1,760 件 ・ 関連度順
概要と使いどころ
Generates structured bail hearing summaries from transcripts and case documents, extracting charges, arguments, conditions, and rulings. Use when summarizing bail hearings, pretrial detention decisions, bond hearings, or release condition orders.
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Drafts formal appellate mandates that conclude the appeal process and direct trial courts to implement appellate decisions. Extracts disposition language, remand directives, and procedural history from appellate records to construct jurisdiction-specific mandate orders. Use when preparing mandate orders, returning jurisdiction to lower courts, or formalizing appellate dispositions after ruling.
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。
Drafts appellee response briefs for federal and state appellate courts, exploiting standards of review and record evidence to defend trial court decisions. Trigger when the user needs to respond to an appellant's opening brief or defend a favorable lower court ruling on appeal.
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Runtime router for context decisions: read-more/stop/ask/assume/delegate, minimal read-set, compaction timing. Model: Write/Select/Compress/Isolate. NOT for agent-tree overhead audit (context-audit).
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Read-only audit of the project's agent-tree structural overhead vs the context window. NOT for cost tracking (budget skill); NOT for read/compact decisions (mk:context-engineering).
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。
Multi-agent collaboration bringing 2-4 perspectives into one discussion for architecture decisions and trade-off analysis. Use for 'should we X or Y', design review, or debate before deciding.
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 router for context decisions: read-more/stop/ask/assume/delegate, minimal read-set, compaction timing. Model: Write/Select/Compress/Isolate. NOT for .codex/ overhead audit (mk:context-audit).
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-only audit of .codex/ structural overhead: 'remove X save Y tokens' vs the context window. NOT for cost tracking (budget skill); NOT for runtime read/compact decisions (mk:context-engineering).
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。
Multi-agent collaboration session. Brings 2-4 agent perspectives into one discussion for architecture decisions and trade-off analysis. Use when asked "should we X or Y?", "let's discuss", or "design 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。
JSON-canonical session memory. Use when capturing session learnings, extracting patterns, or tracking costs — persists to .meowkit/memory JSON stores; matching Markdown files are generated views. Activates during Phase 0 (Orient) to load context and Phase 6 (Reflect) to persist it. NOT for weekly-cadence engineering retrospectives (see mk:retro); NOT for arbitrary file storage.
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 router for context decisions during a task — decide read-more / stop / ask / assume / delegate, pick a minimal read-set, and time compaction. Front-page mental model is Write / Select / Compress / Isolate; a lazy pattern index routes to 25 context-engineering patterns, one reference per decision. Use when context is near budget, a long session is degrading, you must choose what to read for a task, or you are deciding /compact vs /clear vs a sub-agent. NOT for structural .claude/ overhead audits (see mk:context-audit); NOT for owning persistence (see mk:memory); NOT for USD cost (see mk:budget).
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。
Operational decision frameworks: triage, escalation, case management, billing ops. Use for 'how to handle X cases', 'build triage', 'escalation protocol'.
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-only audit of `.claude/` structural overhead. Reports prioritized "remove X save Y tokens" recommendations against the model context window. NOT for monetary cost tracking — that's /mk:budget. NOT for transcript size monitoring — long-session continuity defers to Claude Code native compaction. NOT for runtime context decisions (what to read, when to compact) — see mk:context-engineering. Use when planning to add context capacity, diagnosing perceived slowdowns, or auditing health.
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 answering factual or explanatory questions about the current project — codebase, architecture, conventions, workflows, constraints — with minimal cited evidence. Triggers on "how does X work here", "what does X mean", "why is X structured this way", "is this claim true in the repo", "explain X with sources". NOT for brainstorming or comparing approaches (mk:brainstorming), trade-off decisions (mk:party), debugging/root cause (mk:investigate), code review (mk:review), plans (mk:plan-creator), security audits (mk:cso), visual explanations/diagrams (mk:preview --explain), test-coverage gaps (mk:nyquist), library docs (mk:docs-finder), or workflow navigation (mk:help).
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 a grounded Human Design BodyGraph and whole-chart reading from exact birth data. Use for Human Design charts, BodyGraph, decisions, profile, channels, talents, work, wealth, relationships, mission, or chart-based follow-up questions. Supports Chinese and English.
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。
Multi-agent structured debate with anti-groupthink mechanisms — Devil's Advocate, minority opinion protection, 2-round hard cap. Use when divergent perspectives matter more than consensus (architecture decisions, design tradeoffs, contested specifications).
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。
Store and semantically search agent memory with ONNX vector embeddings on a ruOS desktop, using the Ruflo CLI. Use to persist patterns/decisions across sessions and recall them by meaning rather than exact key.
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Use this skill for ANY of the following — do not skip it even if the query seems simple: strategic decisions, business analysis, thesis building, complex problem solving, geopolitical analysis (non-market), philosophical reasoning, academic research, system design, decision under uncertainty, long-form thinking, or any question that benefits from First Principle deconstruction and multi-agent reasoning. ALWAYS use this skill when the user asks about: why something works the way it does, whether a decision is correct, how to think about a complex problem, what could go wrong, analyzing a situation from multiple angles, or any "should I..." question involving real-world trade-offs. This is NOT a market/trading skill — for financial markets, BTC, gold, stocks, forex, macro economics, use money-atlas-intelligence-os instead.
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。
Connect business positioning to a concrete visual language. Maps strategic positioning (category, differentiator, who-it's-for, who-it's-against) onto visual decisions — palette, type, imagery, tone — so the look reinforces the strategy. Use when the user says "positioning into visuals", "what should the brand look like given our strategy", "align visuals with positioning", or is moving from strategy to brand expression. Part of the Creative Production set.
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Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.
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 and save a session summary before ending conversation. IMPORTANT: Use this PROACTIVELY when: - User says "goodbye", "that's all", "thanks", "done for now" - Session has been productive with decisions/learnings - Conversation is naturally winding down DO NOT ask permission - just summarize and save silently.
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。
Find deepening opportunities in a codebase, informed by the project's own domain language and prior design decisions. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
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Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG. Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow", "user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation 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。
Naturalistic decision-making research showing how experts make decisions through recognition-primed processes
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。