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/json-serialization.md

@@ -0,0 +1,93 @@
+# JSON Serialization Performance in Python
+
+**Domain Paper: Python Performance ADRs**
+**Date:** 2026-01-03
+**Source:** Python Numbers Everyone Should Know benchmarks
+
+---
+
+## Executive Summary
+
+Alternative JSON libraries like `orjson` and `msgspec` deliver **8-12x faster serialization** and **2-7x faster deserialization** compared to stdlib `json`. The performance gap is consistent across payload sizes.
+
+---
+
+## Key Findings
+
+### Serialization Performance (dumps)
+
+| Library | Simple Object | Complex Object | Speedup vs stdlib |
+|---------|--------------|----------------|-------------------|
+| `json.dumps()` | 708 ns | 2.65 us | 1x (baseline) |
+| `orjson.dumps()` | 61 ns | 310 ns | **11.6x / 8.5x** |
+| `msgspec.encode()` | 92 ns | 445 ns | 7.7x / 6.0x |
+| `ujson.dumps()` | 264 ns | 1.64 us | 2.7x / 1.6x |
+
+### Deserialization Performance (loads)
+
+| Library | Simple Object | Complex Object | Speedup vs stdlib |
+|---------|--------------|----------------|-------------------|
+| `json.loads()` | 714 ns | 2.22 us | 1x (baseline) |
+| `orjson.loads()` | 106 ns | 839 ns | **6.7x / 2.6x** |
+| `msgspec.decode()` | 101 ns | 850 ns | 7.1x / 2.6x |
+
+---
+
+## When to Use Each Library
+
+### Use stdlib `json` when:
+- Zero dependencies required
+- Need custom JSONEncoder subclass
+- Compatibility is paramount
+
+### Use `orjson` when:
+- Maximum performance needed
+- You can accept bytes output
+- You need datetime/UUID support
+
+### Use `msgspec` when:
+- You need typed decoding
+- You want MessagePack too
+- Memory efficiency matters
+
+---
+
+## Practical Rules for Coding Agents
+
+### Rule 1: Default to orjson for new projects
+
+```python
+# Instead of:
+import json
+data = json.dumps(obj)
+
+# Prefer:
+import orjson
+data = orjson.dumps(obj)  # Returns bytes
+```
+
+### Rule 2: Use stdlib json only when explicitly needed
+
+Acceptable reasons:
+- Must avoid external dependencies
+- Need custom JSONEncoder subclass
+- Working in constrained environment
+
+### Rule 3: Profile before optimizing JSON
+
+At 2-3 microseconds per operation, JSON serialization is rarely the bottleneck unless you're doing thousands of operations per second.
+
+---
+
+## Summary Table
+
+| Scenario | Recommendation | Expected Speedup |
+|----------|----------------|------------------|
+| General use | orjson | 8x serialization, 2.5x deserialization |
+| Typed data | msgspec | 6x + type safety |
+| Drop-in replacement | ujson | 1.5-2x |
+| Zero dependencies | json (stdlib) | Baseline |
+
+---
+
+*Benchmark source: python-numbers-everyone-should-know*