What problem does it solve?
Adding a new measurable objective to the jolt-eval optimization framework requires touching many files: the objective struct, trait implementation, enum registration, const keys, objective functions, and Criterion benchmarks. This Skill automates that boilerplate so a new objective is wired in correctly and consistently.
Core Features & Use Cases
- Guided Requirements Gathering: Validates the objective name and asks whether it is a static analysis or performance objective, plus units and measured paths.
- Template-Based Implementation: Generates the objective file from reference templates for static analysis (code metrics) or performance (Criterion benchmarks).
- Full Registration: Updates enums, dispatch methods, const re-exports, objective functions, and bench targets, then runs sync_targets.sh.
- Validation Pipeline: Runs cargo fmt, clippy, nextest, and measurement or benchmark verification before reporting success.
- Use Case: A developer wants the AI optimizer to minimize cyclomatic complexity in jolt-prover-legacy; this Skill scaffolds the objective, registers it, and verifies it end to end.
Quick Start
Ask the assistant to implement a new jolt-eval objective named cyclomatic_complexity that statically measures code complexity in the prover crate.