Best practices for git usage, commits, and workflow
Optimizing arithmetic circuits / constraint systems (R1CS, PLONKish, AIR) for zero-knowledge proofs — minimizing multiplication constraints, rows, witnesses, and gate degree. Use when reducing constraint/witness counts, designing or golfing R1CS/PLONK/AIR gadgets (boolean ops, adders, range checks, hashes like SHA-256/Keccak/Poseidon), doing foreign-field/non-native or CRT/RNS arithmetic, choosing lookups vs arithmetic, or using SMT (cvc5) and SageMath (Gröbner basis) to synthesize, verify, and certify constraint encodings.
Writing, proving, and debugging Lean 4 + Mathlib. Use when proving theorems, filling `sorry`s, formalizing math, fixing broken proofs, or setting up a Lean project. Covers the cached-Mathlib setup and a plan-first proving workflow.
Performance-guided Rust optimization using benchmarks, sampling profilers, and agent-readable profiling artifacts. Use when optimizing Rust throughput, latency, CPU time, allocations, code size, compile/runtime hot paths, Criterion benchmarks, hyperfine measurements, Linux perf, macOS xctrace/Instruments, samply, flamegraphs, heap profilers, cargo-bloat, cargo-llvm-lines, or cargo-asm.
The essential best practices for Rust development
Invoke Codex CLI as a sub-agent for code tasks. Useful for second opinions, parallel exploration, or offloading isolated subtasks.
Best practices for Python development
End-to-end Rust verification and formalization in Lean 4 using Aeneas and Charon. Use when Codex needs to translate Rust crates or functions to Lean, work with Aeneas-generated Lean files, write weakest-precondition specs, prove Rust program properties with Aeneas tactics, model mutable borrows/back-functions, reason about Rust scalars/vectors/loops/traits, configure Lake for the Aeneas Lean backend, or debug Charon/Aeneas/LLBC translation and proof workflows.