「recommendation」の検索結果

Upscale and restore video in ComfyUI. Both the quick local path (per-frame ESRGAN like 4x_foolhardy_Remacri via ImageUpscaleWithModel + 4x→2x supersample, with its temporal-flicker tradeoff) and temporal-aware super-resolution (SeedVR2, the newer FlashVSR) with the downscale-first restore pipeline; RIFE/FILM frame interpolation via the BUILT-IN ComfyUI 0.26 FrameInterpolate (rife_v4.26 in models/frame_interpolation/) or the ComfyUI-Frame-Interpolation pack; 2x/4x scaling, VRAM tiers, VHS encode. Captures the classic downscale→SeedVR2→RIFE recipe and the current 2026 recommendation.

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Comprehensive performance analysis, bottleneck detection, and optimization recommendations for Claude Flow swarms

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Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".

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Localization and internationalization strategy for iOS/macOS apps. Covers market prioritization, language tier recommendations, minimum viable localization levels, translation workflows, cultural adaptation, localized ASO, and testing. Use when planning localization, expanding to new markets, deciding which languages to support, or planning translation workflow.

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Expert guidance for designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI, HPC, SaaS, AVD/AVS workloads, choosing regions/AZs, or configuring core Azure services, and other Azure Well Architected related development tasks.

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One token recommendation and one prediction market pick - scored, quantified, with a skip branch when signals are weak

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Use RightStack as a read-only Web3 stack advisor for architecture recommendations, workflow inspection, tool comparisons, and package-migration checks. Use for planning; do not treat corpus output as implementation proof.

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Autonomous cloud-cost analyst across Neon, Vercel, Railway and GitHub Actions - pulls per-object usage, attributes it to the biggest drivers, root-causes each, and emits recommendations ranked by real signal (idle %, over-allowance, failure rate) with dollar figures only where the billing API returns real ones (and, armed, applies safe cost levers).

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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.

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Write Reddit posts and comments that recommend affiliate products without getting banned or flagged as spam. Subreddit-native content that adds value first. Use this skill when the user asks about Reddit posts for affiliate marketing, writing Reddit comments that mention products, how to promote affiliate links on Reddit, or says "write a Reddit post for X", "how to mention affiliate on Reddit", "Reddit comment promoting product", "Reddit-friendly affiliate content", "post for r/[subreddit] about X", "share affiliate link on Reddit without getting banned", "genuine Reddit recommendation", "organic Reddit affiliate post", "Reddit thread idea for product".

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Generate affiliate performance reports with KPIs and recommendations. Triggers on: "show my affiliate report", "how are my programs doing", "performance review", "earnings report", "monthly affiliate report", "weekly report", "analyze my affiliate earnings", "which program is best", "EPC report", "conversion rate analysis", "revenue breakdown", "campaign performance".

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Write how-to guides and tutorials that naturally integrate affiliate product recommendations. Triggers on: "write a how-to guide", "tutorial for [task]", "step by step guide to [goal]", "how to [verb] with [product]", "write a tutorial blog post", "guide on how to [task]", "beginner guide to [topic]", "walkthrough for [product]", "write an educational article", "how do I [task] blog post", "write a tutorial that promotes [product]".

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Aircraft seat maps, cabin dimensions, and seat recommendations via SeatMaps.com and AeroLOPA. Search by flight number or airline+aircraft via agent-browser.

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Unified flight comparison across cash (Duffel, Ignav), award (seats.aero), and portal (Chase, Amex) sources in parallel. Outputs one table with transfer partner optimization and recommendations.

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Detect and investigate bulk registration abuse on SaaS platforms. Given access to a user database (or exported CSV/JSON), run a multi-dimensional anomaly analysis — email domain clustering, registration tempo, UA fingerprinting, session structure, usage patterns, and credit consumption — to produce a scored abuse report with actionable recommendations. Use when investigating suspicious sign-up spikes, credit fraud, free-tier abuse, or account farming.

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Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts

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Weekly, file work only, no browser at all. Reads everything the week produced, picks exactly three moves, and argues both sides of each one before the member reads a word of it, with every clause carrying a number or an observation from a file it names. It argues against its own top recommendation last, records a predicted effect and the metric that would show it so the monthly review can score it, and files each move onto the register. It sends only where you released the channel, spends only where you released it, never deploys, and never touches a credential.

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Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.

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Use when facing architectural decisions, complex trade-offs, strategic technology choices, or scaling and migration questions that deserve more than a quick answer. Lays out options with trade-offs, then a recommendation.

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Detailed completeness audit of all project documents. Use when someone asks "are we ready for production", "audit our docs", "readiness check", "nfr tracker", or "what's missing". Do NOT use for a quick command listing — use /pmstudio instead. Scans every document for section-level completeness, reports staleness, and gives prioritized gap-closure recommendations.

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Audit token consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations. Use when the context window is filling up too fast or before adding new components.

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Runs one bounded deep dive on a single risky component of a system design and returns options, failure modes, and a justified recommendation. Use during a design session when a component needs expert depth beyond the main thread's budget.

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Analyze skill effectiveness across sessions. Computes per-skill metrics (action rate, friction, outcomes), identifies degrading skills, and generates improvement recommendations. Requires session-scan data in metrics.jsonl.

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Use the `hf` CLI for Hub authentication, downloads, uploads, repositories, cache, jobs, buckets, webhooks, and endpoint administration. Use for CLI operations, not model recommendations, paper analysis, training design, or building a Space.

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。