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概要と使いどころ
Comprehensive Amazon profit analysis and revenue optimization. Revenue waterfall analysis, hidden fee identification, ACoS impact assessment, return cost calculation, and profitability optimization strategies. Use when the user asks about Amazon profit analysis, profitability calculation, fee analysis, margin optimization, or revenue breakdown.
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 products around price using the 9 rules from Ramanujam and Tacke - WTP conversations, needs-based segmentation, Good/Better/Best configuration, monetization models, behavioral pricing, and price integrity. Use when designing new products, validating pricing for SaaS/B2B/B2C launches, choosing between subscription/usage/freemium models, fixing post-launch sales below plan, diagnosing failed launches as Feature Shock/Minivation/Hidden Gem/Undead, or when product teams say 'let's price it later'. Not for pure commodities or cost-plus regulated 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。
Designs irresistible Grand Slam Offers using Alex Hormozi's value equation, obstacle-solution mapping, value-cost trim/stack, and offer-naming patterns. Use when fixing low conversion rates, packaging products, pricing services, designing high-converting offers, or when prospects browse but don't buy. Covers value stacking, guarantees, scarcity, urgency, and pricing psychology for validated markets.
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 a User Generated Content (UGC) strategy — getting customers to create content, review generation, UGC briefs for creators, social campaigns, contest mechanics, legal rights management, and repurposing UGC in paid ads. Use when the user says "UGC", "user generated content", "get customers to create content", "customer content", "UGC ads", "UGC creators", "organic UGC", "UGC strategy", "how do we get customers to post about us", "brand hashtag", "UGC brief", "influencer UGC", "UGC for ads", "customer photos and videos", or wants to use real customer content to build trust and reduce content production costs.
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 run a full ad-campaign pipeline on top of Higgsfield Marketing Studio — 'create a campaign', 'build a content plan', 'run the content pipeline', 'generate 100 UGC videos', 'plan and schedule a launch', 'make a batch of ads from my product', or 'how much did this save vs traditional production'. Covers the 5-stage orchestration (Research → Plan → Generate → Publish → Report), the UGC-first 5-format campaign mix (UGC Entertainment, Street Interview, Unboxing, Product Review, ASMR), the even-split allocation math, button-driven onboarding, the per-batch generation gate, and the publish + cost-report tail (satellite). Defers all Marketing Studio API ground-truth (presets, params, hooks, avatars) to higgsfield-marketing-studio.
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 already has a FINISHED video and wants Higgsfield to repurpose, restyle, clip or analyze it: Shorts Studio (restyle one 4–120s source video into a set of short-form clips from a style preset), Clipify (turn one YouTube video into subtitled clips), Virality Predictor (predict virality, hook strength, attention, retention risk with a dashboard), or Video Analysis (scene-by-scene breakdown of an uploaded video or a YouTube link). Also use when the user asks which of these cost credits and which are free, 'turn my video into shorts', 'will this go viral', 'score my ad', 'break this video down scene by scene'. Names: shorts_studio_create, shorts_studio_create_preset, shorts_studio_list_presets, shorts_studio_status, clipify, virality_predictor, brain_activity, video_analysis_create, video_analysis_status.
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 mentions Higgsfield Canvas, a node-based or node graph workspace, an infinite board/canvas, chaining generations into a pipeline, or wants to wire prompts → images → videos across models on one surface. Covers what Canvas is, the node categories, the seven models that run inside Canvas, the named canvas patterns (Simple Seedance, Extend Video, Image Edit, StoryBoard With Elements, Long Video fan-out), the build-free / generate-paid cost model, reusable templates, assets-as-nodes, and Shared Canvas live collaboration. Also trigger on 'Higgsfield ComfyUI alternative', 'node workflow', or 'connect nodes to build a scene/campaign'.
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。
Pull Google Ads performance data and generate reports. Use when asked about ad campaign performance, keyword costs, quality scores, ROAS, conversion tracking, or ad spend analysis. Trigger phrases: "google ads", "adwords", "campaign performance", "ad spend", "quality score", "CPC report", "ROAS", "ad conversion", "keyword performance", "google ads report".
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 Glasser to retrieve live marketing data through one prepaid account when a task needs SERPs, keyword metrics, ad libraries, community posts, company data, or another external API and no suitable free source or existing integration is available. Prefer the user's own integrations; Glasser fills data gaps and every run has a disclosed cost.
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。
Full trip planning orchestration. Combines flight search, hotel search, and points optimization into one cost analysis. Use when the user says "plan a trip" or wants the complete picture for a destination.
