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Curate the project's knowledge base and memory in .unikit/memory/ — the indexed reference library of coding standards and framework usage patterns. ALWAYS use this skill whenever the user adds knowledge from a source: a URL, a file, a folder path, a PDF, an article, or a book — any path/link/document is a strong signal for the knowledge base, not a quick rule. Also use it to research or document how the project uses a framework, or to add a researched core/stack knowledge entry. Sub-commands by phrasing: "migrate the rules" or "promote rules" runs migrate-rules; "optimize the knowledge base" or "optimize the rules" runs optimise (extract large sections into references); "validate" re-syncs RULES_INDEX.md; "--module <id>" targets a module. For a quick one-line convention or correction typed as a prompt ("always do X", "never use Y", "remember this") with no source, use /unikit-rules; for architecture decisions use ARCHITECTURE.md.

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。

Author and own ONE content type's typed schema (the CONTENT-TYPE.md) at .unikit/gamedesign/content-types/CT-<slug>.md — the content / catalog layer of the GDD (cards, items, weapons, enemies, levels, quests, loot). Use whenever the user wants to ADD a new kind of content or UPDATE its schema, e.g. "add a new content type", "design the loot content", "define the card schema", "add an enemies content type", "add a rarity field to items", "switch this catalog to curated". Create the skeleton, fill its typed field schema (CT.fields), scale, relationships, and validation, AND revise the schema under the delta discipline (a schema change bumps the version + changelog). Selects the scale (bulk | curated) and self-registers content_types/content. This owns the content SCHEMA, not the catalog of values — adding individual entries/units is data handled in the editor, not a schema edit. To add or detail a system use /unikit-gd-system; to add a flow use /unikit-gd-flow; to apply a multi-zone edit use /unikit-gd-apply.

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。

Qualitative quality review of game design documents — answers "is this design good, and does it follow best practices?" — across all three GDD axes: systems, flows, and content types. Fans out adversarial lenses (completeness, clarity, pillar alignment, systems-math, fantasy-delivery, feasibility, scope) plus domain lenses that check it against the game-design rules, each prompted to find problems rather than validate; produces a severity-graded verdict and a two-bucket report (apply-ready fixes vs research), handed to /unikit-gd-apply or /unikit-gd-explore in one move. Scope is inferred: a named system, flow, or content type reviews that document; "all" runs a cross-document review. Use when the user wants a critique, quality judgment, or rules check of the design, e.g. "review the combat GDD", "review the onboarding flow". This is the subjective quality pass — for a mechanical consistency check (numbers, terms, IDs, references matching the registry) use /unikit-gd-verify.

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 LLM API RAG fine-tuning prompt engineering พร้อมคุม token cost และกัน prompt injection

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。

ML Engineer — model deployment, MLOps pipeline, feature store, model monitoring, A/B testing, production ML system 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。

เขียน SQL วิเคราะห์ insight สรุป finding และออกแบบ dashboard — แปลงตัวเลขเป็น action

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Sync customer data between [Gorgias](https://composio.dev/toolkits/gorgias) and [HubSpot](https://composio.dev/toolkits/hubspot) - find mismatches and missing contacts

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Enrich customer/lead profiles with business data using [LeadIQ](https://composio.dev/toolkits/leadiq), [RocketReach](https://composio.dev/toolkits/rocket-reach), or [Clearout](https://composio.dev/toolkits/clearout)

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。

Pre-landing PR review. Analyzes diff against main for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues.

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 a single seed-script owner OID variable when bootstrap data must satisfy owner-aware logic on fresh databases.

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。

Database Architect skill. Use this when you need to modify the database schema, add tables, or seed data. This project uses RAW SQL SCRIPTS orchestrated by .NET Aspire, NOT Entity Framework Migrations.

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。

Determine which LLM model to use for each agent spawn based on configuration, session directives, and task type

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 AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

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。

Expert .NET 10 Full-Stack Developer skill. Use this when implementing new features, vertical slices, or modifying existing logic in the JJGNET Broadcasting application. It covers Domain, Data (EF Core), Managers, Api, and Web.

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 the web with LLM-optimized results via the Tavily CLI. Use this skill when the user wants to search the web, find articles, look up information, get recent news, discover sources, or says "search for", "find me", "look up", "what's the latest on", "find articles about", or needs current information from the internet. Returns relevant results with content snippets, relevance scores, and metadata optimized for LLM consumption. Supports domain filtering, time ranges, and multiple search depths.

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 creating scientific magazine cover style images or prompts, research hero visuals, journal-cover-inspired concepts, academic poster covers, PPT covers, and high-impact scientific visual concepts. If the user asks to generate the image, use ChatGPT image generation.

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Use when creating high-quality scientific or medical infographic images or prompts, including review figures, disease maps, teaching diagrams, mechanism summaries, classification overviews, timelines, matrices, and academic visual explainers. If the user asks to generate the image, use ChatGPT image 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。

Use when working with Auto.dev APIs, vehicle data, VIN decoding, car listings, vehicle photos, specs, recalls, payments, interest rates, taxes, OEM build data, plate-to-VIN, CLI commands, MCP tools, or SDK methods for any automotive data task

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 orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work, generating implementation prompts, or deciding next loop state.

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 orchestrating Loop Factory specs from factory/specs/inbox or factory/specs/active, dispatching work to Codex or Claude agents, generating implementation prompts, or deciding next loop state.

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 Mastra framework guide. Teaches how to find current documentation, verify API signatures, and build agents and workflows. Covers documentation lookup strategies (embedded docs, remote docs), core concepts (agents vs workflows, tools, memory, RAG), TypeScript requirements, and common patterns. Use this skill for all Mastra development to ensure you're using current APIs from the installed version or latest documentation.

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Abductive reasoning about causes. Takes a phenomenon, separates observations from interpretations, then spawns diagnosticians applying different reasoning lenses (technical, human-factors, process, incentive-structure, environmental, temporal, measurement-artifact, statistical) in isolation. Orchestrator evaluates candidate causes for fit-against-evidence, calibrates confidence honestly, and reports leading candidate(s) with distinguishing evidence needed. Produces feedback only — no code, no tickets, no artifacts.

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 creating, reviewing, debugging, or maintaining Home Assistant custom Lovelace cards or card editors, including custom elements, Web Components, hass data, setConfig, getCardSize/getGridOptions, getConfigElement/getConfigForm, window.customCards, resource registration, HACS card packages, entity display, service calls, translations, or frontend build 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。

Optimizes natural-language image requests and rough prompts into ready-to-use GPT Image 2.5 prompts using the official prompting guide as a live reference. Use when users ask to improve image prompts, 优化生图提示词, 把想法变成提示词, or write prompts for GPT Image 2.5 Flare or Sunburst. Produces prompt text, not images, unless the user explicitly asks to 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。