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cccskills

「comparison」の検索結果

928 件 ・ 関連度順

概要と使いどころ

Generates complete FAERS pharmacovigilance study designs for multi-drug or class-level safety comparison inside one predefined SOC or AE family using active comparators, disproportionality analysis, subgroup characterization, and reviewer-facing evidence control.

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。

aipoch/medical-research-skills1,9482026年9月17日 更新

Side-by-side comparison of two contract versions or two different contracts with change tracking, favorability analysis, and risk assessment

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。

zubair-trabzada/ai-legal-claude1,7822026年3月27日 更新

Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake. Answers questions about storage access tiers (hot, cool, cold, archive), when to use each tier, and tier comparison. Provides object storage, SMB file shares, async messaging, NoSQL key-value, and big data analytics. Includes lifecycle management. USE FOR: blob storage, file shares, queue storage, table storage, data lake, upload files, download blobs, storage accounts, access tiers, storage tiers, hot cool cold archive, storage tier comparison, when to use storage tiers, lifecycle management, Azure Storage concepts. DO NOT USE FOR: SQL databases, Cosmos DB (use azure-prepare), messaging with Event Hubs or Service Bus (use azure-messaging).

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。

microsoft/azure-skills1,5662026年10月13日 更新

Produce a structured competitive analysis for any product or market. Use when asked for a competitor analysis, competitive teardown, market comparison, SWOT, or positioning map. Generates a structured teardown with positioning map, feature comparison, messaging gaps, and strategic recommendations. For a full landscape doc with feature matrix and win/loss analysis use competitive-analysis instead.

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。

mohitagw15856/pm-claude-skills1,4492026年10月13日 更新

oil-ui

無料

Design, improve, and review interfaces for websites, apps, dashboards, and components: explore distinct design directions, compare styles side by side, and refine visual hierarchy against real screenshots. Delivers comparison pages, design notes, mockups, or working interfaces. Use for new interface design, style comparison, visual polish, screenshot recreation, or interface review. Not for business logic, APIs, builds, or code cleanup without visible UI changes. If oil-ui-pro is installed, use oil-ui-pro instead. 设计、改进和评审网站、App、后台与组件的界面,完成设计方向探索、多风格同屏比较、视觉层级和基于实际画面的迭代;按请求交付对比页、设计说明、设计稿或可运行界面。当用户需要新界面设计、比较不同设计风格、视觉精修、截图还原或界面评审时使用。只负责设计判断,组件归属、数据流、状态正确性和测试等代码实现质量不在范围内。不用于纯业务逻辑、接口、构建部署、无界面行为变化的代码整理、单独绘制普通插画或操作现有网站。已安装 oil-ui-pro 时交给 oil-ui-pro。

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。

oil-oil/oil-ui1,4352026年10月13日 更新

Captures before/after screenshots of web pages or elements for visual comparison. Use when user says "take before and after", "screenshot comparison", "visual diff", "PR screenshots", "compare old and new", or needs to document UI changes. Accepts two URLs (file://, http://, https://) or two image paths.

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。

michaelshimeles/skills1,3172026年10月4日 更新

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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。

GPTomics/bioSkills1,2192026年8月15日 更新

Use when comparing Scenario models on the same brief through MCP: a bake-off with shared prompts and inputs, cost, generation time and quality measured side by side, choosing a default model, testing a new release against the incumbent, or finding which model handles a style, edit, or reference image best. Keywords: model comparison, benchmark, bake-off, A/B test, contact sheet, cost per asset, latency.

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。

scenario-labs/skills9962026年10月13日 更新

Use when retexturing an existing 3D asset with PATINA PBR materials through the Scenario MCP server: a GLB or Blender scene (an image-to-3D result with flat colors, a prop, a diorama) whose surfaces need basecolor, normal, roughness, metalness, and height maps per material family with geometry untouched, or a matched before-and-after comparison film rendered in Blender with one camera path, lighting, and cut. Keywords: PATINA, PBR retexture, material families, Blender EEVEE, comparison video.

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。

scenario-labs/skills9962026年10月13日 更新

Extract structured data from multiple documents into comparison matrix with citations. Use for bulk document 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。

borghei/Claude-Skills9052026年10月7日 更新

Use when the user wants to compare or benchmark multiple LLMs/agents arena-style but it's unclear which specific workflow fits — a general-purpose win-rate comparison on a custom task, or a benchmark specifically about reference/citation hallucination rate. Also use when the user mentions model arena, agent arena, pairwise model comparison, win-rate ranking, or comparing models on a task and hasn't specified whether that task is generic or about citation accuracy. This skill is the entry router for the arena-eval suite: it asks one diagnostic question when needed, then recommends the workflow or workflows needed to cover the request.

