Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing 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。
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概要と使いどころ
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing 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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing 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。
Use when the user provides multiple options and wants a specific 16-minds (MBTI) personality to make the decision in that personality's judgment style. Triggers on Japanese phrases like 「ENTJで決めて」「INFPならどっち選ぶ」「INTJとして判断して」, English equivalents like "decide as ESTJ" / "which would ENFP pick", or 16-minds commands with a `--decide` flag. Requires both a personality and 2+ options to be present or extractable from the request.
Personal App-in-Skill voice-of-customer desk (Busabase App-in-Skill) aggregating user feedback from support email, Discord/Slack communities, X replies, app-store reviews, surveys, and interviews into clustered feature requests and a roadmap decision queue. Use when the user invokes $kelly-feedback or /kelly-feedback, or mentions user feedback, voice of customer, feature requests, roadmap decisions, feedback clustering, feedback triage, request dedupe, changelog replies, decline replies, or reviewing what users are asking for.
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 publish-ready LinkedIn posts for B2B SaaS marketing leaders writing from an operator point of view: real decisions, real trade-offs, real constraints, and defensible lessons. Use when turning an internal observation, framework, campaign decision, miss, or shipped artifact into a public LinkedIn post. Best for demand generation, ABM, marketing ops, product marketing, and SaaS GTM leaders building credibility through practical operating insight. Not intended for generic brand posts, company announcements, sales outreach, or long-form 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。
Write or edit Japanese articles, reports, guides, and public-facing drafts so the argument is clear and the prose reads naturally. Preserve the author's meaning, evidence, and voice when revising. Use for requests to write from a topic or notes, remove AI臭 / AIっぽさ, improve awkward wording or structure, or format notes as 箇条書き, チーム共有, 社内共有, a decision note, handoff, Slack post, or Teams post. For existing prose, ask Gemini through Antigravity CLI to rewrite naturally by default, then audit against the source; use the current model if unavailable. Do NOT use for ordinary Japanese answers, progress updates, technical explanations, code review, English-only prose that is not a bullet-formatting request, or an explicitly incompatible format.
Delegate writing predictable code to the `code-write` command, which has a cheaper writer model write the target file directly so the generated code never enters your context. Use for tests, scaffolding, fixtures, boilerplate, or a new file that copies an existing pattern, and when the user says "code write", "生成を委譲", or "雛形を書かせて". Do NOT use for design decisions, changes to public contracts or shared modules, edits inside an existing file that needs judgment, or logic with subtle correctness requirements.
Three-file state management for session continuity. Maintains STATE.md, DECISIONS.md, and PROGRESS.md as human-readable session context alongside the SQLite ticket database. Use this skill when updating workflow state, logging decisions, tracking session progress, or resuming work after a context break.
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。
Architecture Decision Records — context, decision, consequences, supersession trail.
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。
Facilitates conversational discovery to create Architectural Decision Records (ADRs) for non-functional requirements using the ISO/IEC 25010:2023 quality model. Use when the user wants to document quality attributes, NFR decisions, security/performance/scalability architecture, or design systems with measurable quality criteria. This should trigger for requests such as Create ADR for Non-functional requirements; Document Non-functional requirements; Capture Non-functional requirements; Generate Non-functional requirements in an ADR. Part of cursor-rules-java project
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。
MAGI System v2 — AoT 分解 + MELCHIOR/BALTHASAR/CASPAR 合議 + gabriel adversarial probe による 構造化意思決定フレームワーク。判断ポイント 2+ / 影響レイヤー 3+ / 選択肢 3+ で使用。 Use when facing complex decisions with multiple trade-offs or architectural choices.
MAGI System v2 — AoT 分解 + MELCHIOR/BALTHASAR/CASPAR 合議 + lam-harness:gabriel adversarial probe による 構造化意思決定フレームワーク。判断ポイント 2+ / 影響レイヤー 3+ / 選択肢 3+ で使用。 Use when facing complex decisions with multiple trade-offs or architectural choices.
Prepare the internal case for hosting an Otameshi Tenshoku trial: total what it costs the company with candidate pay, staff hours and platform or incidental fees kept apart, compare it with the budget, state what remains if no hire follows, and lay out the risks, stop conditions, decision owners and deadlines. Use when a hiring manager needs approval or budget from a decision maker, a recruiter has to explain the plan to the team that will host, or a small-business owner wants to see the full cost including their own time before posting; do not use to design the work (trial-work-design), to write the listing (trial-listing-draft), to predict whether a hire will follow, to state platform fees, agency fees or market rates from memory, or to submit the request or post the listing on the user's behalf.
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。
Guide for building and refactoring a FastAPI + Prefect + PostgreSQL/pgvector analytics pipeline that extracts insights from chat data using LLM classification, embeddings, UMAP/HDBSCAN clustering, and narrative generation. Use this skill whenever working on this project's codebase — including adding features, refactoring, debugging, creating new endpoints, modifying pipeline stages, or reviewing architecture decisions. Also trigger when the user asks about project structure, stack decisions, async migration, or best practices for this specific 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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing 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。
Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.
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。
Curate durable PWDEV Flow project decisions, lessons, and conventions. Use when the user asks to capture, list, show, link, supersede, or select project memory, or when an approved workflow decision should persist across phases.
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。
Analyze meeting transcripts, recordings, and notes to extract action items, key decisions, topics, sentiment, and generate summaries using NLP and AI. Use when processing meeting transcripts, extracting action items, summarizing discussions, identifying key decisions, tracking topics and themes, analyzing sentiment, generating meeting notes, or building collaboration and productivity tools.
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。
アーキテクチャ決定会議。技術選定・設計方針・構成変更を多角的に検討し、ADR(Architecture Decision Record)として出力する。「アーキ会議」「技術選定して」「設計会議」「どっちの構成がいい」「ADR書いて」などのリクエストで使用。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context.
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。
Grilling session that challenges your plan against the existing domain model and sharpens terminology. Use when user wants to stress-test a plan against their project's language and documented decisions; propose documentation updates for approval as decisions crystallise.
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。
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented 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。
Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).
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。
Think through the consequences of consequences — not just what happens immediately, but what happens next, and next after that, across time. Load when a decision looks obviously good or obviously bad on initial read, when the user is optimising for a short-term outcome that might create a long-term problem, when unintended consequences are a concern, or when deep-thinking diagnoses a second-order frame. Triggers on "what are the downstream effects", "what happens after that", "unintended consequences", "think ahead on this", "long-term vs short-term", or "what comes after that". Based on Howard Marks second-level thinking and Farnam Street mental models. Most powerful for decisions with delayed consequences or systemic effects.
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。