「cover」の検索結果

Produce a standalone test plan by analyzing code for test coverage gaps and edge cases. Use when you need to create, generate, or draft a test plan for a branch, need to analyze test coverage, or need to identify what tests to write for specific files or directories. Does not produce a plain-language plan for a person to run tests by hand — use manual-test-planning for that. Does not write test code — use tdd to implement behavior test-first. Does not refine existing plans — use iterative-plan-review. Does not review code quality, security, or style — use code-review for full code review. Does not evaluate architectural testability or structural coupling — use architectural-analysis for architectural assessment.

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Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.

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Optimize YouTube videos for search and discovery. Generates SEO-optimized titles, descriptions, tags, hashtags, and chapters. Includes keyword research and competitor analysis. Use when publishing videos, improving discoverability, or optimizing existing content.

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。

n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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Search and discover Claude Code skills and MCP servers from marketplaces, GitHub repositories, and registries. Use when (1) user asks to find skills for a specific task, (2) looking for MCP servers to connect external tools, (3) user mentions "find skill", "search MCP", "discover tools", or "what skills exist for X", (4) before creating a custom skill to check if one already exists.

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Recover Vine video metadata (author, user ID, title, thumbnail) from vine.co oembed endpoint even though the Vine API and pages are dead. Use when: (1) you have Vine shortcode URLs (vine.co/v/XXXXX) and need to identify the creator, (2) the Vine page only shows a loading spinner, (3) archive-api.vineapp.com returns 404, (4) need to map Vine shortcodes to user IDs for cross-referencing against the divine.video database. The oembed endpoint is a still-live metadata oracle for the otherwise-dead Vine platform.

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Removes unnecessary code from a repository while preserving exactly the behavior exercised by a given test suite. Use this skill when you need to debloat, slim down, or remove unused code from a codebase based on test coverage. The skill analyzes which code elements (files, classes, functions, methods, branches, imports) are exercised by tests, identifies unreachable or unused code, and safely removes it while ensuring all tests continue to pass. Triggers when users ask to remove unused code, debloat based on tests, eliminate dead code guided by test coverage, or slim down a repository to only test-required functionality.

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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.

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Establish explicit traceability between formal specifications (preconditions, postconditions, invariants) and verified code components with their correctness proofs. Produce structured Markdown mapping reports showing verification coverage and proof evidence. Use when auditing formal verification, documenting verified systems, establishing traceability for certification, or when the user asks to map specifications to code, generate verification reports, or analyze verification coverage in Coq, Dafny, Isabelle, or other proof assistants.

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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.

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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.

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This skill applies brand guidelines to content creation. It should be used when the user asks to "write an email", "draft a proposal", "create a pitch deck", "write a LinkedIn post", "draft a presentation", "write a Slack message", "draft sales content", or any content creation request where brand voice should be applied. Also triggers on "on-brand", "brand voice", "enforce voice", "apply brand guidelines", "brand-aligned content", "write in our voice", "use our brand tone", "make this sound like us", "rewrite this in our tone", or "this doesn't sound on-brand". Not for generating guidelines from scratch (use guideline-generation) or discovering brand materials (use discover-brand).

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。

One-time autonomous design context discovery. Scans the codebase to extract design tokens, typography, colors, spacing, brand patterns, and tech stack, then writes a Design Context section to CLAUDE.md. Zero questions asked. Use when: 'setup design', 'design context', 'design tokens', 'brand discovery', 'design system scan', 'initialize design', 'teach design'.

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Discover and reduce task unknowns — blindspots, missing context, and unknown unknowns — before committing to a plan, implementation, review, or merge.

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。

Manage MCP servers - discover, analyze, execute tools/prompts/resources. Use for MCP integrations, capability discovery, tool filtering, programmatic execution, or encountering context bloat, server configuration, tool execution errors.

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Generate Jest unit tests for JavaScript/TypeScript with mocking, coverage. Use for JS/TS modules, React components, test generation, or encountering missing coverage, improper mocking, test structure errors.

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。

Dynamic MCP server discovery and code-mode execution via central registry. Use for multiple MCP integrations, tool discovery, progressive disclosure, or encountering MCP context bloat, changing server sets, large tool sets.

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, execute, and analyze tests for codebases, covering unit, integration, and end-to-end testing with coverage reporting. Use when the user requests testing or provides relevant inputs for this workflow.

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, edit, extend, and govern short videos with xAI's direct Grok Imagine Video API. Use for text-to-video, image-to-video, multi-reference video, natural-language video edits, video continuation, exact media-cost approval, asynchronous request recovery, Files/Batch integration, moderation review, and API privacy or residency decisions. Covers `grok-imagine-video` and image-only `grok-imagine-video-1.5`; excludes Grok consumer subscriptions, Grok on X, still-image generation, voice APIs, and third-party gateways.

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Produce music with MiniMax Music 2.6 and MiniMax cover/lyrics APIs for songs, instrumentals, AI-generated lyrics, reference-audio covers, video/social/ad soundtracks, artifact custody, rights checks, and production QA.

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Direct cinematic shots and coverage for AI-assisted content production. Use when an agent must translate story intent into framing, shot size, lens and perspective, camera position, movement, blocking, screen direction, lighting-aware coverage, continuity, shot specifications, or diagnose why generated or filmed shots do not cut together.

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。

Research WeChat Official Account articles, analyze account profiles, track article performance, and read comments. Covers article discovery, account profiling, engagement statistics, comment analysis, and content strategy research on China's dominant messaging ecosystem.

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Search videos, analyze channels, extract captions, read comments, and discover trends on YouTube through SandBase. Use when asked for YouTube research, video discovery, channel analysis, content strategy research, comment sentiment, or video transcript extraction.

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Research podcasts, episodes, and audio content by searching YouTube (where most podcasts crosspost), web sources, and transcript databases. Discover relevant shows, track guest appearances, and analyze topic coverage in the podcast ecosystem.

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。