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概要と使いどころ
Orchestrate the narrative team: coordinates narrative-director, writer, world-builder, and level-designer to create cohesive story content, world lore, and narrative-driven level design.
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。
Orchestrate the live-ops team for post-launch content planning: coordinates live-ops-designer, economy-designer, analytics-engineer, community-manager, writer, and narrative-director to design and plan a season, event, or live content update.
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。
Validate that a story file is implementation-ready. Checks for embedded GDD requirements, ADR references, engine notes, clear acceptance criteria, and no open design questions. Produces READY / NEEDS WORK / BLOCKED verdict with specific gaps. Use when user says 'is this story ready', 'can I start on this story', 'is story X ready to implement'.
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。
Break a single epic into implementable story files. Reads the epic, its GDD, governing ADRs, and control manifest. Each story embeds its GDD requirement TR-ID, ADR guidance, acceptance criteria, story type, and test evidence path. Run after /create-epics for each epic.
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。
Generates a sprint or milestone retrospective by analyzing completed work, velocity, blockers, and patterns. Produces actionable insights for the next iteration.
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 a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
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。
Iterative Python via live Jupyter kernel (hamelnb).
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。
Install a Skill into the current workspace from a Git repository or local directory. Use this whenever the user wants to: - Add a new Skill to the agent ("install the X skill", "add the foo skill", "I want a skill that does Y, find one") - Pull the latest version of an already-installed Skill ("update the X skill", "refresh my skills") - List Skills currently active in the workspace - Remove a Skill they no longer need Trigger on phrases like "装一个 X skill", "安装技能", "install skill", "update skill", "list skills", "remove skill", "找个能 X 的 skill". Do not require the user to say the literal Skill name — if they describe a capability, propose a candidate Skill (Git URL or local path) and confirm before installing.
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。
Runtime QA of a change: start the app, test acceptance criteria and edge cases, and report evidence. Use for "test this", "QA", "does it work", exploratory checks, or post-review runtime verification. Not static code review.
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。
Authoring unified specification packages across Business/Development/Design teams via staged elaboration (L0 Vision, L1 Requirements, L2 Team Detail, L3 Acceptance Criteria). Use for cross-team specs.
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。
Authoring standalone and cross-team specifications: PRD/SRS/HLD/LLD, staged L0-L4 unified packages, BDD acceptance criteria, and traceability. Use for technical or multi-audience documentation; not implementation or architecture 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。
Implementing production iOS/Android/macOS native features (SwiftUI, Compose) and iterating a screen against a reference design. Not for cross-platform RN/Flutter (Forge prototypes only; production is out of scope) or web (Artisan).
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。
Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).
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。
Orchestrating multi-specialist task chains and scope-adaptive product delivery: classifies intent, selects and executes the minimum viable chain, aggregates results, and verifies acceptance criteria. For multi-domain tasks, build-first delivery, and product lifecycle execution.
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。
Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.
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 tests for a completed phase based on UAT criteria and implementation
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。
Search and analyze TikTok video data, filter videos by region, creator, product, category, views, duration, publish time, selling/ad/AI video flags, and return views, likes, comments, shares, favorites, video sales and GMV metrics across 16 TikTok Shop sites. Trigger when users mention TikTok video search, TikTok video list, TikTok promotional videos, TikTok video data, TikTok video views, TikTok video sales, TikTok video analytics, Nexscope video, TikTok video search, TikTok video list, TikTok video analytics, TikTok promotional videos, TikTok video views, TikTok video engagement. Even if the user does not explicitly mention "Nexscope" or "TikTok", trigger this skill whenever their need involves searching or analyzing TikTok video performance metrics by criteria.
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。
Query patent forward citation details from the Nexscope patent database. Triggered when users ask about patent citations, cited patents, cited references, patent references, forward citations, prior art citations, or want to see which patents and non-patent literature specific patents cited during the application process, patent cited references, forward citations, patent references, citation analysis, or Nexscope. When users provide a patent ID or publication number and need citation information, even if they do not explicitly say "forward citation," any request about which references a patent cited applies.
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 Nexscope data to search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, this skill should also be triggered.
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。
Amazon reverse product selection: filter Amazon niches and keywords by 30+ business dimensions (market size & growth, price tiers & share, competition density & top concentration, demographics such as age/gender/income, review highlights & pain points) from a metrics pool aggregated from historical business insight reports. Trigger when users mention reverse product selection, metrics filtering, niche reverse lookup, blue ocean niche discovery, low-competition niche, newcomer-friendly niche, brand-fragmented market, pain-point entry, feature reverse lookup, pricing tier opportunity, demographic-based selection, or similar terms. Even if the user does not explicitly say "reverse product selection," trigger this skill whenever the request involves filtering Amazon niches that match specific business criteria.
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。
Nexscope Product Database multi-condition filtering. Filter Amazon products by category, price, sales volume, revenue, reviews, rating, weight, BSR rank, LQS, seller type, and more across 10 marketplaces. Trigger when users mention Amazon product selection, product database filtering, BSR rank filtering, category-based product discovery, high-rating low-competition products, FBA product search, Amazon product discovery, or similar terms. Even if the user does not explicitly mention "Nexscope" or "product database," trigger this skill whenever the request involves filtering Amazon products by multiple criteria or discovering potential products.
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 Nexscope market list capability to filter Amazon niche markets by category dimensions, supporting market size, competition, top concentration, seller structure, new product share, price/rating/margin ranges, and many other criteria for discovering viable markets and evaluating product selection directions. Trigger when the user mentions Amazon market research, niche category research, market opportunity screening, market concentration analysis, new product opportunities, market selection, Nexscope market research, category market research. Even if the user does not explicitly mention "Nexscope", if the need is to screen and evaluate Amazon markets by category dimensions, this skill should also be triggered.
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。
Convert a vague mini-program idea, feature request, or partially documented product into an evidence-calibrated product specification with target users, core problem, version-one scope, main and exception flows, page responsibilities, state matrices, and testable acceptance criteria. Use when users ask to define an MVP, clarify requirements, organize product flows, specify page behavior, resolve ambiguous product states, or prepare a stable handoff before architecture or implementation. Separates facts, user decisions, assumptions, unknowns, and future ideas; never invents product logic merely to complete a screen or technical plan.
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 shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. (gstack)
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。