「value」の検索結果

When you want to clean and reformat content (usually from your terminal) for pasting into Slack, Notion, Twitter/X, LinkedIn, email, GitHub, or plain text. Strips ANSI codes, box-drawing chars, terminal prompt artifacts, and applies destination-specific formatting. Default input is the system clipboard; default output is both the clipboard and a chat preview. Triggers on "/paste", "/paste [destination]", "clean this for X," "format for slack/notion/twitter/linkedin/email/github," "render as markdown," "paste-ready," "strip formatting," "make this copy-pastable." Default destination is plain. Also scans for secrets (API keys, tokens, .env values) and warns before copying anything sensitive.

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Surface beliefs, assumptions, stories, and values shaping a system. Use when deeper mental models need examining with care and evidence.

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Identify and navigate genuine dilemmas in curriculum, school, or community contexts — tensions between competing goods that cannot be solved, only navigated. Produces a structured dilemma map with both poles named and both/and possibilities.

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Design learning tasks built on Ubuntu philosophy emphasising collective knowledge-building and mutual responsibility. Use when fostering collaboration that values community over individual competition.

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Expert patterns for AnimationPlayer including track types (Value, Method, Audio, Bezier), root motion extraction, animation callbacks, procedural animation generation, call mode optimization, and RESET tracks. Use for timeline-based animations, cutscenes, or UI transitions. Trigger keywords: AnimationPlayer, Animation, track_insert_key, root_motion, animation_finished, RESET_track, call_mode, animation_set_next, queue, blend_times.

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。

Expert patterns for AnimationPlayer including track types (Value, Method, Audio, Bezier), root motion extraction, animation callbacks, procedural animation generation, call mode optimization, and RESET tracks. Use for timeline-based animations, cutscenes, or UI transitions. Trigger keywords: AnimationPlayer, Animation, track_insert_key, root_motion, animation_finished, RESET_track, call_mode, animation_set_next, queue, blend_times.

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。

Integrate the Temps Node.js SDKs for server-side platform access, KV storage, and Blob storage. Use when the user wants to: (1) Call the Temps platform API from Node.js (deployments, projects, analytics, session replay, etc.), (2) Use Temps KV (key-value) storage, (3) Use Temps Blob storage for files, (4) Server-side integration with a Temps project, (5) Backend access to Temps resources. Triggers: "temps node sdk", "temps kv", "temps blob", "backend integration", "node.js temps", "@temps-sdk/node-sdk".

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Comparable company analysis for A-share stocks. Uses AkShare MCP to build peer groups, pull financial data, compute valuation multiples (PE, PB, PS), and assess relative value within a Chinese industry sector.

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Generic PowerPoint authoring skill for A-share investment analysis and pitch decks. Creates professional 路演PPT / 投资分析PPT for any A-share company using live data from iFind MCP (Tier-1) and AkShare MCP (Tier-2 fallback) tools. All company-specific values are parameterized — never hardcoded. Triggers on "A股PPT制作", "投资PPT", "制作PPT", "路演PPT", "pitch deck", "PowerPoint [company/ticker]", or any request to create slides for a Chinese stock. When invoked, use the `generate_a_share_ppt` script with --company and --ticker args.

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When the user wants to plan, evaluate, or build a free tool for marketing purposes — lead generation, SEO value, or brand awareness. Also use when the user mentions "engineering as marketing," "free tool," "marketing tool," "calculator," "generator," "interactive tool," "lead gen tool," "build a tool for leads," or "free resource." This skill bridges engineering and marketing — useful for founders and technical marketers.

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When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flow," "activation rate," "user activation," "first-run experience," "empty states," "onboarding checklist," "aha moment," or "new user experience." For signup/registration optimization, see signup-flow-cro. For ongoing email sequences, see email-sequence.

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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

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When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' or 'monetization.' This skill covers pricing research, tier structure, and packaging strategy.

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SwiftUI layout beyond stacks — the Layout protocol (when custom layout beats GeometryReader), Grid vs lazy grids, custom containers with sections and container values, and lazy-stack/ScrollView performance rules (what breaks laziness, prefetch discipline, scroll APIs). Use when building custom layouts or containers, fixing lazy-stack jank or memory growth, or wiring programmatic/snapping scrolling.

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Generates value-moment-first onboarding flows for iOS/macOS apps — the default architecture races a new user to the first felt experience of the app's promised outcome, branching on whether they can experience it right now or need to plan for later. The classic paged welcome-carousel tour is an explicit fallback for genuinely explain-first apps. Use when user wants to add onboarding, welcome screens, first-launch experience, or improve activation/trial conversion.

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Generates guided first-action flows that help users achieve a meaningful result within 60 seconds to boost retention. Use when user wants quick win onboarding, time-to-value optimization, or first success moments.

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Plan compelling App Store screenshot sequences that showcase your app's value. Use when preparing App Store assets, improving screenshot conversion, or drafting screenshot captions for a launch or major update.

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SF Symbols end-to-end — choosing and configuring the 7,000+ system symbols (rendering modes, variable color/draw, gradients), authoring custom symbols that interpolate across weights, and the animation preset vocabulary. Use when picking iconography, building custom symbols, animating symbol state changes, or rendering values as symbols.

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SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations.

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An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

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The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. epistack needs three different inputs: 1) a genomic score objects, such as ChIP-seq coverage or DNA methylation values, provided as a `GRanges` (easily obtained from `bigwig` or `bam` files). 2) a list of feature of interest, such as peaks or transcription start sites, provi

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BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value ca

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Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA o

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Check whether an x402 payment endpoint is reliably delivering value before spending USDC on it. Returns paid delivery rate, active incidents, latency, and a clear proceed/warn/block recommendation.

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。