2,998 件 ・ 関連度順
概要と使いどころ
投标人侧「投标文件合规检查」技能：对照招标文件系统检查投标文件的格式、资质、签字盖章、密封、份数、实质性响应等完整性，输出可打印打勾的四级结构化清单（🔴废标/🟡资质/🟢格式/⏰时间）与倒计时冲刺流程，提前发现废标风险；含输入预检、必查盲区强制校验、判定标尺、多轮状态管理、内置法条/案例库离线兜底。支撑招标文件合法性审查、法条问答、违规识别、异议投诉辅助。不适用于政府采购货物服务（财政部87号令体系）。触发词：检查投标文件/投标前合规检查/审招标文件合法性/围标串标认定。
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。
提供加密货币、Web3、区块链领域的法律咨询服务；当用户询问虚拟货币交易风险、OTC合规、发币募资、交易所合规、Web3创业合规、跨境支付或地区规则差异时使用
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。
Produces a plain-language comparison of advance directives and POLST/MOLST forms, covering legal status, clinician signatures, emergency precedence, clinical appropriateness, and document coordination. Use when the user asks about advance directive vs. POLST, living will vs. DNR, which document EMS follows, POLST vs. MOLST vs. POST, whether a healthy person needs a POLST, or document coordination in elder law, estate planning, or serious illness contexts.
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 Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams 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。
Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.
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。
Applies game theory to multi-agent coordination protocols with advisory claims, repeated interactions, daemon-mediated correlation, and deviation penalties. Use for Nash equilibrium checks, folk-theorem cooperation analysis, correlated-equilibrium routing, and price-of-anarchy audits in strategic agent systems. NOT for consumer pricing, card-game strategy, or general microeconomics outside agent coordination.
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。
Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.
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。
Managing third-party dependencies — version pinning, security auditing, license compliance, update workflows, lockfile management, supply chain security. Activate on "npm audit", "dependabot", "renovate", "pin versions", "dependency update", "supply chain", "license compliance", "lockfile", "security advisory", "typosquatting", "SBOM". NOT for internal monorepo package management (use monorepo-management) or publishing your own packages to npm/PyPI.
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 hierarchical DAG-like systems by separating undirected substrate from ordering metadata, then classifying diamonds, mixers, shortcuts, and hidden cycle structure. Use for workflow architecture, citation or genealogy DAG comparison, and information-flow diagnosis. NOT for ordinary cycle detection in arbitrary graphs, one-node runtime debugging, or systems with no real ordering constraint.
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 daemon/background processes that start on boot, run continuously, and manage their own lifecycle. Covers macOS launchd (plist files, agents vs daemons), Linux systemd (unit files), Windows services, process supervision, logging, health checks, graceful shutdown, auto-restart, and AI-powered daemons that manage LLM API connections and rate limits. Activate on: "daemon", "background process", "launchd", "systemd", "service file", "plist", "launch agent", "launch daemon", "auto-start", "always running", "process supervisor", "pm2", "background service", "boot service", "AI daemon", "long-running process". NOT for: container orchestration (use devops-automator), cron jobs that run and exit (use task-scheduler), web server deployment (use backend-architect).
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。
Tracks cumulative LLM costs across DAG execution and makes real-time decisions to stay within budget. Downgrades models, skips optional nodes, or stops early when cost exceeds thresholds. Use when managing execution budgets, analyzing cost breakdowns, or optimizing model routing for cost. Activate on "cost budget", "too expensive", "reduce cost", "cost optimization", "model downgrade", "budget exceeded". NOT for LLM model selection logic (use llm-router), pricing comparisons across providers, or billing/invoicing.
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。
How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).
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。
Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).
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。
Safety, privacy, cost management, and frank advice for building always-on AI agents with episodic memory. Covers data hygiene, privacy risk surfaces, runaway cost prevention, scope creep, psychological effects of persistent AI companions, and responsible deployment patterns. This is the skill that tells you what can go wrong and how to prevent it. Activate on: "agent safety", "always-on agent privacy", "agent cost control", "persistent agent risks", "AI companion safety", "agent data hygiene", "runaway agent costs", "parasocial AI risk", "/always-on-agent-safety". NOT for: architecture design (use always-on-agent-architecture), input design (use always-on-agent-inputs), application brainstorming (use always-on-agent-applications), healthcare compliance specifically (use hipaa-compliance).
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 and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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。
Manages git worktrees for parallel agent isolation: creates parallel/{name}-{timestamp} branches, merges with conflict detection. NOT for user feature branches (use git worktree directly).
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 agent-tree overhead audit (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。
Pre-ticket Fibonacci sizing for pasted stories; advisory by default, writes a Sizing Report. Opt-in auto-create adds a confirm gate. NOT for single-ticket sizing (mk:jira-estimator).
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。
Coordinate staged or parallel jobs across coding-agent runtimes and subagents: routes by capability/risk, isolates writers in worktrees, arbitrates results. NOT for a single-agent task (mk:cook).
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。
Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).
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。
Vue 3 testing best-practices review: Vitest + Vue Test Utils for components, composables, Pinia, Router, async/Suspense, forms, a11y, plus Playwright E2E strategy. Advisory only.
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。
Correct file paths, project layout, and naming conventions. Two modes: advisory (return a path) and organize (restructure).
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。
Pre-ticket Fibonacci sizing for a paste-mode batch of user stories. Default mode is advisory-only (writes a Story Sizing Report). Opt-in `--auto-create` delegates ticket creation to mk:jira-issue + mk:jira-collaborate with mandatory dry-run + single batch confirmation gate. NOT for single-ticket estimation (mk:jira-estimator); NOT for sprint capacity (mk:planning-engine).
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。