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 cost-safe geospatial SQL for BigQuery, DuckDB, Snowflake, Wherobots, and Postgres and/or render results on an interactive Dekart map.
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。
Weekdays, file work only, no browser at all. Takes the single highest ranked open fault on the fleet and turns it into something the member can act on in a minute: the fault in one sentence, the first record that shows it, what changed around that date, three ranked causes each with its evidence line, what the failure has cost, and one paste ready correction line with the exact path it belongs in. It reads other Employees strictly read only, writes nothing outside its own folder, sends only where you released the channel, spends only where you released it, and never touches a credential.
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。
Weekdays, conditional browser lane. Reads the ticket ledger and the account, billing, and usage surfaces the member named, and decides which paying customers are about to leave. Every flag is a dossier carrying the trip wires that fired, the exact evidence behind each one, that account's whole history, and one suggested save with its cost and the screen it happens on. It reads billing screens and touches no control on them: a refund, a credit, a plan change, and a cancellation are named and never made.
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。
Knowledge base from "Solving a Million-Step LLM Task with Zero Errors" (Meyerson, Paolo, Dailey, Shahrzad, Francon, Hayes, Qiu, Hodjat, Miikkulainen — arXiv:2511.09030). Use when designing long-horizon, multi-step agentic LLM pipelines that need very high reliability, applying Maximal Agentic Decomposition (MAD), first-to-ahead-by-k voting, or red-flagging error correction, estimating cost/reliability scaling laws for multi-agent systems, or studying Massively Decomposed Agentic Processes (MDAPs).
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 a session ends and the user wants a summary of what was done and what it cost. Writes a shareable SESSION_REPORT.md with outcomes, work performed and estimated resource usage.
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 AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.
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。
Develop an evidence based business plan spanning customers, product, operating costs, hiring and governance. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.
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, derive, optimize, and diagnose structured AI image prompts for luminous cinematic Eastern xianxia environments under one inherited master canon with measurable divine scale, fixed five-layer space, 40–60% content-bearing breathing air, monumental Eastern architecture, railing-free edges, and coherent immortals and costume. Cover one-location scenic landmarks, immense inhabited celestial realms, cloud-borne palace cities, high-key Eastern sky-megastructure imagery, and the explicit `仙界大境原典` creative route. Use for Chinese fantasy scenery, places where immortals live, heavenly capitals, colossal celestial gates, architectural fragments above cloud seas, measurable divine scale, unusual framing, color and lighting direction, style-preserving variants, prompt rewrites, diagnosis, or direct image generation. Support 16:9, 21:9, 4:3, 3:2, 4:5, and 9:16; default to 16:9 for one prompt and never infer a nine-image series from a vertical ratio.
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。
Modernizes a bounded capability to reduce demonstrated maintenance cost; not routine upgrades or performance tuning.
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。
Rules for writing concise, token-efficient workflow and skill files. Prevents over-building that requires costly optimization passes. Use when creating or editing workflow files, SKILL.md files, or new skill definitions.
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。
跨字段比较校验工具。读取结构化数据文件（CSV、TSV、Excel等），检查两个字段之间的逻辑约束关系 是否满足（如 start < end、min ≤ max、price >= cost），支持六种比较运算符，多约束可组合使用。 当用户提到跨字段校验、字段间比较、逻辑约束检查、大小关系验证等需求时使用此skill。 即使用户没有明确说出"跨字段"，只要任务涉及验证两个字段值之间的逻辑关系，就应该使用此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。
Show full session token usage, costs, TLDR savings, and hook activity
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。
Brand-consistent asset sets from DESIGN.md — hero, section illustrations, icon sets, OG image — generated through whichever channel the user actually has (grok build image_gen, Codex $imagegen, Gemini, xAI/Recraft/OpenAI keys) as recorded by omd:setup, with a provenance and cost ledger. No channel → prompt pack + manual queue, never stock. Trigger: '이미지 세트 만들어', '에셋 생성', 'generate assets', 'hero image', '아이콘 세트', 'OG 이미지'.
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。
Guided setup of the tools the design harness can use — image/video generation channels, browser, encoders. Detects what the user already has, explains pros/cons/costs, asks instead of instructing, and saves choices to .omd/config.json so every later design task reuses them. Trigger: 'omd setup', '도구 셋업', '이미지 생성 뭐 쓸 수 있어', 'configure media', '세팅해줘', 'what can you generate'.
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。