「unit」の検索結果

Use when writing, testing, or preparing ANY contribution or pull request to the JSONbored/awesome-claude (HeyClaude) repo — adding a community content entry (agent/MCP server/skill/hook/command/rule/guide/collection/statusline), or making a platform/code change (website, registry package, MCP package, scripts). The repo has two separate review paths: a private, one-shot "HeyClaude submission gate" that decides single-file content PRs, and the shared gittensory-orb bot that posts an advisory readiness/linked-issue comment on platform/code PRs but does not itself merge or close content PRs. Unlike JSONbored/gittensory and JSONbored/metagraphed, a linked issue is OPTIONAL here — do not import their mandatory-linked-issue rule into this repo. Invoke for any "contribute to / open a PR against / submit an entry to / fix a bug in awesome-claude (HeyClaude)" task.

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。

Interact with V2EX (v2ex.com) — browse hot and latest topics, read topic details and replies, search posts, create topics, reply to discussions, thank posts, favorite topics/nodes, follow members, daily check-in. Use this skill whenever the user mentions V2EX, v2ex.com, wants to browse tech forum topics, read V2EX discussions, search V2EX posts, check V2EX notifications, create or reply to V2EX topics, or look up V2EX users/nodes. Also trigger when the user pastes a V2EX URL (e.g. v2ex.com/t/12345), mentions a V2EX node name, or asks about Chinese tech community discussions. Keywords: V2EX, v2ex, 论坛, 技术社区, 节点, 主题, 回复.

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。

Create original short videos using the 表情校准｜固定身份下的动作单元序列 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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。

Product discovery and market research expert. Use when validating product ideas, conducting market research, user interviews, competitive analysis, or opportunity assessment. Covers JTBD, Kano model, and Value Proposition Canvas.

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。

Complete testing strategy covering TDD workflow, test pyramid, unit/integration/E2E/property testing, framework best practices (Jest, Vitest, pytest), mock strategies, and CI integration. Use when writing tests, reviewing test quality, or establishing testing standards.

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。

Use when a contributor wants to move beyond simple bug fixes into architectural improvements, technical debt discovery, design proposals, or module ownership opportunities.

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。

Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.

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。

Use this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.

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 user conversion funnels, calculate step-by-step conversion rates, create interactive visualizations, and identify optimization opportunities. Use when working with multi-step user journey data, conversion analysis, or when user mentions funnels, conversion rates, or user flow 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 EMERGING trends by connecting dots across unrelated sources. Monitor niche communities, academic research, GitHub, patents, funding, regulatory changes. Predict what will trend in 3-6 months based on weak signals.

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。

Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities

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。

Act! 365 integration. Manage Contacts, Groups, Opportunities, Tasks, Users. Use when the user wants to interact with Act! 365 data.

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。

Fix Dart/Flutter services where calling start() after stop() is a silent no-op because stop() sets a _disposed (or similar) flag that start()'s guard short-circuits on. Use when: (1) A repository/service/controller has startListening/stopListening, subscribe/unsubscribe, open/close, or similar lifecycle methods, (2) Re-opening the service after closing it appears to do nothing — no subscription, no events, no error, (3) Unit tests that only exercise a single mount/open/start cycle pass while the real app breaks on the second visit to a screen, (4) A boolean flag is used both for "in the middle of tearing down this instance forever" AND for "currently stopped, can be re-started". Common in Riverpod/Bloc-driven screens that wire startListening() in initState and stopListening() in dispose — the second time the user visits the screen, nothing happens.

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。

Create Quarterly Business Reviews with account health scores, usage analytics, ROI analysis, expansion opportunities, and risk mitigation

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 meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.

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 case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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 reduces bug-triggering test cases to minimal form while preserving the failure. Use when debugging with large, complex test cases that need simplification, reproducing bugs with minimal examples, or creating regression tests from verbose failure scenarios. Takes a failing test and systematically removes unnecessary inputs, steps, assertions, and code using delta debugging and other reduction algorithms. Supports unit tests, integration tests, input files, and multiple programming languages (Python, JavaScript, Java, C/C++).

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。

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 generates regression tests for Python codebases by analyzing changes between old and new code versions and their existing tests. Migrates tests to work with new code, generates tests for new functionality, and creates mocks for external dependencies. Supports unittest and pytest frameworks. Use when refactoring code, adding features, or ensuring backward compatibility.

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 updates regression tests based on interval analysis to maintain coverage of key program intervals. Use when code changes affect value ranges, conditionals, or control flow, and existing tests need updating to maintain interval coverage. Analyzes interval information from updated code, identifies coverage gaps, adjusts test inputs and assertions, removes redundant tests, and generates new tests for uncovered intervals. Supports Python, Java, JavaScript, and C/C++ with various test frameworks (pytest, JUnit, Jest, Google Test).

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。

Incrementally implement new features in Java repositories from natural language descriptions. Use when adding functionality to existing Java codebases (Maven or Gradle projects). Takes a feature description as input and outputs modified repository with implementation code, corresponding JUnit tests, and verification that all tests pass. Supports method additions, new class creation, and method modifications with proper Java 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。

Identify design quality issues in code including high coupling, low cohesion, God classes, long methods, and other code smells. Use when: (1) Reviewing code architecture and design quality, (2) Identifying refactoring opportunities, (3) Detecting God classes or classes with too many responsibilities, (4) Finding high coupling or low cohesion issues, (5) Analyzing code maintainability and technical debt. Detects coupling smells, cohesion problems, complexity issues, size violations, and encapsulation problems with actionable refactoring suggestions.

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。