Configures deployment pipelines, manages environment variables, schedules cron jobs, applies security headers, implements caching strategies. Use when working with Docker, Vercel, AWS, Dockerfile, nginx.conf, or platform deployment configs.

Run and generate NX targets, configure project.json, and visualize dependency graphs. Use when you say: 'run affected tests', 'nx generate a library', 'configure project.json', or 'show dependency graph'.

Creates Contentful content types, queries entries via GraphQL/REST, runs CLI migrations, and manages assets and locales. Use when building or modifying Contentful content models, writing queries, or migrating content.

Creates Notion pages and databases, applies templates for research docs, ADRs, and specs, and manages team knowledge bases. Use when creating Notion pages, structuring databases, documenting decisions, or capturing research findings.

Implements a use case's backend: FastAPI router, Pydantic request/response schemas, service/domain layer, and SQLAlchemy models/queries. Use when the user asks to "implement the backend for UC-xxx", "build the API endpoint", "add a FastAPI route", "write the service layer", or mentions FastAPI implementation, Pydantic schemas, or SQLAlchemy queries for a specific use case.

Safety rules, workflows, and tool reference for debugging applications via IntelliJ debugger: breakpoints, debug sessions, stepping, evaluating expressions, inspecting runtime state. TRIGGER when: user wants to debug, investigate a bug, set breakpoints, inspect runtime behavior, step through code, or understand why code behaves unexpectedly at runtime. Trigger phrases (EN): "debug", "breakpoint", "step through", "step into", "step over", "why does this crash", "why is this null", "launch in debug mode", "trace execution", "run with debugger", "evaluate expression". Trigger phrases (RU): "отладить", "дебаг", "брейкпоинт", "почему падает", "пошагово пройти", "зайти в метод", "посмотреть значение переменной", "запустить в режиме отладки", "почему null", "стектрейс". Also trigger when user wants to understand runtime behavior or investigate incorrect behavior.

Translates Python functions into Dafny, adding types, pre/postconditions, and loop invariants sufficient for Dafny to verify. Use when formally verifying a Python algorithm, when the user wants machine-checked correctness for a critical function, or when building a verified reference implementation.

Finds and safely removes code that is never executed — unreachable branches, uncalled functions, unused classes, dead feature flags. Use when cleaning up after a feature removal, when the user suspects the codebase has accumulated cruft, or when reducing build/bundle size.

Uses mutation testing to find weak assertions and missing tests — injects small bugs and checks if the suite catches them, then generates tests targeting the surviving mutants. Use when coverage is high but bugs still ship, when auditing test quality, or when deciding if the suite is good enough.

Finds and removes redundant tests — tests that cover the same code, kill the same mutants, or assert the same behavior — to shrink suite runtime without losing coverage. Use when the test suite is slow, when tests have accumulated over years of copy-paste, or when CI costs are too high.

Generates configuration files for services and tools (app config, logging config, linter config, database config) from a brief description of desired behavior, matching the target format's idioms. Use when bootstrapping a new service, when the user asks for a config file for a specific tool, or when translating config intent between formats.

Advises on rollback strategies by analyzing what a deploy changes — recommending revert, roll-forward, feature-flag kill, or data repair depending on reversibility. Use during an incident when a deploy went bad, when designing a deploy pipeline and the user asks how to make it reversible, or when a migration needs an undo plan.

Generates unit tests for a function or class by analyzing branches, boundaries, and error paths — then emits test code in the project's existing framework and style. Covers happy path, edge cases, and failure modes with mocks for external dependencies. Use when writing tests for new code, when backfilling coverage on untested functions, when the user asks to generate tests, or when a coverage report shows specific gaps.

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.