「recommendations」の検索結果

E-commerce warehouse and inventory optimization advisor. Analyzes inventory health, calculates safety stock and reorder points, performs ABC analysis, evaluates fulfillment costs, and provides actionable recommendations for improving efficiency. Supports all major fulfillment models: Self-fulfillment, Amazon FBA/FBM, Walmart WFS, 3PL, Shopify Fulfillment, TikTok Shop, Dropshipping, and Hybrid setups. No API key required. Use when: (1) reducing stockouts or overstock, (2) calculating safety stock levels, (3) optimizing warehouse costs, (4) improving Amazon IPI score, (5) analyzing inventory KPIs.

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App stack advisor — essential apps by business stage, cost optimization, performance impact analysis

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Design personalization strategies for e-commerce stores. Product recommendations, dynamic content, customer segmentation, and personalized email/SMS based on browsing and purchase behavior.

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Use when managing Apple Ads with asc, including OAuth profiles, ad-account discovery, Platform API v1 campaigns and targeting, reports, assets, recommendations, guarded mutations, raw requests, and Campaign Management API v5 migration.

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Analyze workflow patterns using the Agent Monitor's workflow intelligence API — orchestration DAGs, tool flow transitions, subagent effectiveness, model delegation patterns, error propagation by depth, concurrency lanes, compaction impact, and agent co-occurrence. Produces prioritized optimization recommendations with quantified impact.

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Compare UX patterns across multiple reference apps using pattern libraries produced by ux-extract. Reads 2+ pattern-library.md files, walks them category by category, identifies where apps converge (strong signal), where they diverge (genuine design choice), what's unique to one app, and what's absent across the set. Produces an opinionated comparison document with recommendations for a new build. No browser needed — pure markdown analysis. Trigger with 'compare UX patterns', 'how do top apps handle X', 'ux comparison', 'pattern comparison across reference apps'.

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When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-strategy.

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When the user wants to choose or execute third-party platform SEO (high-authority sites for rankings or backlinks). Also use when the user mentions "parasite SEO," "parasitic SEO," "barnacle SEO," "hosted content," "third-party publishing," "Medium SEO," "Reddit SEO," "GitHub parasite SEO," "LinkedIn Pulse SEO," "high-authority platforms," "distributed authority," "borrow domain authority," or "rank without own website." For GitHub-specific playbooks, use github. For Medium.com posts, use medium-posts. For Grokipedia, use grokipedia-recommendations. For AI answer-engine visibility (not platform selection), use generative-engine-optimization.

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Reviews the design of an existing Power Pages site - a live URL or a local project folder - and recommends what to change, without modifying anything. Captures up to eight key pages at desktop and mobile, scores them against the plugin's design rubric (first impression, hierarchy, narrative, copy, trust, brand, responsive and accessible design, motion, detail), and returns prioritized, concrete recommendations. Use when the user wants a design review, critique, or audit of a site, asks how to make an existing site look better, more polished, or more impressive, or asks why it looks generic or dated.

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Capture a 2-3 line pitch from the user, draft a product overview (PRD.md) and a technical design (ARCHITECTURE.md) for a third-party `.ppmplugin` native control, then walk through every operation's iOS + Android implementation strategy with opinionated recommendations (library choice, hosting, key APIs, edge cases) and capture the agreed spec in ARCHITECTURE.md §3.<n>. The depth of ARCHITECTURE.md is what lets the scaffold skill generate complete working code instead of TODO placeholders. Iterates with the user until they approve both docs. Optionally seeds from a design doc / FRD URL. PRD.md + ARCHITECTURE.md are the source of truth for every downstream skill (generate, build, assemble). Run this BEFORE any code is generated.

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Use this when setting up Nix for a development project (devShell + package build) and you care about `nix develop` being fast. Covers the zero-inputs flake.nix + npins + default.nix/shell.nix layout, sub-flakes for non-user-facing Nix, and language-specific recommendations.

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Designing quizzes, personality assessments, and recommendation tools that segment users into actionable categories rather than generating clicks for clicks' sake. Question architecture, scoring algorithms, result categorization, recommendation mapping, lead capture integration. Honest about clickbait-quiz (engagement only), vanity-result (entertaining, not useful), and actionable-segmentation (genuine categorization that drives next-step recommendations) patterns. Triggers on quiz, assessment, personality test, recommendation tool, scorecard, diagnostic, fit evaluator, what-type-of-X-are-you, persona quiz. Also triggers when an audience needs a categorization-driven lead magnet, when a vanity quiz is producing engagement but no qualified leads, or when an assessment is being scoped for the first time.

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A comprehensive stock deep analysis tool that combines real-time quotes, fundamental metrics, technical indicators, and growth analysis into a single professional report. Supports A-share, US stocks, HK stocks. Generates detailed investment recommendations with risk assessment and actionable trading strategies.

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WeChat Official Account topic planning and content calendar management. Based on WeChat article search and trending analysis, generates differentiated topic recommendations and outputs structured content calendars. Activated when users mention "topic", "planning", "content calendar", "trending", or "what to write next week".

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Search cloud drives for downloadable film and TV resources (movies, TV series, anime). Use this skill when the user wants to download a specific movie or TV show. Do NOT use for general movie information, schedules, reviews, or recommendations.

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Search cloud drives for downloadable music resources (songs, albums, lossless audio). Use this skill when the user wants to download a specific song or album. Do NOT use for general music information, lyrics, or recommendations.

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。

Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP.

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。

Synthesize performance findings into evidence-backed recommendations and 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。

Comprehensive weekly review of goals, progress, and recommendations

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Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations.

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Comprehensive technology stack evaluation and comparison tool with TCO analysis, security assessment, and intelligent recommendations for engineering teams

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GDPR Transfer Impact Assessment for Chapter V transfers under the EDPB Recommendations 01/2020 six-step methodology, the CNIL TIA Guide (January 2025), and EDPB essential guarantees. Handles transfer qualification, Art. 45 adequacy fast-tracks, Art. 46 full assessments with country profiles for 12 jurisdictions, and balanced Art. 49 derogation analysis. Outputs a Markdown report, a .docx formal TIA, and a JSON delta for RoPA interchange.

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Builds compliance checklists for business scenarios involving GDPR, PIPL, or advertising/data laws. Outputs a structured checklist with check items, legal basis, risk levels, and actionable recommendations. Triggered by requests like "run a compliance check," "GDPR/PIPL compliance," "pre-launch review," "privacy impact assessment (PIA/DPIA)," or asking if a feature is compliant.

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Panel health assessment, firm exit management, coverage gap analysis, and panel refresh brief for in-house legal ops teams. Produce an annual Panel Health Report synthesising scorecard data, billing compliance, and step-out patterns into per-firm recommendations — Retain, Watch, Improvement Plan, or Exit Review. Produce a firm exit pack: formal exit notice with active matter transition plan, plus an internal exit record. Identify practice area, geographic, or tier gaps and produce a structured Coverage Gap Report with remediation options. Produce a Panel Refresh Brief as a scoped input to the RFP process. Trigger on: 'review our panel', 'annual panel review', 'is our panel working', 'which firms should we exit', 'write the exit notice', 'coverage gap', 'we don't have anyone for restructuring', 'refresh the panel', 'panel rationalisation', 'too many firms', 'step-out problem', 'do we have the right firms'.

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。