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Found 14 Skills
Remove LLM-generated code patterns that add noise without value. Use when reviewing diffs, PRs, or branches to clean up AI-generated code. Triggers include requests to "remove slop", "clean up AI code", "review for AI patterns", or checking diffs against main for unnecessary verbosity, redundant checks, or over-engineering introduced by LLMs. Language-agnostic.
Apply Clean Architecture + DDD + Hexagonal patterns to backend services. Use when designing APIs, microservices, domain models, aggregates, repositories, bounded contexts, or scalable backend structure. Triggers on DDD, Clean Architecture, Hexagonal, ports and adapters, entities, value objects, domain events, CQRS, event sourcing, repository pattern, use cases, onion architecture, outbox pattern, aggregate root, anti-corruption layer. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).
iLEAP technical specs for exchanging logistics emissions data (ISO 14083 / GLEC Framework) via the PACT protocol. Use when implementing iLEAP APIs, data models (ShipmentFootprint, TCE, TOC, HOC, TAD), PACT DataModelExtensions, or conformance testing. Language-agnostic.
Analyze codebase structure for reverse engineering. Identify entry points, dependencies, modules, and components with file:line traceability. Creates manifest.json for pipeline chaining with Phase 2 (logic visualization). Language-agnostic with optional language reference files. Use when: reverse engineer, analyze structure, structure analysis, codebase analysis, re-structure-analysis.
Comprehensive testing doctrine for software and AI systems — covers positive patterns, anti-patterns, gates for coding agents writing tests, CI discipline, and an LLM/agent evaluation primer. Use when authoring or reviewing tests, adding mocks, deciding test placement, generating tests via agents, debugging flaky CI, designing eval suites for LLM features, or rebuilding a brittle test suite. Contains 12 positive patterns (selector hierarchy, table-driven, builders, real-system gates), 25 anti-patterns across Brittleness, Flakiness, Mock-misuse, Process, and AI-specific families, 7 mandatory gates for agents writing tests, flaky-test taxonomy with quarantine workflow, contract / property / mutation testing patterns, and an oracle-ladder primer for LLM-as-judge and agent eval. Language-agnostic — pseudo-code only. Don't use for general code review, library-specific debugging unrelated to tests, non-testing CI pipeline design, or production observability.
Comprehensive Contentful REST API guide. Covers Content Management API (CMA) for creating/updating content, Content Delivery API (CDA) for fetching published content, Preview API, Images API, and GraphQL API. All examples use curl/HTTP — language-agnostic.
Use when consuming external APIs, integrating third-party services, generating type-safe API clients, implementing authentication flows, or working with OpenAPI/Swagger, GraphQL, or REST specs. TypeScript-primary with language-agnostic patterns.
Create a language-agnostic ghost package (spec + portable tests) from an existing repo by extracting SPEC.md, exhaustive tests.yaml (operations and/or scenarios), INSTALL.md, README.md, VERIFY.md, and upstream LICENSE files with provenance and regeneration instructions. Use when prompts say "$ghost", "ghostify this repo", "spec-ify/spec-package this library", "ghost library", or ask to extract portable spec/tests for libraries or tool-using agent loops (scenario testing); do not use for implementation work or editing skills.
Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.
Unix-composable CLI design patterns. Use when building CLI tools, designing command trees, implementing output layers, or testing CLI behavior. Covers stream separation (stdout/stderr), format flags (--json/--plain), exit codes, TTY detection, composability, and error design. Language-agnostic principles; TypeScript implementation patterns in resources/. For API design (REST, HTTP), see api-design.
Deep code simplification, refactoring, and quality refinement. Analyzes structural complexity, anti-patterns, and readability debt, then applies targeted refactoring preserving exact behavior. Language-agnostic: Python, Go, TypeScript/JavaScript, Rust. Use this skill when the goal is simplification and clarity rather than bug-finding. Triggers on: "simplify this code", "clean up my code", "refactor for clarity", "reduce complexity", "make this more readable", "code quality pass", "tech debt cleanup", "run the code refiner", "simplify recent changes", "this code is messy", "too much nesting", "this function is too long", "clean this up before I PR it", "tidy up my code", cyclomatic complexity, cognitive complexity, code smells.
Apply language-agnostic naming conventions using the A/HC/LC pattern. Use when naming variables, functions, or reviewing code for naming consistency.