「comparison」の検索結果

"Calculate and analyze promotional ROI including incremental sales lift, margin impact, and promo type comparison. Use this skill when the user needs to evaluate whether a promotion was profitable, compare promotion types, simulate promo scenarios, or optimize promotional spending — even if they say 'was this sale worth it', 'which promo type works best', 'are we giving away too many discounts', or 'plan our next promotion'.".

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 publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.

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 competitors and generate a positioning and messaging comparison with content gaps, opportunities, and threats. Use when building sales battlecards, when finding positioning gaps and messaging angles competitors haven't claimed, or when a competitor makes a move and you need to assess the impact.

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 your competitors and build an interactive battlecard. Outputs an HTML artifact with clickable competitor cards and a comparison matrix. Trigger with "competitive intel", "research competitors", "how do we compare to [competitor]", "battlecard for [competitor]", or "what's new with [competitor]".

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。

Provides context about the regelrecht Figma design system and helps with Figma-related tasks like component lookups and design comparisons. Use this skill proactively when: implementing UI components, looking up Figma node IDs, comparing code with Figma designs, downloading Figma assets, or working with design tokens. Activate automatically when user mentions Figma, design system, component styling, or asks about RR-Components.

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。

Search, screen, deeply read, synthesize, and publish recent research papers for a user-specified topic, window, count, quality/attention balance, and language. Use for evidence-anchored literature digests, cross-paper comparisons, original-language excerpts, Chinese explanations, guided Feishu setup, or Feishu research 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。

Analyze survey and feedback data with statistical distributions, cross-tabulations, significance testing, and open-ended theme extraction. Produces executive-ready survey reports with segment comparisons and action recommendations. Use when processing NPS, CSAT, employee engagement, or market research surveys.

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 structured vendor/tool evaluation matrices with weighted scoring, TCO analysis, and recommendation memos. Covers criteria definition, deal-breaker identification, and negotiation points. Use when selecting SaaS tools, evaluating technology vendors, or making build-vs-buy decisions.

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 competitive intelligence briefs following the CI cycle — Plan, Collect, Analyze, Disseminate. Profile competitors, create feature comparison matrices, analyze pricing, build positioning maps, and identify win/loss patterns. Use when preparing for product launches, sales enablement, or strategic reviews.

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 PlayCanvas rendering change is meant to leave the image unchanged, to prove it with deterministic captures and pixel comparison.

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 running or wiring OpenScientist for AI Gene Review gene-function hypotheses, especially blinded comparisons against local bioinformatics analyses.

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。

date-fns patterns for TypeScript - formatting, parsing, manipulation, comparison, timezone handling, and internationalization

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。

Audit technical, on-page, and AI-search SEO for pages, sites, and content. Use when the user asks for `/seo-audit`, `/ai-seo`, SEO review, keyword targeting, heading structure, metadata, internal linking, content gaps, AEO, GEO, LLMO, or competitor SEO comparisons.

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。

Route a vague Prisma Next prompt to the right specific skill. Use for "help me with Prisma Next", "what is Prisma Next", "explain Prisma Next", "I'm new to PN", "where do I start", "what can I do with Prisma Next", "what can I do next with Prisma", "just ran createprisma", "tour of Prisma Next", "Prisma Next overview", and comparison questions like "Prisma Next vs Prisma 7", "PN vs Drizzle", "PN vs Kysely", "PN vs TypeORM". Do NOT use when the prompt clearly matches a workflow skill — adoption / quickstart / first-touch orientation / brownfield introspection, schema / contract editing, migration authoring (db update / migration plan / migrate), migration review on deploy / concurrent migrations, queries / db.orm / db.sql / TypedSQL, runtime / db.ts / middleware wiring, build / Vite plugin / Next.js plugin, debug / structured error envelopes / PN-* error codes, or feedback / bug report / feature request — load that sibling skill directly.

