「coverage」の検索結果

Analyzes code change impact with risk scoring and affected-node mapping. Use before merging to understand what a change touches and what lacks test coverage.

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。

Updates, generates, and validates tests using git-workspace context and TDD/BDD methodology. Use when code changes require new or updated test coverage.

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。

Audits the DSA problem bank for coverage gaps and proposes new YAML entries. Use when refreshing the problem bank during update-plugins runs.

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。

Guide minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.

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。

Enforce evidence bar, coverage gates, and regression guards. Use when adding tests or claiming done. Do not use to run suites; use night-market-operations.

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。

Business-logic flaw hunting - workflow/state bypass, price/quantity tampering, negative/overflow values, coupon/refund abuse, mass assignment, and logic races. The top-paying bug class with no scanner coverage. Wiki-first, FIND schema output.

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 screenshot coverage for screen-level Compose previews from the current diff or a specific target, check required visual and data variations, and produce a compact visual 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。

Build privacy KPI dashboards tracking DSAR volume and response time, breach count and severity, DPIA completion rate, training coverage, and consent rates. Includes metric definitions, data collection patterns, visualization designs, and executive reporting templates for privacy program measurement.

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。

Coordinates credit monitoring and identity theft protection services for individuals affected by a data breach. Covers vendor selection criteria, enrollment logistics, coverage duration (12-24 months), identity theft insurance options, communication to affected individuals, and enrollment rate tracking. Keywords: credit monitoring, identity protection, breach response, Experian, enrollment, identity theft insurance.

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。

Produce a plain-language manual test plan from the context supplied to it — an executive summary, a high-level list of named tests, and a detail section per test with the steps a person follows by hand and the outcomes they should expect. Use when you want to create, draft, generate, or outline a manual test plan, manual QA steps, hands-on verification steps, or an acceptance walkthrough for a feature, change, branch, plan, or PR. When the plan holds more than five tests and at least two natural categories emerge, both the test list and the detail sections are organized under plain-language categories. When nothing in the supplied context can be manually tested, it says so and asks for more context instead of producing a document. Does not analyze code for automated test coverage gaps — use automated-test-planning. Does not write test code — use tdd. Does not review code quality — use code-review. Does not stress-test an existing plan — use iterative-plan-review.

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。

Learning designer quality check for Canvas LMS courses. Audits module structure, content completeness, publishing state, date consistency, and rubric coverage. Use when asked to "QC a course", "is this course ready", "pre-semester check", or "quality review".

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。

Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property 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。

Analyzes test suites and suggests optimal test execution order based on code change impact and project requirements. Use when optimizing CI/CD pipelines, deciding which tests to run before committing, or selecting critical tests under time constraints. Prioritizes tests by analyzing recently modified files, test dependencies, and coverage overlap. Outputs detailed rankings with priority scores and reasoning to help teams run the most impactful tests first.

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。

Builds traceability matrices connecting requirements to design documents to source code implementation, tracking the complete development lifecycle. Use when you need to verify implementation completeness, ensure all requirements are implemented in code, generate compliance documentation, audit requirement coverage, identify orphaned code, or create traceability reports for stakeholders. Supports parsing requirements from Markdown, Word, and PDFs; extracting design from architecture docs and API specs; and scanning source code for implementations. Outputs to Markdown tables, Excel/CSV, and HTML visualizations.

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 formal verification artifacts (Isabelle, Coq, Dafny, etc.) and produce structured reports identifying the precise boundary between verified, assumed, and unverified components. Use when assessing verification coverage, understanding trust boundaries, auditing formal proofs, or documenting verification scope. Reports explicitly list verified code, assumptions, axioms, trusted computing base, and unverified components. Conservative and explicit about verification status without attempting to repair or mask gaps.

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 generates comprehensive unit tests for functions, classes, and modules. Use when you need to create tests for Python (pytest, unittest) or Java (JUnit, TestNG) code. Generates tests with comprehensive coverage including happy paths, edge cases, and error conditions. Analyzes existing test patterns in the codebase to match style and conventions. Supports mocking, parameterized tests, fixtures, and follows best practices for each framework.

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 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.

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。

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.

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 generate regression tests for Java codebases by analyzing changes between old and new code versions. Use when users need to: (1) Generate tests after refactoring or code changes, (2) Ensure previously tested behavior still works in new versions, (3) Cover modified or newly added code paths, (4) Migrate existing tests to work with updated APIs or signatures, (5) Maintain test coverage during code evolution. Supports JUnit and TestNG frameworks with unit tests, parameterized tests, and exception testing patterns.

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。

Produces comprehensive summaries and insights about legacy codebases to help understand unfamiliar code. Use when onboarding to a new project, planning refactoring efforts, assessing code for acquisition/migration, or generating documentation for undocumented systems. Analyzes architecture, dependencies, code quality issues, and test coverage. Creates high-level overviews with architecture diagrams, key components, entry points, and actionable insights for understanding and improving legacy code.

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。

Conduct comprehensive code reviews identifying bugs, security issues, performance problems, code quality concerns, and best practice violations. Use when reviewing pull requests, examining code changes, evaluating new code, assessing code quality, or providing feedback on implementations. Analyzes code for correctness, security vulnerabilities, performance bottlenecks, maintainability issues, test coverage, documentation quality, and adherence to coding standards. Produces structured markdown reviews with categorized findings, severity ratings, specific examples, and actionable recommendations. Triggers when users ask to review code, check pull requests, evaluate implementations, find bugs, or assess code quality.

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。

Generate test assertions from existing code implementation. Use when the user has implementation code without tests or incomplete test coverage, and needs assertions synthesized by analyzing the code's behavior, inputs, outputs, and state changes. Supports Python (pytest/unittest), Java (JUnit/AssertJ), and JavaScript/TypeScript (Jest/Chai). Handles equality checks, collections, exceptions, and state 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。