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概要と使いどころ
Automatically generate TLA+ specifications from program code, repositories, or system implementations. Use when asked to generate TLA+ spec, create TLA+ specification from code, convert program to TLA+, formalize system in TLA+, extract TLA+ model from code, or when working with formal specification of concurrent systems, distributed systems, protocols, algorithms, or state machines that need to be verified.
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。
Systematic external reference searching across documentation, open-source repositories, and web resources. Use when working with unfamiliar libraries, APIs, or when needing production-quality implementation examples.
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。
Analyzes a repository and its test suite to generate customized mutation operators tailored to the project. Use this skill when setting up mutation testing, improving test quality, or generating project-specific mutants. Considers language constructs, business logic patterns, API calls, data types, and code complexity to produce effective mutation operators that maximize test sensitivity while avoiding trivial or equivalent mutations. Generates prioritized operator sets (high/medium/low value) with impact summaries. Triggers when users ask to generate mutation operators, customize mutation testing, analyze code for mutation opportunities, or create project-specific mutants.
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。
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.
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。
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.
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。
Instruments programs to capture execution traces specifically for reproducing reported bugs, enabling consistent replay and diagnosis of failures. Use this skill when you need to reproduce a bug, capture execution traces for debugging, instrument code to record program behavior, generate replay scripts for bug reproduction, diagnose hard-to-reproduce failures, or perform deterministic replay of program execution. Triggers when users ask to instrument code for tracing, capture execution traces, reproduce bugs, generate replay scripts, or enable deterministic debugging.
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。
Validate API consistency between two versions of Java libraries. Use when you need to compare API behavior, signatures, and exceptions between Java library versions to identify breaking changes, incompatible modifications, and behavior differences. The skill performs static analysis of Java code, compares method signatures, class definitions, interface changes, parameter types, return types, exception declarations, and generates a detailed JSON report with breaking changes, warnings, and migration guidance. Supports Java libraries and packages.
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。
Generate test cases using metamorphic testing by applying transformations based on metamorphic properties. Use when you need to expand test suites, test programs without oracles, validate mathematical or algorithmic properties, or detect subtle bugs through input-output relationships. The skill takes a program, original test cases, and metamorphic properties as input, generates new test cases by applying transformations, executes tests, verifies outputs satisfy properties, reports violations and anomalies, and outputs an expanded test suite with property coverage summary. Supports multiple programming languages and property types.
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。
Takes a Python repository and natural language feature description as input, implements the feature with proper code placement, generates comprehensive tests, and ensures all tests pass. Use when Claude needs to: (1) Add new features to existing Python projects, (2) Implement functions, classes, or modules based on requirements, (3) Modify existing code to add functionality, (4) Generate unit and integration tests for new code, (5) Fix failing tests after implementation, (6) Ensure code follows existing patterns and conventions.
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。
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.
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。
Generate integration tests for multiple interacting components in Python. Use when testing interactions between: (1) Multiple services or APIs (REST/GraphQL endpoints, microservices), (2) Database operations with repositories/ORMs (SQLAlchemy, Django ORM), (3) External services (payment gateways, email services, third-party APIs), (4) Message queues and event-driven systems, (5) Full stack workflows (API + database + business logic). Provides test structure templates, fixtures, test data builders, and patterns for pytest-based integration testing.
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。
Identify and analyze unused or redundant code including unused functions/methods, unused variables/imports, unreachable code, and redundant conditions. Use when cleaning up codebases, improving maintainability, reducing technical debt, or conducting code quality audits. Analyzes Python code using AST analysis and produces markdown reports listing dead code locations with line numbers, severity ratings, and recommendations. Triggers when users ask to find dead code, remove unused code, identify unused imports, find unreachable code, or clean up redundant logic.