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。

Answer questions against a persistent markdown wiki and optionally file the answer back into the wiki. Use this when the user asks what their wiki says about a topic, wants a comparison across notes, or wants citations grounded in the wiki.

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 one or more standalone Meta image-ad creatives via ChatGPT Image 2 (gpt-image-2) through the Novoads API. Locks the model, auto-strips platform chrome, enforces edge-safe layouts and glyph-safety inside body text. Use when the user asks for a "gpt-image-2 ad", "ChatGPT Image ad", "Image 2 ad creative", "make a static image ad with GPT", or anchors on a need for typography-heavy / dense-text / UI-mimicry ad creatives (chat threads, comparison tables, fake search results, iOS dialogs, Slack snapshots, ChatGPT-conversation ads, Apple Notes lists). Does NOT trigger on Nano Banana cues — use nano-banana-image-ad for those.

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。

UI design and implementation for real product surfaces with taste-first critique, reference routing, project-specific design briefs, variation comparison, and implementation verification. Use when Codex should redesign, build, polish, or critique frontend UI by reading project context, exploring taste, routing through `references/catalog.md`, synthesizing `PROJECT_DESIGN.md` or `design.md`, using investment gates to avoid wrong taste/reference/scope, implementing in the existing stack, and evaluating whether the result is actually better. Best for app screens, dashboards, landing pages, query tools, product surfaces, and design explorations; not for backend-only work or tiny cosmetic edits.

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。

Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance 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。

Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell 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。

Automate semantic versioning releases with CHANGELOG validation, comparison link management, GitHub Actions workflow triggering, and monitoring. Scaffold release infrastructure for new projects. Use when user runs /release command, mentions "release version", "cut a release", "create release", "publish release", "tag release", "release setup", or "scaffold release pipeline".

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。

Detect content decay by identifying pages that are losing both clicks and impressions over a 90-day comparison window in Google Search Console. Surfaces pages with true long-term decline (not seasonal variation) and assigns decay severity ratings. Use when user says "content decay", "declining pages", "losing rankings", "pages losing traffic over time", or "90 day decline".

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。

Compare Google Search Console performance between two time periods to identify trends and changes. Supports month-over-month (MoM), year-over-year (YoY), or custom date ranges. Use when user says "GSC compare", "period comparison", "month over month", "year over year", "compare performance", "MoM", or "YoY".

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。

Query and answer questions about Volcengine API specifications. Use when the user asks about API parameters, error codes, request methods, enum values, required fields, response structures, pagination, parameter dependencies, service capability lists, API availability, or API comparisons, even if they do not explicitly say "API". Typical intent includes checking what an action supports, which fields are required, what values a parameter accepts, why an API returns a specific error, how to pass nested or body parameters, how pagination works, what an API response contains, whether a batch operation exists, what services or versions expose an operation, or how two APIs differ. Use API Explorer data as the authoritative source and preserve constraints, examples, and caveats found in the spec. Answer in Chinese or English. When the user needs runnable SDK code, language-specific examples, or SDK configuration, hand off to volcengine-sdk-generator. When they need CLI operations, hand off to volcengine-cli.

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 accurate Volcengine SDK examples by locating an API through API Explorer search, fetching its swagger, and calling api/common/explorer/make-code with user-provided Params. Use when the user asks how to write Volcengine SDK code, generate SDK samples, or call a Volcengine API in Python, Go, Java, PHP, cURL, Node.js. Supports Chinese and English API names such as "角色扮演", "AssumeRole", or "STS AssumeRole". Also use for explicit SDK configuration questions about retry, timeout, AK/SK, STS, AssumeRole, temporary credentials, proxy, connection pooling, SSL, debug mode, request signing, response parsing, and error handling. If the user only needs API parameters, enum values, required fields, error codes, response schemas, pagination, or API comparisons, hand off to volcengine-api. If they need CLI-based operations, hand off to volcengine-cli.

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。