Explorar skills

Extract abstract mathematical models from imperative code (C, C++, Python, Java, etc.) suitable for formal reasoning in Coq. Use when the user asks to model imperative code in Coq, create Coq specifications from imperative programs, extract mathematical models for verification, or translate imperative algorithms to Coq for formal reasoning and proof.

Generate comprehensive test scenarios, user stories, and acceptance criteria from requirements and specifications. Use this skill when planning testing efforts, writing user stories, creating test cases, documenting acceptance criteria, exploring edge cases, designing test coverage, or translating requirements into actionable scenarios. Supports BDD (Given-When-Then), Gherkin syntax, user story fo

Generate systematic refinement steps from high-level specifications to concrete implementations in Isabelle/HOL or Coq, preserving correctness obligations at each step. Use when working with formal verification, program refinement, proof development, or when translating abstract specifications into executable code while maintaining formal guarantees. Supports data refinement (abstract types → conc

Iteratively enhance user requirements into clear, complete, actionable specifications through analysis and clarification. Use when: (1) Users provide initial requirements that need refinement, (2) Requirements are vague, incomplete, or ambiguous, (3) Creating formal specifications from informal descriptions, (4) Identifying missing constraints, edge cases, or acceptance criteria, (5) Clarifying as

Automatically migrates build systems and CI/CD configurations to target platforms. Use when modernizing build infrastructure, switching CI/CD providers, or standardizing across projects. Supports common migration paths including Maven↔Gradle, npm↔Yarn, Travis CI→GitHub Actions, CircleCI→GitHub Actions, Jenkins→GitLab CI, and GitLab CI→GitHub Actions. Analyzes existing configuration, generates equi

Analyzes and optimizes code for better performance, memory usage, and efficiency. Use when code is slow, memory-intensive, or inefficient. Supports Python and Java optimization including execution speed improvements, memory reduction, database query optimization, and I/O efficiency. Provides before/after examples with detailed explanations of why optimizations work, complexity analysis, and measur

Validate API consistency between two versions of Python libraries. Use when you need to compare API behavior, signatures, and exceptions between library versions to identify breaking changes, incompatible modifications, and behavior differences. The skill performs static analysis of Python code, compares function signatures, class definitions, parameter types, return types, and generates a detaile

CLI-based browser automation with persistent page state using ref-based element interaction. Use when users ask to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Automatically simplify and minimize TLA+ specifications by reducing redundant state variables, merging equivalent actions, and minimizing invariants while preserving specified properties. Use when working with TLA+ specifications that need optimization, simplification, or reduction. Triggers when users ask to minimize, reduce, simplify, or optimize TLA+ specs, or when they want to remove redundanc

Analyze failing tests to detect functional bugs in code. Takes repository and failing test output as input, analyzes execution behavior, assertions, and stack traces to identify suspicious code regions and root causes. Use when debugging test failures, investigating regression bugs, or understanding why tests fail. Explains the bug mechanism, identifies affected code, and suggests fixes based on t

Collect, normalize, and structure execution traces from instrumented programs (strace, ltrace) into JSON format for downstream analysis. Use when working with system call traces, library call traces, or execution logs that need to be analyzed for debugging, test case reproduction, or verification. Supports parsing strace/ltrace output, filtering noise, extracting debug information, and preparing t

Generates hierarchical context files (CLAUDE.md) throughout a project directory tree, providing AI agents with directory-specific knowledge for better code understanding. Use when setting up a new project for AI-assisted development.