import pandas as pd
+
+def process(data: "pd.DataFrame"):
+    import pandas as pd
+    return pd.DataFrame(data)
+```
+
+---
+
+## Practical Rules for Coding Agents
+
+### MUST
+
+1. **Use `TYPE_CHECKING` for type-only imports** when the type is from a heavy package
+2. **Use function-level imports for rarely-used code paths**
+3. **Never import heavy packages at module level in CLI tools**
+
+### SHOULD
+
+4. **Use `from __future__ import annotations`** for cleaner TYPE_CHECKING
+5. **Profile import time for new dependencies:**
+   ```bash
+   python -c "import time; s=time.perf_counter(); import PACKAGE; print(f'{(time.perf_counter()-s)*1000:.1f}ms')"
+   ```
+
+---
+
+## Summary Table
+
+| Scenario | Pattern | Example |
+|----------|---------|---------|
+| Type hints for heavy types | `TYPE_CHECKING` | pandas, numpy types |
+| Rarely-used function | Function-level import | Error handling paths |
+| CLI fast path | Defer until needed | `--version`, `--help` |
+| Serverless cold start | Minimize top-level | Lambda/Cloud Functions |
+
+---
+
+*Import costs are hidden taxes. Pay them lazily.*
+
+---
+
+*Benchmark source: python-numbers-everyone-should-know*