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。

agentscope-ai/OpenJudge8742026年9月11日 更新

Performance scorecard design, post-matter feedback collection, QBR preparation, and firm comparison for in-house legal ops teams evaluating outside counsel. Design a scorecard framework calibrated to your team's maturity level. Produce post-matter feedback forms for supervising attorneys to complete at matter close. Prepare a QBR pack with agenda, data summary, and talking points for structured business reviews. Produce a side-by-side firm comparison for panel decision-making. Trigger on: 'design a performance scorecard', 'how do we evaluate our firms', 'build us a scorecard', 'post-matter feedback form', 'feedback at matter close', 'collect feedback on the firm', 'QBR', 'quarterly business review', 'prep for the firm review', 'what should I raise with the partner', 'compare the firms', 'which firm is performing better', 'rank our panel', 'firm performance data', 'scorecard framework', 'evaluate outside counsel', 'annual firm review', 'how are our firms performing'.

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。

lawve-ai/awesome-legal-skills8642026年10月3日 更新

Deep competitive analysis for iOS/macOS apps including feature comparison, pricing analysis, strengths/weaknesses, market positioning, and differentiation opportunities. Use when user asks for competitive analysis, competitor research, feature comparison, market positioning, or wants to understand competition in detail.

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。

rshankras/claude-code-apple-skills7942026年7月24日 更新

Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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。

nexscope-ai/Amazon-Skills7592026年8月26日 更新

Write SEO-optimized affiliate blog articles, product reviews, comparison posts, listicles, and how-to guides. Triggers on: "write a blog post about", "review of [product]", "best [category] article", "comparison blog", "affiliate blog", "SEO article", "write a review", "product roundup", "blog content for affiliate", "how to use [product] blog post", "listicle about [category]", "[product] vs [product] blog", "content for my affiliate site".

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。

Affitor/affiliate-skills7102026年9月15日 更新

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.

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。

managedcode/dotnet-skills4862026年10月13日 更新

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).

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。

managedcode/dotnet-skills4862026年10月13日 更新

Creates .NET projects from templates with validated parameters, smart defaults, Central Package Management adaptation, and latest NuGet version resolution. USE FOR: creating new dotnet projects, scaffolding solutions with multiple projects, installing or uninstalling template packages, creating projects that respect Directory.Packages.props (CPM), composing multi-project solutions (API + tests + library), getting latest NuGet package versions in newly created projects. DO NOT USE FOR: finding templates (use template-discovery), producing a detailed side-by-side comparison of templates (use template-comparison), authoring custom templates (use template-authoring), deciding cross-parameter defaults such as which framework to pair with native AOT or whether to keep HTTPS when auth is enabled (use template-smart-defaults), modifying existing projects or adding NuGet packages to existing projects.

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。

managedcode/dotnet-skills4862026年10月13日 更新

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

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。

jabrena/plinth4482026年10月13日 更新

Trigger for: FFP IGCE, firm-fixed-price estimate, FFP cost model, proposed FFP rate validation, wrap-rate analysis, Agency BPA rate comparison, price-reasonableness memo, or fair-and-reasonable analysis. Build auditable Firm-Fixed-Price federal estimates using BLS OEWS wages, layered fringe/overhead/G&A/profit, GSA CALC+ positioning, and GSA Per Diem travel. Use for FFP-by-period, FFP-by-deliverable, SOW/PWS decomposition, implied-multiplier analysis, and fixed-price scenario comparisons. Do NOT use for Labor Hour, T&M, or cost-reimbursement IGCEs (use IGCE Builder LH/T&M or IGCE Builder CR). Do NOT use for grant budgets. Requires the bls-oews, gsa-calc, and gsa-perdiem MCP servers.

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。

aiskillstore/marketplace4322026年10月13日 更新

Trigger for: Other Transaction or OT should-cost, cost estimate, cost-share analysis, milestone pricing, funding profile, proposed-price comparison, prototype price analysis, research OT budget, production follow-on OT estimate, or OT price-reasonableness support under 10 U.S.C. 4021 or 4022. Build auditable milestone-based .xlsx workbooks using BLS OEWS wages, CALC+ positioning, GSA Per Diem, materials, cost sharing, and agreement-specific fees. Use for pre-solicitation budgets and proposal comparisons. Do NOT use for FAR-based IGCEs, OT project descriptions, grants, or an Agreements Officer's determination. Requires the bls-oews, gsa-calc, and, when travel applies, gsa-perdiem MCP servers.

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。

aiskillstore/marketplace4322026年10月13日 更新

Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.

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。

Adkid-Zephyr/oral-paper-skill3882026年9月18日 更新

Compare cost estimates across projects, versions, and scenarios. Identify variances, benchmark against standards, and generate comparison reports.

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。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.

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。

OneWave-AI/claude-skills3402026年10月2日 更新