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Comprehensive product research and opportunity analysis for Amazon sellers. Analyzes demand, competition, profit potential, market entry barriers, and validates product ideas. Covers product sourcing, pricing strategy, and go-to-market planning. Use when the user asks about researching a product to sell, validating product ideas, product opportunity analysis, market research for Amazon, competition analysis, profit potential, should I sell this product, product viability, or any general product research questions.

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Create HTML dashboards with KPI metric cards, bar/pie/line charts, progress indicators, and data visualizations. Use when users request dashboards, metrics displays, KPI visualizations, data charts, or monitoring interfaces.

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。

Membangun motion graphic sinematik di browser (promo video, opener, intro, bumper, kinetic typography, explainer/video penjelasan bergambar 16:9 atau 9:16) memakai HTML + CSS + GSAP (+ Three.js bila perlu), dengan hasil yang bergerak seperti video sungguhan — bukan slide presentasi. Gunakan skill ini setiap kali user meminta "video promo", "opener", "intro animasi", "motion graphic", "bumper", "kinetic typography", "animasi teks sinematik", "explainer", "video penjelasan" (jurnalisme visual, kartun edukasi, kolase), atau menunjukkan referensi video promosi/explainer dan ingin dibuatkan versi web-nya — bahkan bila mereka tidak menyebut kata "motion graphic" secara eksplisit. Juga gunakan bila user mengeluh hasil animasi web "seperti PPT/presentasi" dan ingin lebih sinematik, atau ingin merender animasi web menjadi file video MP4, atau ingin explainer kartun/animasi dibangun LANGSUNG di After Effects lewat bridge/MCP Higgsfield.

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Finds the price buyers will actually pay. Reads the consumer panel's four price answers per buyer into a Van Westendorp price-sensitivity range (too cheap, a bargain, getting expensive, too expensive), sets it against what competitors really charge and against the CFO's margin, and recommends a price, a price ladder (good / better / best) and an opening offer to test. Use when the user says "how much should I charge", "price this", "is $X too expensive", "pricing strategy", "what price", or after the consumer 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。

Implement route configuration, go_router/deep linking, and named routes in Flutter. Use for route declarations, route guards, and URL-to-screen mapping; defer transition-only animation, state-transfer/BLoC questions, auto_route-specific setup, and generic app-bar controls.

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 the user asks which /phx-* command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).

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 the user asks which phx-* command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).

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 the user asks which /phx: command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).

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。

Creates a Check for Understanding (CFU) for a math topic or standard: 1–3 targeted formative items whose distractors come from documented student misconceptions, plus a teacher guide routing each likely response to a specific next instructional step. Load BEFORE asking any clarifying question about grade, standard, or topic. This skill is for math; for other subjects, help as you normally would and don't mention the skill to the user. Triggers on asks to create, write, generate, or design a check for understanding, formative check, quick check, or exit ticket for a math concept or standard, and on implicit signals like "I need to see who got it before I move on." Not a quiz, test, or practice set — a CFU is read for what it reveals, not scored, so a numbered quiz or a bare answer key is a different job. Does not create a lesson (k12-lesson-plan-creation, which brings its own exit ticket), tier one (k12-lesson-differentiation), or prepare a teacher to teach one (k12-lesson-prep).

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。

Build modern Shiny dashboards and applications using bslib (Bootstrap 5). Use when creating new Shiny apps, modernizing legacy apps (fluidPage, fluidRow/column, tabsetPanel, wellPanel, shinythemes), or working with bslib page layouts, grid systems, cards, value boxes, navigation, sidebars, filling layouts, theming, accordions, tooltips, popovers, toasts, or bslib inputs. Assumes familiarity with basic Shiny.

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。

Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training tasks.

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。

Run Hugging Face models in JavaScript or TypeScript with Transformers.js, WebGPU, or WASM across browser, Node.js, Bun, and Deno. Use for client-side or JS-runtime inference, not Python training.

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。

Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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。

Find similar vulnerabilities and bugs across codebases using pattern-based analysis. Use when hunting bug variants, building CodeQL/Semgrep queries, analyzing security vulnerabilities, or performing systematic code audits after finding an initial issue.

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Turn one confirmed security finding into a disclosure-ready GitHub advisory with root cause, proof of concept, impact, and source evidence. Use for reporting an established vulnerability, not discovering or validating one.

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。

Plan and coordinate a model deployment to Amazon SageMaker, including serving stack and real-time versus async inference. Use as the entry point for SageMaker hosting requests, before image, IAM, and endpoint implementation skills.

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。

Rewrite AI-generated copy in Spanish and English so it sounds direct, concrete and human while preserving meaning, facts and numbers exactly. Use when drafts feel robotic, verbose or translated and need short active sentences without em-dashes.

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 this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.

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。

Systematically verifies suspected security bugs to eliminate false positives. Produces TRUE POSITIVE or FALSE POSITIVE verdicts with documented evidence for each bug.

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。

Create an isolated Python environment with a compatible Python version and boto3 for SageMaker or AWS automation. Use before executing AWS Python code; do not use for general Python environment setup.

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。

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

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。

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

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 to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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。

Build and publish a Gradio Hugging Face Space for a user-provided image or video LoRA, including pipeline selection and model-card settings. Use for LoRA demos; use huggingface-spaces for general Space work.

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。