init: Python Performance ADRs repo structure

Three-layer model:
- papers/ - Domain exploration (full context)
- RULEBOOK.md - Tight rules for agents (~200 tokens)
- evals/ - Machine-readable rules (future)

Source: python-numbers-everyone-should-know benchmarks

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

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

PLAN.md

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+# Python Performance ADRs - Plan
+
+**Created:** 2026-01-03
+**Source:** python-numbers-everyone-should-know benchmarks
+
+---
+
+## The Three-Layer Model
+
+```
+Layer 1: Domain Papers (Exploration)
+├── Full context, reasoning, benchmark methodology
+├── ~2000-5000 tokens each
+└── Loaded only when deep-diving into a domain
+
+        ↓ distill
+
+Layer 2: Rulebook (Tight)
+├── Actionable rules, no fluff
+├── ~200-400 tokens TOTAL
+└── Loaded into every coding subagent
+
+        ↓ formalize
+
+Layer 3: Evals (Machine-Readable)
+├── AST patterns, thresholds, rule IDs
+├── For automated review agent
+└── JSON/YAML, not prose
+```
+
+---
+
+## Directory Structure
+
+```
+python-performance-adrs/
+├── PLAN.md              # This file
+├── README.md            # Usage for agents
+├── RULEBOOK.md          # The tight rulebook (~200 tokens)
+├── papers/              # Domain exploration papers
+│   ├── async-overhead.md
+│   ├── collection-membership.md
+│   ├── json-serialization.md
+│   ├── exception-flow.md
+│   ├── string-formatting.md
+│   ├── memory-slots.md
+│   ├── import-optimization.md
+│   └── database-patterns.md
+├── data/
+│   └── benchmarks.yaml  # Raw numbers for eval generation
+└── evals/
+    └── rules.yaml       # Machine-readable eval rules (future)
+```
+
+---
+
+## Domains to Explore
+
+| Domain | Key Insight | Priority |
+|--------|-------------|----------|
+| **Async Overhead** | 1400x slower than sync for simple calls | Critical |
+| **Collection Membership** | Set O(1) vs List O(n), 200x at 1000 items | Critical |
+| **JSON Serialization** | orjson 8x faster than stdlib | High |
+| **Exception Flow** | 6x overhead when raised | High |
+| **String Formatting** | f-string > % > .format() | Medium |
+| **Memory/Slots** | __slots__ saves 50% memory | Medium |
+| **Import Optimization** | Lazy imports for CLI startup | Medium |
+| **Database Patterns** | SQLite reads fast, writes slow | Medium |
+
+---
+
+## Workflow
+
+1. **Explore**: Spin up agents to analyze each domain from source benchmarks
+2. **Write Papers**: Each agent produces a domain paper with full context
+3. **Distill**: Extract tight rules into RULEBOOK.md
+4. **Formalize**: Convert to machine-readable evals (later)
+
+---
+
+## Source
+
+Benchmark data from:
+- `/home/dafit/nimmerverse/references/python-numbers-everyone-should-know/`
+- `the-report.md` - Main findings
+- `results.json` - Raw benchmark data
+- `code/` - Benchmark implementations
+
+---
+
+*The substrate holds. The rules compress.*

README.md

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+# Python Performance ADRs
+
+Architecture Decision Records for Python performance patterns, distilled from benchmarks.
+
+## For Coding Agents
+
+Load `RULEBOOK.md` into context for coding tasks. It's tight (~200 tokens).
+
+For deeper understanding, load the relevant paper from `papers/`.
+
+## For Review Agents
+
+Use `evals/rules.yaml` for automated code review checks.
+
+## Source
+
+Distilled from [python-numbers-everyone-should-know](../references/python-numbers-everyone-should-know/) benchmarks.
+
+---
+
+See `PLAN.md` for the full methodology.