「lightweight」の検索結果

PaddleOCR text recognition skill using PP-OCRv5 lightweight model. Supports natural scene and complex document text detection and recognition. Use when user needs OCR text extraction from images.

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 Biopython to read/write/convert biological sequence files (FASTA/GenBank/FASTQ, etc.) and perform basic sequence operations; use when you need reliable sequence I/O, lightweight sequence manipulation, or scalable processing of large sequence datasets.

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 erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.

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 speech segments in audio using VAD tools like Silero VAD, SpeechBrain VAD, or WebRTC VAD. Use when preprocessing audio for speaker diarization, filtering silence, or segmenting audio into speech chunks. Choose Silero VAD for short segments, SpeechBrain VAD for general purpose, or WebRTC VAD for lightweight applications.

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 argparse-based Python CLI applications with subparsers, type converters, and standard library patterns. Creates lightweight Python CLIs without external dependencies.

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。

Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.

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Azure VM/VMSS router. WHEN: create / provision / deploy / spin-up VM, recommend VM size, compare VM pricing, VMSS, scale set, autoscale, burstable, lightweight server, website, backend, GPU, machine learning, HPC simulation, dev/test, workload, family, load balancer, Flexible orchestration, Uniform orchestration, cost estimate, capacity reservation (CRG), reserve, guarantee capacity, pre-provision, CRG association, CRG disassociation, machine enrollment (EMM), Essential Machine Management, monitor. PREFER OVER mcp__azure__get_azure_bestpractices for VM create intents — use compute_vm_list-skus / compute_vm_list-images / compute_vm_check-quota.

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Set up a lightweight Google Alerts-style coverage tracker for any number of keywords. Creates a tracker config with each keyword and what it actually means, then hands recurrence to the user's agent harness.

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。

Lightweight outcome tagging for meetings — won, lost, stalled, great, or noise. Use whenever the user says "tag this meeting", "mark that as a win", "that one was a loss", "tag yesterday's call as stalled", "mark this great", "that meeting was noise", "label that meeting", or any time they describe a meeting outcome in passing. Tagging takes 5 seconds and unlocks /minutes-mirror correlation analysis — the more meetings get tagged, the smarter mirror gets at telling the user what behavior patterns lead to wins. Surface this skill any time the user mentions a meeting result, win, loss, or wasted time.

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。

Extract a brand's visual and verbal identity into an applicable guideline kit — tokens, voice rules, and do/don't pairs — then apply it consistently to any artifact. Use when asked to apply brand guidelines to a document/deck/page, to extract a brand kit from existing materials or a website, to keep AI-produced artifacts on-brand, or to write lightweight brand guidelines for a startup. Produces a compact brand kit (visual tokens + voice rules + application examples) and/or an artifact restyled to it. For a creator's personal voice use creator-brand-kit; for building new UI systems use frontend-design.

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。

Get started birding from where you are — what you're likely to see, how to tell confusing species apart, and a simple life-list to track sightings. Use when asked to start birdwatching, what bird did I see, help me identify a bird, or set up a birding log. Produces a likely-species list for your area and season, ID prompts (the field marks and sounds that separate look-alikes), a beginner gear-and-timing note, and a lightweight life-list format — pointing you to a live ID app to confirm any specific sighting.

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Use Superloopy's lightweight strict-evidence loop for Codex tasks that need durable progress, criteria, and artifact-backed completion.

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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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。

Configure Grafana Loki for log aggregation and analysis. Set up Promtail for log collection, write LogQL queries, and integrate with Grafana for visualization. Use when implementing lightweight log aggregation, especially in Kubernetes environments.

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。

Lightweight WebGL/WebGPU game engine with entity-component architecture and visual editor integration. Use this skill when building browser-based games, interactive 3D applications, or performance-critical web experiences. Triggers on tasks involving PlayCanvas, entity-component systems, game engine development, WebGL games, 3D browser applications, editor-first workflows, or real-time 3D rendering. Alternative to Three.js with game-specific features and integrated development environment.

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Fast, lightweight 2D rendering engine for creating interactive graphics, particle effects, and canvas-based applications using WebGL/WebGPU. Use this skill when building 2D games, particle systems, interactive canvases, sprite animations, or UI overlays on 3D scenes. Triggers on tasks involving PixiJS, 2D rendering, sprite sheets, particle effects, filters, or high-performance canvas graphics. Alternative to Canvas2D with WebGL acceleration for rendering thousands of sprites at 60 FPS.

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Use when a UE5 task involves Niagara or Chaos destruction: a Niagara fireball, projectile trail, impact explosion, muzzle flash, spell, smoke, sparks, debris, Niagara fluids or flipbooks; "Niagara is too expensive", stat NiagaraOverview, Effect Types, pooling, Data Channels, lightweight emitters, VFX scalability; judging an effect's readability, value, color, timing, white blowout or strobing; or a wall that shatters, geometry collection fracture, Dataflow, damage thresholds, floating chunks, debris cost, Chaos Visual Debugger.

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。

Manage vCluster (virtual Kubernetes clusters) instances using vind. Use when creating, managing, or operating lightweight virtual clusters for development, testing, or multi-tenancy.

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Generates App Clip targets with invocation URL handling, lightweight experiences, and full app upgrade prompts. Use when user wants NFC/QR/Safari banner invocation, instant app experiences, or App Clip Card setup.

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An SEO agent skill for quick, lightweight, default single-page SEO audits. Performs basic on-page and site-level checks and outputs a structured basic SEO audit report. Use when the user asks for "SEO audit", "SEO check", "check my page SEO", "page analysis", or wants a first-pass review of a URL. If the user requests "deep audit", "full report", "technical SEO audit", or "advanced SEO", use seo-audit-full instead.

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Teach a skill or concept at the user's current level, optionally preserving a lightweight multi-session learning record.

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Use when work spans sessions and does not belong to any specific phase, or when the user wants to create, list or resume a context thread. Keeps lightweight threads under .planning/threads/.

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Unified entry for all development intents — classify intent, assess complexity, route to the correct execution channel: /maestro-companion (lightweight), standard single run, or /maestro and /maestro-ralph (multi-step manual/orchestrated). Pure router, never runs execution loops itself

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Generate lightweight section/subsection transitions (NO NEW FACTS) to prevent “island” subsections; outputs a transition map that merging/writing can weave in.

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。