Analyze and explain why Isabelle or Coq proofs fail, identifying the root cause such as type mismatches, missing assumptions, incorrect goals, unification failures, or inapplicable tactics. Use when the user encounters proof failures, error messages in formal verification, stuck proof states, or asks why their Isabelle/Coq proof doesn't work.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false positives in bug localization. Supports git repositories and provides explanations for why commits are identified as bug-introducing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate executable code together with formal proofs certifying safety and correctness properties in Isabelle/HOL or Coq. Use when building verified software, safety-critical systems, or when formal guarantees are required. Produces code with accompanying proofs for memory safety, bounds checking, functional correctness, invariant preservation, and termination. Supports extraction to OCaml/Haskell/SML and integration with existing codebases.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyzes and verifies semantic equivalence between two functions, classes, or modules by examining control flow, data flow, and observable behavior. Use when comparing code implementations (refactored vs original, different implementations of same functionality, migration verification), determining if two code artifacts produce identical behavior, identifying behavioral differences between code versions, or validating that code changes preserve semantics. Supports formal reasoning and symbolic execution approaches.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Proposes secure remediation strategies for detected security vulnerabilities including buffer overflows, injection risks, insecure deserialization, improper authentication, and unsafe cryptographic usage. Provides recommended security checks, safer API alternatives, design-level changes, code examples, trade-off analysis, and prioritized remediation plans. Does NOT automatically modify code unless explicitly requested.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Instruments authentication, authorization, and input-handling code paths to monitor security-relevant events and states at runtime. Use this skill when developers need to add security monitoring and logging to their code, including tracking authentication attempts (login/logout), authorization decisions (access control checks), input validation failures, session management events, and other security-critical operations. Supports Python, JavaScript/TypeScript, and Java with structured logging patterns. Triggers when users ask to add security instrumentation, monitor security events, log authentication/authorization, track security-sensitive operations, or add security observability to their codebase.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Check behavioral consistency between high-level hardware specifications and RTL implementations. Use when asked to check RTL consistency, verify RTL against spec, check hardware specification compliance, validate RTL implementation, find spec violations in RTL, check behavioral consistency, or when working with hardware designs that need verification against protocol specifications, timing requirements, or functional specifications in Verilog, VHDL, or SystemVerilog.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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 format, and test case documentation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Explains runtime errors and compilation failures with actionable debugging guidance. Use when Python or Java code throws runtime exceptions (NullPointerException, TypeError, AttributeError, etc.), compilation errors (syntax errors, type mismatches, import failures), or dependency issues. Analyzes error messages, stack traces, and code context to identify root causes and provide concrete fixes with examples. Distinct from test-related errors - focuses on errors during normal code execution and build processes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detect semantic-level bugs by analyzing whether code behavior matches its intended purpose inferred from function/variable names, comments, docstrings, and documentation. Use when users need to: (1) Find logic errors where implementation contradicts stated intent, (2) Identify off-by-one errors and boundary mismatches, (3) Detect inverted logic or wrong operators, (4) Catch missing edge case handling, (5) Verify code matches its documentation. Highlights mismatches between intent and implementation across multiple programming languages.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates structured context summaries for continuing work across AI sessions. Use when ending a session to enable seamless continuation in a new session without losing context.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate Isabelle or Coq proofs establishing partial or total correctness of imperative programs from code and formal specifications. Use when users need to: (1) Prove program correctness using Hoare logic, (2) Generate verification conditions from pre/postconditions, (3) Construct loop invariants and termination arguments, (4) Verify imperative programs with assignments, conditionals, and loops. Supports both partial correctness (if terminates, postcondition holds) and total correctness (terminates and postcondition holds) for both Isabelle/HOL and Coq.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate concise, structured summaries of requirements for quick team understanding. Use when analyzing requirements from text documents (MD, TXT, DOCX) or technical specifications to create bullet-point summaries that highlight core functionality and dependencies/constraints. Ideal for sprint planning, stakeholder updates, team onboarding, or any situation requiring rapid comprehension of requirement documents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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 assumptions and implicit dependencies, or (6) Preparing requirements for design, implementation, or verification. Operates interactively: analyze → ask clarifying questions → refine based on responses.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automatically derives TLA+ properties (invariants, safety, liveness) from natural-language requirements or structured requirement documents. Resolves ambiguities, asks clarifying questions for underspecified requirements, and outputs TLA+-compatible property definitions with semantic explanations. Use when translating system requirements, specifications, or behavioral constraints into formal TLA+ temporal logic properties for verification with TLC model checker.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Transforms natural language requirements (user stories, verbal descriptions, business rules) into formal specifications and constraints. Use when converting informal requirements into structured, testable specifications with explicit constraints. Outputs in multiple formats including BDD-style Given-When-Then, JSON Schema, and structured plain text requirements documents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Browser automation via Playwright for web testing, screenshots, form filling, scraping, and verification. Use when tasks require navigating websites, interacting with web pages, or testing web applications.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates comprehensive test scenarios from requirements including BDD/Gherkin scenarios, unit tests, integration tests, and end-to-end test cases. Use when converting requirements, user stories, or specifications into testable scenarios with full coverage including happy paths, error cases, edge cases, and boundary conditions. Outputs structured test suites ready for implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Compares old and new requirement documents, analyzes code repository impact, and generates detailed modification plans. Use when Claude needs to: (1) Compare requirement versions and identify changes, (2) Map requirement changes to code components, (3) Identify components to modify, delete, or add, (4) Analyze dependencies and integration points, (5) Assess test impact, (6) Generate comprehensive modification plans in Markdown format. Supports text/Markdown requirements and analyzes feature-level, functional, and API changes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Hardware verification tool for checking functional equivalence between two RTL designs (Verilog). Use when users need to: (1) Verify if two RTL versions are functionally equivalent, (2) Compare original vs. refactored RTL code, (3) Validate design changes or optimizations, (4) Identify semantic vs. cosmetic differences, (5) Generate counterexamples for non-equivalent designs. Analyzes interface alignment, state variables, logic differences, and produces detailed equivalence verdicts with plain language explanations. Particularly effective for design verification, code reviews, and regression testing of RTL modifications.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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 → concrete structures), algorithmic refinement (specifications → algorithms), and stepwise refinement with proof obligations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Locate root causes of failing regression tests by analyzing code changes, error messages, and test dependencies. Use when regression tests start failing after code changes, investigating test failures in CI/CD, debugging flaky tests, or understanding why previously passing tests now fail. Analyzes git diffs, stack traces, test output, and dependency changes to produce structured markdown reports ranking likely causes. Triggers when users ask to find why tests are failing, debug regression failures, investigate test breakage, or analyze failing test suites.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.