Updates broken JUnit tests after a deliberate code change — distinguishing tests that broke because the behavior changed (update assertion) from tests that broke because they were overcoupled to structure (loosen or delete). Use after API changes, refactors, or intentional behavior changes leave a trail of failing tests.

Uses an existing test suite as the behavioral oracle during a migration, tracking which tests pass at each step and localizing regressions to specific migration changes. Use when porting or refactoring code that has tests, when the user wants to migrate incrementally with a safety net, or when a migration broke something and you need to find which step did it.

Infers likely loop invariants and function contracts by observing execution traces, synthesizing candidates, and checking them inductively. Use when a verifier rejects a loop because the invariant is missing or too weak, when a Daikon-style tool is needed, or before translating code to a verification language.

Generates deployment pipelines with environment promotion, approval gates, and rollback triggers based on target infrastructure. Use when wiring automated deployments from CI to staging/production, when the user asks for a release pipeline, or when adding promotion gates to an existing deploy workflow.

Generates JUnit regression tests that lock in current behavior before a refactor, capturing observed outputs as assertions so that any behavioral change trips a test. Use before large refactors, when inheriting untested legacy Java, or when the spec is "whatever it does now."

Updates broken pytest tests after intentional code changes — triaging assertion failures from mock-coupling failures from genuine regressions, using Python's introspection to automate where safe. Use when a refactor or API change leaves a pile of failing tests and you need to decide update vs. fix vs. delete.

Generates hardened, multi-stage Dockerfiles with non-root users, minimal base images, and a .dockerignore, after auto-detecting the application stack. Use when containerizing an application for the first time, when the user asks for a Dockerfile, when migrating from a VM deployment, or when an existing Dockerfile runs as root, uses a fat base image, or leaks build tooling into the runtime layer.

Executes refactorings — extract method, inline, rename, move — in small, behavior-preserving steps with a test between each. Use when the user wants to restructure working code, when cleaning up after a feature lands, or when a smell has been identified and needs fixing.

Derives executable test cases directly from requirements (user stories, acceptance criteria, specs) by extracting testable conditions, enumerating equivalence classes and boundaries, and producing a traceability map from each test back to its source requirement. Use when building acceptance tests from a spec, when checking whether requirements are covered by existing tests, when translating Gherkin or plain-English criteria into code, or when proving coverage for compliance.

Extracts a TLA+ specification from concurrent or distributed code, modeling the state machine, actions, and fairness conditions for model checking with TLC. Use when verifying concurrency properties of production code, when designing a protocol and wanting to check it before implementation, or when the user has a race condition and needs to prove the fix.

Verifies that a refactoring or transformation preserved observable behavior by comparing before and after execution, differential testing, or I/O capture. Use after a refactoring, after automated code transformation, before merging a structural PR, or whenever the claim is that two code versions do the same thing.

Translates natural-language or pseudocode descriptions of concurrent and distributed systems into TLA+ specifications ready for the TLC model checker. Identifies state variables, actions, type invariants, safety properties, and liveness properties from the description. Use when formalizing a protocol, when the user describes a distributed algorithm to verify, when designing a consensus or locking scheme, or when starting formal verification of a concurrent system.

Applies the SZZ algorithm to VCS history to identify which commits introduced bugs by correlating bug-fix commits with earlier changes. Use when mining a repository for bug-introducing commits, when building a defect-prediction dataset, or when the user asks which commit introduced a given fixed bug.

Translates an entire module or package between languages, handling imports, file layout, visibility, and cross-function dependencies that single-function translation misses. Use when porting a library, when a migration spans multiple files, or when the user hands you a directory and a target language.

Migrates a Spring MVC application to Spring Boot, converting XML config to auto-configuration, restructuring the project, and replacing container deployment with embedded. Use when modernizing a legacy Spring app, when moving off a standalone servlet container, or when the user has web.xml and wants application.yml.

Vue 3 Composition API, script setup macros, reactivity system, and built-in components. Use when writing Vue SFCs, defineProps/defineEmits/defineModel, watchers, or using Transition/Teleport/Suspense/KeepAlive.

Node.js package manager with strict dependency resolution. Use when running pnpm specific commands, configuring workspaces, or managing dependencies with catalogs, patches, or overrides.

Create a Gmail filter to automatically label, star, or categorize incoming messages.

Append a deal status update to a Google Sheets sales tracking spreadsheet.

Handle errors as values instead of throwing exceptions. Result/Either pattern, railway-oriented programming, and validation with error accumulation. Activate when: writing error handling, try/catch blocks, validation logic, operations that can fail, chaining fallible operations, form validation, API input validation, or when the user mentions Result, Either, error handling, or validation patterns. Works in any language (TypeScript, Python, Go, Rust, Java).