Formal Correctness Engineer

Stop shipping code that only works on the happy path. This skill transforms your agent into an adversarial correctness engineer that prioritizes machine-checkable evidence over vague reassurances. It forces explicit specifications, identifies mathematical impossibility early, and produces implementations with defined resource bounds.

What it does

• Formal Specification defines input domains, error semantics, and consistency models before writing code.
• Complexity Analysis calculates time and space bounds for every algorithm to ensure it meets your performance budget.
• Invariant Enforcement selects data structures that make invalid states unrepresentable and maintains clear state transitions.
• Failure Visibility implements explicit error channels and observability rather than swallowing exceptions.
• Verification Strategy generates testable properties like idempotence, monotonicity, and stability for critical logic.

How it works

1. Analyze constraints by mapping your request to specific problem classes and checking known lower bounds.
2. Review premises to identify invalid assumptions, such as using regex for nested markup or exact streaming medians without storage.
3. Build models using the Algorithmic Decision Ladder to select the most efficient representation for your data.
4. Implement artifacts with runnable code that includes Kahan compensation for floats or ownership-aware concurrency.

Frameworks & tools

Language agnostic with specialized knowledge in Rust, Ada/SPARK, Julia, C++, Python (fractions/NumPy), and formal tools like Z3 or TLA+ concepts.

Why this beats prompting it yourself

Generic prompts often lead to "clever" code that fails at the edges. This skill enforces a rigid response pattern (SPEC, VERDICT, MODEL, BOUND) that prevents silent approximations and forces the agent to prove its work.

Use cases

• Auditing concurrent algorithms for race conditions or memory ordering issues.
• Building high-precision financial systems using exact rational arithmetic.
• Designing streaming data pipelines with fixed memory ceilings and backpressure.
• Verifying parser logic for complex, nested data structures.

Known limitations

This skill focuses on algorithmic and systems correctness, not aesthetic UI design or creative writing. It requires standard IEEE arithmetic for floating-point compensation to be effective.