「compare」の検索結果

When you want to model personal financial scenarios — house purchase + rental income (ADU, bedroom rentals, house-hacking), renovation budgets, monthly cash flow, big-purchase decisions, savings/investment what-ifs for a household. Ships with a house scenario template; other scenarios (refi, car, education, retirement, side income) slot in as templates. Outputs markdown scenario comparison tables. Saves scenarios to ~/Documents/personal-cfo/ with an index in ~/.config/makerskills/personal-cfo/archive/. Composes with decide, deep-research (rental comps, rates), business-brainstorm, second-brain. Triggers on "/personal-cfo," "model this scenario," "house math," "rental forecast," "monthly cash flow," "what if I rent out the ADU," "compare these housing scenarios," "should we buy this house," "house-hack math," "renovation budget."

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Build students' capacity to compare accounts across multiple historical sources — identifying agreements, contradictions, and gaps. Use when students treat individual documents as complete answers rather than partial perspectives.

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Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

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Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.

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 a project's infrastructure and SaaS stack, then produce a cost report showing what the user currently pays and what they would save by consolidating onto Temps (self-hosted or Temps Cloud). Detects hosting platforms (Vercel, Netlify, Railway, Render, Heroku, Fly.io), analytics (PostHog, Plausible, Mixpanel, Amplitude, Fathom), error tracking (Sentry, Bugsnag, Rollbar, Honeybadger), session replay (LogRocket, FullStory, Hotjar, Highlight), uptime monitoring (Pingdom, UptimeRobot, Better Stack, Checkly), managed databases (Supabase, Neon, PlanetScale, MongoDB Atlas, Upstash, RDS), and transactional email (SendGrid, Postmark, Resend, Mailgun) from dependencies, config files, and env var names. Use when the user wants to: (1) Know how much they would save by switching to Temps, (2) Audit their SaaS/infrastructure spend, (3) Compare their current stack's cost against self-hosting, (4) Decide whether Temps is worth it, (5) Build a business case for consolidating tools. Triggers: "how much would I save", "temps savings", "cost comparison", "audit my stack", "am I overpaying", "saas spend", "calculate savings", "is temps cheaper".

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Competitive landscape analysis for A-share companies and Chinese industries. Maps competitors, compares positioning, and assesses relative strengths. Adapted from the original competitive-analysis skill for Chinese market context. Triggers on "A股竞争格局", "行业竞争分析", "competitive landscape China", "competitive analysis A-share", or "[company] competitors China".

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SEO drift monitoring: capture baselines of SEO-critical elements, detect changes, and track regressions over time. Git for SEO — baseline, diff, and track changes to your on-page SEO. Use when user says "SEO drift", "baseline", "track changes", "did anything break", "SEO regression", "compare SEO", "before and after", "monitor SEO changes", or "deployment check".

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Generate SEO-optimized competitor comparison and alternatives pages. Covers "X vs Y" layouts, "alternatives to X" pages, feature matrices, schema markup, and conversion optimization. Use when user says "comparison page", "vs page", "alternatives page", "competitor comparison", "X vs Y", "versus", "compare competitors", or "alternative to".

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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".

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MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry,

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A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum

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Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.

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Compare reply rates, bounce rates, and positive reply rates broken down by inbox type (SMTP / Gmail / Outlook) for a Smartlead account. Use when you want to know "which inbox type delivers best" or when debugging unexplained deliverability differences across a mixed fleet. Operates via Smartlead API only (no database required).

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Framework for running single-variable cold email experiments. Defines experiment types (list-only, copy-only, combined), confidence weighting, minimum sample sizes, and success criteria. Use when the user wants to improve a campaign, test a new list vs old one, or compare copy variants. Prevents the

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Evaluate and plan Amazon marketplace expansion across countries and regions. Use when a seller asks which Amazon marketplace to enter, how to compare international demand and economics, what tax, product-compliance, logistics, localization, account, or launch workstreams to investigate, or how to build a gated global-selling roadmap. Do not use as legal, tax, customs, or certification advice.

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Full-spectrum Amazon competitor analysis. Compare listings, pricing, reviews, advertising strategy, and market positioning against direct competitors. Identify weaknesses to exploit and strengths to counter. Use when the user asks about competitor analysis, competitive research, ASIN comparison, pricing analysis, or how to beat competitors on Amazon.

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 two schema states and surface the risky changes. Use before applying migrations or after a model change.

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April Dunford's 10-step positioning methodology for B2B tech products. Use when customers don't understand the product, sales cycles are long because reps must "explain it," prospects compare you to wrong competitors, or you face price pressure despite a good product. Covers competitive alternatives, unique attributes, value themes, target market characteristics, market category (Head to Head / Big Fish Small Pond / New Game), and trend layering.

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Analyze how competitors present their brand — identity, messaging, positioning, voice, and visual style — to find gaps and opportunities. Use when the user says "competitor brand analysis", "analyze competitor brands", "how do competitors position themselves", "competitor messaging", "competitor identity", "what are competitors saying", "benchmark competitors", "competitive brand landscape", "how do we compare to competitors", "competitive audit", or wants to understand the brand landscape before defining their own position.

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Use when the user asks which model to use, wants to compare models, or needs guidance on selecting between Kling, Wan (incl. Wan 3.0), Seedance (incl. 2.5), Veo 3, Minimax Hailuo / MiniMax H3, FLUX 3 Video, Gemini Omni Flash, Soul, Nano Banana, GPT Image 2.5, or other Higgsfield engines — including which lane edits existing footage (video_edit, Kling 3.0 Omni Edit, FLUX 3 Video Edit, Genjutsu, Ad Multiplier) and which models make one clip longer than 15s.

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Fetch website traffic estimates (monthly visits, traffic sources, top countries, keywords, engagement, ranks) for any domain from SimilarWeb. Use when the user asks about a domain's traffic, monthly visits, traffic sources, audience countries, or wants to compare/benchmark sites against competitors.

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 YouTube channel and video performance using the YouTube Data API. Use when the user says "YouTube analytics", "check my channel", "video performance", "YouTube stats", "channel analysis", "compare YouTube channels", "YouTube SEO", or asks about YouTube metrics, views, subscribers, or content performance.

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。

Fetch Google review ratings and review counts for businesses via DataForSEO API. Use when the user asks to check Google reviews, get review counts, compare business ratings, audit Google Maps presence, or analyze competitor reviews.

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Audit EVERY Google Search Console property at once — rank all sites by clicks and impressions with period-over-period deltas, then diff keywords per site to surface what is newly ranking, rising, dropping, lost, or ranking well without earning clicks. Built for agencies and multi-site owners. Use when asked to compare all sites, rank properties by traffic, find new keywords across a portfolio, or spot which site is down. Trigger phrases: "audit GSC", "all my sites", "rank my properties", "portfolio search performance", "which sites are down", "what new keywords are we ranking for", "client site performance", "GSC report across accounts". For ONE site's queries, pages, or index coverage, use the search-console skill 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。