cairo-optimization
Improves Cairo performance after correctness is established, including hotspot profiling, arithmetic/loop optimization, and bounded-int hardening.
Best use case
cairo-optimization is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Improves Cairo performance after correctness is established, including hotspot profiling, arithmetic/loop optimization, and bounded-int hardening.
Teams using cairo-optimization should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cairo-optimization/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cairo-optimization Compares
| Feature / Agent | cairo-optimization | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Improves Cairo performance after correctness is established, including hotspot profiling, arithmetic/loop optimization, and bounded-int hardening.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Cairo Optimization Apply only after tests pass and behavior is locked. ## When to Use - Improving gas/resource usage in hot paths. - Rewriting expensive loops/arithmetic patterns. - Optimizing storage layout and integer choices. ## When NOT to Use - Early feature prototyping without tests. - Security review as a substitute for correctness proofs. ## Quick Start 1. Confirm baseline behavior with tests. 2. Profile target paths with `python3 scripts/profile.py profile`. 3. Apply one optimization class at a time. 4. Re-run tests and compare resource deltas. 5. Use anti-pattern/secure-pattern pairs to keep micro-optimizations explicit. 6. Encode stable optimization rules in `../evals/cases/contract_skill_benchmark.jsonl` to prevent regressions. 7. Run `cairo-auditor` on touched files to ensure no security regressions were introduced. ## Workflow - Main optimization flow: [default workflow](workflows/default.md) ## References - Detailed optimization rules: [legacy reference](references/legacy-full.md) - Profiling workflow and troubleshooting: [profiling reference](references/profiling.md) - Optimization anti-pattern pairs: [anti-pattern pairs](references/anti-pattern-pairs.md) - Module index: [references index](references/README.md)
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