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概要と使いどころ
End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.
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。
Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.
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。
Turn uploaded photos into full-canvas 3:4 handcrafted travel diorama posters. Use for papercraft models, tactile miniature travel scenes, or when this skill is invoked.
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。
End-to-end automated model quantization and accuracy tuning workflow. Use when user asks for automated model quantization and accuracy tuning, e.g. "自动量化", "量化调优", "一键量化", "精度调优", etc. 当用户给出多卡（≥2 卡）卡号时，量化阶段通过 EP 并行适配（msmodelslim-ep-parallel-adaptation）保证多卡 EP 并行，调优主流程仍在本 Skill。
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。
执行模型量化。通过 msmodelslim quant 依据 Practice YAML 对模型进行量化。
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 msmodelslim evaluation YAML configuration (service_oriented + aisbench + vllm-ascend). Use when user asks for evaluation config generation.
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 viral referral programs with incentive structures, sharing mechanics, and tracking. Covers one-sided, two-sided, tiered, and milestone models with viral coefficient calculations. Use when someone wants word-of-mouth growth, viral loops, invite programs, or says 'referral', 'viral loop', 'word of mouth', 'invite program', 'refer a friend', 'growth loop', 'referral incentive', 'affiliate program', 'ambassador program', 'get users to invite friends', or 'organic growth'. Even casual mentions like 'how do I get my users to spread the word' or 'can users invite others' should trigger this. Includes anti-fraud measures and copy templates for both referrer and referee. Use this whenever user-driven acquisition or viral mechanics are discussed.
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 wants to train a reusable face-faithful Soul Character identity on Higgsfield. One-time training returns a reference_id passed to Soul models in higgsfield-generate via `--soul-id`. Use this skill whenever: "train soul character", "create my Soul", "train my face", "make my digital twin", "build me an avatar", "create a character of me", "set up identity for video", "I want my face in generated images", "face-faithful character", "reusable character", "brand character training", "build a character from photos", "soul training", "soul-id create". After training, logs the soul_reference_id to `brand/assets.md`. NOT for: one-shot face swaps (use higgsfield-generate with --image), fictional/non-photo avatars (use higgsfield-generate with a prompt). Requires Higgsfield CLI + Basic plan or higher.
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 wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "create avatar for ad", "Seedance", "Kling", "Veo", "Soul gen", "Nano Banana", "Marketing Studio". Chain with higgsfield-soul-id when user wants a reusable face identity. Owns AI-generated "marketing video" / "brand video" / ad creative. NOT for: training a Soul Character (use higgsfield-soul-id), product photoshoots with mode-specific enhancer (use higgsfield-product-photoshoot), text/TTS, screen-recorded product demos (use marketing-demo), slides-to-MP4 assembly (use video-content), Remotion code (use remotion-best-practices). Requires Higgsfield CLI installed and authed.
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。
Verify a PCBA BOM and proposed components before circuit or placement freeze, including exact manufacturer part numbers, pin semantics, packages, footprints, CAD and 3D models, lifecycle, live stock, MOQ, assembly class, price, and approved substitutes. Use for sourcing locks, unavailable or expensive parts, JLCPCB/LCSC matching, BOM risk, or cost-aware substitution 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。
Provides guidelines for developing projected Android XR apps for display glasses using the Jetpack Compose Glimmer UI toolkit. This skill covers foundational Glimmer design principles, workflows for implementing Jetpack Compose Glimmer, and interaction models for the glasses form factor. Use this skill to build an Android XR Augmented Experience app with Jetpack Compose Glimmer that adheres to the Glimmer design system for optimized glasses styling.
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 ManagedCode.Orleans.Graph into an Orleans-based .NET application for graph-oriented relationships, edge management, and traversal logic on top of Orleans grains. Use when the application models graph structures in a distributed Orleans system.
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。
Deploy AI models to mobile applications. TRIGGERS - Use when user needs help with ai-mobile-deployment related 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。
Build marketplaces for AI models and prompts. TRIGGERS - Use when user needs help with ai-marketplace-builder related 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。
Deploy AI models to edge devices. TRIGGERS - Use when user needs help with ai-edge-deployment related 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。
Design AI benchmarks for comparing models and tracking improvement. TRIGGERS - Use when user needs help with ai-benchmark-creator related 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。
Deploy AI models to browser environments. TRIGGERS - Use when user needs help with ai-browser-deployment related 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。
Design agency pricing models. TRIGGERS - Use when user needs help with agency-pricing-model related 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。
Design AI A/B testing systems for prompts and models. TRIGGERS - Use when user needs help with ai-ab-test-system related 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。
Design academic advising models. TRIGGERS - Use when user needs help with academic-advising-model related 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。
Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.
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 wants a complete SPSS or SPSS-MCP empirical research workflow: organize source data, prepare datasets, design variables and models, run SPSS analyses, export Chinese result paragraphs and tables, write a Chinese LaTeX paper, and compile the final PDF.
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 about ISAC participation, TAXII feeds, MISP communities, FIRST, STIX-based sharing, or how to publish intelligence externally. Covers sharing models, standards, communities, and TLP-governed dissemination.
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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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。