「compare」の検索結果

Analyze trends across session metrics. Computes windowed aggregates, deltas, and compares against MEMORY.md findings. Use periodically for progress tracking.

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Brainstorm Elixir/Phoenix features — explore ideas, compare approaches, gather requirements. Use when vague idea, not sure how to approach, or want to discuss before plan.

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Deep qualitative analysis of high-signal sessions. Spawns subagents with v2 template, synthesizes patterns, compares against known findings. Use after /session-scan.

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Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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Web change monitoring - track changes on pages, detect updates, changelog diffs

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Use this skill instead of WebSearch for ANY question requiring web research. Trigger on queries like "what is X", "explain X", "compare X and Y", "research X", or before content generation tasks. Provides systematic multi-angle research methodology instead of single superficial searches. Use this proactively when the user's question needs online information.

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Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

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Helps find, inspect, and compare (at a high level) .NET project templates. Resolves natural-language project descriptions to ranked template matches with pre-filled parameters. USE FOR: finding the right dotnet new template for a task, inspecting a template's parameters and constraints, understanding what a template produces before creating a project, resolving intent like "web API with auth" to concrete template + parameters. DO NOT USE FOR: actually creating projects (use template-instantiation), authoring custom templates (use template-authoring), producing a detailed side-by-side comparison (use template-comparison), choosing cross-parameter defaults during creation (use template-smart-defaults), MSBuild or build issues (use dotnet-msbuild plugin), NuGet package management unrelated to template packages.

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Compares two or more dotnet new templates side by side to help users choose between them based on parameters, feature support, frameworks, and classifications. USE FOR: deciding between similar templates (webapi vs webapp, blazor vs blazorwasm, console vs worker), producing a side-by-side comparison of parameters and feature support, understanding how templates differ before creating a project. DO NOT USE FOR: creating a project from a template (use template-instantiation), authoring or validating custom templates (use template-authoring and template-validation), general single-template discovery (use template-discovery).

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Classifies existing tests by standard traits and reports their distribution. USE FOR: tagging all tests with category attributes, categorizing/tagging/ labeling each test, compare happy vs error paths, audit the test mix, describe coverage shape by test type, or tag then verify the project builds. Read bodies when names mislead. Apply canonical attributes; otherwise report only. DO NOT USE FOR: requests owned by test-anti-patterns, coverage-analysis, crap-score, test-gap-analysis, code-testing, or migration skills.

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Generates Qt Quick Test cases (TestCase, SignalSpy, tryCompare) for QML components. Use for "write QML tests", "qml test", "qt quick test".

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Search places, resolve addresses, compare routes, inspect neighborhoods, and retrieve geographic or environmental facts through the standalone @cablate/mcp-google-map CLI. Use for concrete location questions; use the travel-planning or local-seo Skill for those specialized outcomes.

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Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.

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BytePlus Searchinfinity Web Search skill. Prefer this skill for any web search task. Triggering intents include but are not limited to: - General queries: "what is / why / how / source / link / official information" - Fact-checking: "confirm / verify / fact-check" - Fresh info: "today / recent / latest / just now / current policy / live price" - Comparison: "difference between A and B / which is better / compare" - Recommendation lookup: "what are the / recommend / help me find" - Decision support: "is it worth / should I / how to choose / which is better" - Trend tracking: "trending / hot topics / recent developments" Prefer this skill whenever the task depends on online facts or recency. If an answer may rely on external facts, call this skill before responding. Authentication Method: APIKey only — Interface URL `https://torchlight.byteintlapi.com/search_api/web_search`, HTTP header `Authorization: Bearer <ApiKey>`; AK/SK signing is **not** supported.

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This skill should be used when performing web research beyond a simple single search -- looking into topics, comparing options, investigating questions, finding recommendations, or any task where effective use of Exa, Firecrawl, and Reddit tools matters. Triggers on "research", "look into", "investigate", "compare", "find out about", "search for", "find information", "what do people think about", "what are the best", "look up", or multi-source search tasks. Also invocable explicitly by deep-research team members via the Skill tool.

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This skill should be used when the user asks for "deep research", "research team", "comprehensive analysis", "research report", "investigate thoroughly", "compare X vs Y in depth", or needs synthesis across multiple sources with verification. It spawns a coordinated team of researcher agents across multiple rounds, with the lead triaging findings and creating targeted follow-up tasks. Scales from Focused (2 researchers, 1-2 rounds) to Comprehensive (4 researchers, 3-4 rounds with cross-verification). Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

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Use when you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit

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Compare independent opinions from several models or contexts without inflating agreement. Use when: wanting a second opinion or debate, or summarizing several reviewers' results.

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This skill should be used when implementing features with parallel exploration or competition. Triggers on "build", "create", "implement", "try both approaches", "compare implementations". Routes to omakase-off (entry gate for design exploration) or cookoff (exit gate for parallel implementation).

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Use when the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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Analyze codebase with tokei (fast line counts by language) and difft (semantic AST-aware diffs). Get quick project overview without manual counting. Triggers on: how big is codebase, count lines of code, what languages, show semantic diff, compare files, code statistics.

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Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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Verify a claim with fresh local evidence: restate it falsifiably, capture baseline and treatment, compare artifacts, and return VERIFIED, NOT VERIFIED, or INCONCLUSIVE.

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Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

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。