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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>
538 B
538 B
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 benchmarks.
See PLAN.md for the full methodology.