「specification」の検索結果

Automatically repair C/C++ code violations detected by TLA+ model checking. Takes a program, TLA+ specification, and TLC counterexample trace as input, then generates minimal code modifications to eliminate the violation. Use when: (1) TLC model checker reports an invariant violation, deadlock, or temporal property failure, (2) You have a counterexample trace and need to fix the corresponding code, (3) You need to understand how a TLA+ violation maps to program-level bugs, (4) You want to validate repairs by re-running TLC. Supports safety properties (invariants), liveness properties (temporal logic), and deadlock detection.

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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.

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Extract abstract mathematical models from functional code (Haskell, OCaml, F#) for formal reasoning in Isabelle/HOL. Use when users need to: (1) Convert functional programs to Isabelle definitions, (2) Extract high-level algorithm essence from implementation code, (3) Generate formal specifications and properties from code, (4) Create verification-ready models that capture mathematical properties while abstracting away implementation details. Focuses on structural recursion, algebraic data types, higher-order functions, and invariant extraction.

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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.

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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.

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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.

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Translate Python programs into equivalent Dafny code, preserving program semantics and ensuring the generated code is well-typed, executable, and verifiable. Use when the user asks to convert Python code to Dafny, port Python programs to Dafny, add formal verification to Python code, or create Dafny versions of Python algorithms with specifications.

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Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.

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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.

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Generates complete modules and components from interface contracts and specifications. Use when Claude needs to build larger software components, implement interfaces, create service layers, or generate complete modules with multiple related classes. Supports Python (with type hints and protocols) and Java (with interfaces and abstract classes). Trigger when users request "implement this interface", "generate a module for", "create a service layer", "build a component that implements", or provide interface definitions that need implementation.

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Automatically identify potential boundary and exception cases from requirements, specifications, or existing code, and generate comprehensive test cases targeting boundary conditions, edge cases, and uncommon scenarios. Use this skill when analyzing programs, code repositories, functions, or APIs to discover and test corner cases, null handling, overflow conditions, empty inputs, concurrent access patterns, and other exceptional scenarios that are often missed in standard testing.

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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.

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Automatically identify metamorphic properties (symmetry, linearity, additivity, input invariances) from programs or functions. Use when generating metamorphic tests, discovering program properties, validating transformations, or creating test oracles without explicit specifications. Analyzes control flow, data flow, and sample executions to output structured properties for metamorphic test generation and verification.

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Uses abstract interpretation to automatically infer loop invariants, function preconditions, and postconditions for formal verification. Generates invariants that capture program behavior and support correctness proofs in Dafny, Isabelle, Coq, and other verification systems. Use when adding formal specifications to code, generating verification conditions, inferring contracts for functions, or discovering loop invariants for proofs.

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Automatically repair buggy code and generate comprehensive tests for Python, Java, and C++ programs. Use when users need to fix logic errors or runtime errors in functions, modules, or repositories. Accepts specifications via natural language descriptions, existing test cases, or input/output examples. Generates corrected code, creates or updates tests to verify correctness and prevent regressions, and produces a detailed report explaining the bug, fix, and testing strategy. Triggers on requests like "fix this bug", "repair this code", "debug this function", or "this code is broken".

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Design and review APIs with suggestions for endpoints, parameters, return types, and best practices. Use when designing new APIs from requirements, reviewing existing API designs, generating API documentation, or getting implementation guidance. Supports REST APIs with focus on endpoint structure, request/response schemas, authentication, pagination, filtering, versioning, and OpenAPI specifications. Triggers when users ask to design, review, document, or improve APIs.

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Identifies and analyzes conflicts in software requirements including logical contradictions, technical incompatibilities, resource constraints, timeline issues, data conflicts, and stakeholder priority mismatches. Use when reviewing requirement sets, specifications, user stories, or project plans to detect conflicts that could block implementation or cause rework. Provides detailed conflict analysis with resolution strategies and impact assessment.

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Create Request for Proposals with requirements specifications, evaluation criteria, scoring rubrics, timelines, and compliance requirements

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Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.

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Verifies API contracts between services using consumer-driven contracts, schema validation, and tools like Pact. Use when testing microservices communication, preventing breaking changes, or validating OpenAPI specifications.

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Creates professional API documentation using OpenAPI specifications with endpoints, authentication, and interactive examples. Use when documenting REST APIs, creating SDK references, or building developer portals.

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Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.

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Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

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End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on "absolute work", "build this end-to-end", "plan and build", "break this into tasks", "grill me on this plan", "pick up this ticket", "run this migration".

日本語の概要は準備中です。原文の説明を表示しています。