Top-level engineering verification orchestration skill and distributable skill-pack entry point. Use when Codex must plan, execute, or review a Go/Python-heavy engineering change that needs an auditable TDD inner loop plus risk-based gates for formal correctness, model-to-implementation conformance, algorithms, performance, API/schema compatibility, CI failures, profiling, distributed behavior, or experiment claims.
Algorithm and data-structure selection specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to choose, replace, or evaluate algorithms or data structures under latency, throughput, memory, asymptotic-complexity, or benchmark-regression constraints, especially when a Dafny-backed correctness model exists but the target Go/Python implementation must be efficient. Not for formal proof, distributed protocol semantics, network partitions, clocks, consensus, broad infrastructure tuning, or generic refactoring.
API and schema compatibility specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to check OpenAPI, JSON Schema, Protobuf/gRPC, GraphQL schemas, HTTP endpoints, event payloads, SDK interfaces, or serialized wire formats for breaking changes against a baseline; not for endpoint implementation correctness, auth proof, distributed protocol semantics, performance, or general API documentation.
CI failure and regression forensics specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to parse structured CI failure footers, reproduce failing CI locally, classify build/test/lint/typecheck/performance failures, extract stable failure signatures, compare environments, isolate first-bad commits, or produce a minimal fix plan from logs/artifacts. Not for designing new tests, formal verification, API compatibility review, or performance optimization after the failing regression is isolated.
Dafny formal model verification specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to create, modify, debug, or review Dafny `.dfy` models, prove properties before implementation, repair Dafny verifier failures, or work on authorization/access control, tenant isolation, state machines, parsers, validators, algorithmic correctness, invariants, security-sensitive branching, or target-language code that should be backed by machine-checked Dafny specifications.
Model-to-implementation conformance specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to map Dafny or TLA model properties to Go/Python implementation evidence, audit model-to-code conformance, compare model-exported fixtures or oracle traces against implementation output, or distinguish formal model proof from handwritten implementation alignment.
Property-based, differential, metamorphic, and stateful testing specialist for engineering-verification-loop. Use directly or when routed by engineering-verification-loop to compare optimized Go/Python code against a simple oracle, old implementation, Dafny-modeled behavior, protocol trace, parser/validator spec, or API fixture across generated inputs. Not for ordinary Red-Green-Refactor orchestration, formal proof, benchmark claims, mutation-score policy, API schema compatibility, or CI log triage.
Test-driven development specialist for engineering-verification-loop. Use directly or when routed for Go/Python features, bug fixes, behavior-preserving refactors, legacy characterization, or behavior-affecting API, CI, performance, formal-correctness, and distributed changes that need auditable Red-Green-Refactor evidence, requirement-to-test traceability, regression scope, coverage, mutation, and flake controls. Not for formal proof, API compatibility analysis, profiling, distributed model checking, or CI log forensics.