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。
Optimize test suites using mutation testing to maximize mutation kill rate with minimal tests. Use when you need to reduce test suite size while maintaining quality, identify redundant tests, improve mutation coverage, or validate test effectiveness. The skill analyzes test coverage, execution intervals, and redundancy using mutation operators, selects or generates a minimal subset of tests that maximizes mutation kill rate, and outputs an optimized test suite with detailed reports showing killed and surviving mutants. Supports multiple programming languages and mutation testing frameworks.
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。
Automatically performs git bisect to identify the first bad commit that introduced a bug or failure. Use when debugging regressions, tracking down when a test started failing, or identifying which commit broke functionality. Handles flaky tests with retry logic and provides comprehensive reports with bisect logs and confidence levels.
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。
Analyze detected vulnerabilities to assess realistic exploitability by examining control flow, input sources, sanitization logic, and execution context. Use when users need to: (1) Determine if a vulnerability is actually exploitable in practice, (2) Assess severity and impact of security issues, (3) Prioritize vulnerability remediation, (4) Understand attack vectors and exploitation conditions, (5) Generate exploitability reports with proof-of-concept scenarios. Focuses on injection vulnerabilities (SQL, command, XSS, path traversal, LDAP) with detailed analysis of reachability, controllability, sanitization, and impact.
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。
Unified GitHub triage for issues AND PRs. Classifies open items, answers questions from codebase, analyzes bugs, reviews PRs, and produces a structured triage report. Triggers: 'triage', 'triage issues', 'triage PRs', 'github triage'.
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。
Profile programs at the function/method level to identify performance hotspots, bottlenecks, and optimization opportunities. Records execution time, memory usage, and call frequency for each interval. Generates actionable recommendations and visualizations. Use when users need to (1) analyze program performance, (2) identify slow functions or bottlenecks, (3) optimize execution time or memory usage, (4) profile Python, Java, or C/C++ programs with test cases or workload scenarios, or (5) generate performance reports with flame graphs and recommendations.
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。
Analyze differences in program intervals between two versions of a program (old and new) to identify added, removed, or modified intervals. Use when comparing program versions, analyzing variable ranges, detecting behavioral changes in numeric computations, validating refactorings, or assessing migration impacts. Supports optional test suite integration to validate interval changes. Generates comprehensive reports highlighting intervals requiring further testing or verification.
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。
Analyzes surviving mutants from mutation testing to identify why tests failed to detect them. Takes repository code, test suite, and mutation testing results as input. Identifies root causes including insufficient coverage, equivalent mutants, weak assertions, and missed edge cases. Automatically generates actionable test improvements and new test cases. Use when analyzing mutation testing results, improving test suite effectiveness, investigating low mutation scores, generating tests to kill surviving mutants, or enhancing test quality based on mutation analysis.
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。
Identify the precise location of bugs in source code, modules, and systems. Use this skill when debugging applications, investigating test failures, analyzing error reports, tracing runtime issues, or performing root cause analysis. Analyzes stack traces, error messages, failing tests, and code patterns to pinpoint buggy functions, classes, files, or modules with confidence rankings and supporting evidence.
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。
Search code repositories for code related to a given code snippet, ranking results by call chain similarity, textual similarity, and functional similarity. Use when finding related code, locating similar implementations, discovering code dependencies, or identifying code that performs similar operations. Outputs ranked file lists with matching code snippets and relevance scores.
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。
Generate code fixes and patches from bug reports, failing test cases, error messages, and stack traces. Use this skill when debugging code, fixing test failures, addressing GitHub issues, resolving runtime errors, or patching security vulnerabilities. Analyzes the bug context, identifies root causes, and generates precise code patches with explanations and validation steps.
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。
Automatically complete partial code snippets while satisfying semantic constraints including variable types, invariants, pre/post-conditions, interface contracts, and expected input/output behavior. Use when users provide incomplete code with specific requirements like "complete this function that takes a list and returns sorted unique elements" or "fill in this method body that must maintain the invariant that x stays positive" or "implement this interface method with these type constraints." Produces compilable, executable code with tests and a constraint satisfaction report.
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。
Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.
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。