rust-dev-skill

name

rust-dev-skill

description

AI-assisted Rust development with code contracts, lightweight spec-driven development, and performance optimization. Use when writing, reviewing, or optimizing Rust code -- especially for distributed systems, consensus protocols, or any correctness-critical Rust project. Triggers on Rust development tasks, performance tuning requests, "write Rust code with contracts", "optimize Rust performance", "spec-driven Rust development", or AI-assisted Rust workflows modeled on 100K+ line production-grade Rust projects.

Rust AI-Assisted Development

Production-grade Rust development methodology distilled from building a 130K-line multi-Paxos consensus engine with AI coding agents. Covers code contracts for correctness, lightweight spec-driven development, and aggressive performance optimization.

Core Workflow

Specify → Clarify → Contract → Implement → Test → Optimize

Each phase below has a corresponding deep-dive reference. Load the reference when entering that phase.

1. Lightweight Spec-Driven Development

Use /specify (spec-kit) to generate a spec markdown with user stories and acceptance criteria. One user story is the "sweet spot" unit of work for today's AI agents.

Workflow:

1. /specify — Generate spec with user stories + acceptance criteria
2. /clarify — AI self-critiques and improves the spec; suggest additional user stories
3. Enter plan mode for a single user story
4. Implement that user story end-to-end
5. Repeat for next user story

Key insight: Rigid SDD (requirements → design → task list) is too brittle. A single spec markdown per feature, clarified and refined interactively, keeps documents consistent while avoiding overhead.

See references/sdd-workflow.md for the full /clarify interaction pattern and user story templates.

2. Code Contracts — Three Levels

Code contracts specify preconditions, postconditions, and invariants for critical functions. They become runtime asserts in testing and are compiled out in production.

Level 1: AI Writes Contracts

Ask AI to write contracts for critical functions. GPT-5 High writes the best contracts; Opus 4.1 is adequate. Focus human effort on reviewing and refining.

Example for a Paxos process_2a method (16 contracts total):

/// Precondition: ballot_number > 0
/// Precondition: slot_number >= first_slot
/// Postcondition: returns Ok(()) iff the proposal is accepted
/// Postcondition: accepted_proposals[slot_number] == Some(proposal)
/// Invariant: accepted_proposals.len() does not decrease
fn process_2a(&mut self, msg: Phase2aMessage) -> Result<()> { ... }

Level 2: Generate Tests from Contracts

For each postcondition, ask AI to generate targeted test cases covering edge cases.

Level 3: Property-Based Tests

Ask AI to translate contracts into property-based tests (e.g., with proptest). Randomized inputs explore a vast state space — any contract violation triggers a panic.

See references/code-contracts.md for contract authoring guidelines and property-based test patterns.

3. Testing Pyramid

Layer tests by scope — AI excels at generating all three levels:

1. Unit tests — Per-function, contract-driven
2. Minimal integration tests — Two-component pairs (e.g., proposer + acceptor)
3. Full integration tests — Multi-replica with injected failures (network partition, crash-recovery, message reordering)

Aim for tests to comprise 60%+ of the codebase. 1,300+ tests for a 130K-line project is a healthy ratio.

4. Performance Optimization Loop

Rust's safety model enables aggressive optimization without fear. The proven loop:

1. Instrument → add latency metrics across all code paths
2. Measure    → run perf tests, output trace logs
3. Analyze    → AI writes Python scripts for quantile/bottleneck analysis
4. Optimize   → AI proposes optimizations, implement one at a time
5. Repeat     → re-measure after each change

Rust-specific targets (in priority order):

• Minimize allocations (use &[u8] over Vec<u8>, arena allocators)
• Apply zero-copy techniques (Bytes, Cow, borrowed slices)
• Eliminate lock contention (consider parking_lot, sharded locks, lock-free structures)
• Remove unnecessary async overhead (don't tokio::spawn for trivial work)
• Batch operations to amortize syscall/network costs

See references/perf-optimization.md for the full instrumentation and analysis workflow.

5. AI Coding Agent Patterns

CLI-First Workflow

Code from the terminal (Claude Code / Codex CLI), use VS Code only for diff review and minor edits. This creates an asynchronous flow that maximizes throughput.

Task Sizing

One user story = one coding session. AI agents handle this scope well without context compression becoming a problem.

Rate Limit Strategy

For heavy usage, stagger across multiple subscriptions or accounts to handle rate limits.

Quick Reference

Phase	Command/Tool	Output
Specify	/specify from spec-kit	Spec markdown with user stories
Clarify	/clarify	Refined stories + edge cases
Contract	Ask AI: "write contracts for X"	Pre/post/invariant annotations
Test Gen	Ask AI: "generate tests from these contracts"	Unit + property-based tests
Perf	perf script (see references)	Latency breakdown → bottleneck

Resources

scripts/

• instrument_rust.py — Add tracing instrumentation spans to Rust functions

references/

• code-contracts.md — Contract authoring, property-based test patterns, Paxos case study
• sdd-workflow.md — /specify → /clarify → plan mode full workflow
• perf-optimization.md — Instrumentation, bottleneck analysis, Rust-specific optimizations