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概要と使いどころ
Drafts a U.S. C-TPAT Security Profile for CBP submission covering physical, personnel, procedural, conveyance, and IT security domains. Use when preparing C-TPAT enrollment, certification, validation, or recertification profiles, or assembling a CBP-ready security narrative. Trigger: C-TPAT, CBP security profile, supply chain security, trusted trader, customs validation.
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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。
当AI智能体需要将法律推理结果组织为完整、自洽、有说服力的论证结构时触发此技能。 典型触发场景包括但不限于： - 撰写法律意见书、代理词、辩护词、答辩状等法律文书中的论证部分 - 回答用户提出的复杂法律问题，需要多步骤推理并呈现结论 - 对争议焦点进行系统性分析，需要构建从事实到法律到结论的完整推理路径 - 反驳对方观点时，需要构建替代性论证链条 - 对多个法律问题进行综合分析，需要确保各论证之间逻辑一致、互不矛盾 - 在法律研究备忘录中呈现分析结论 触发信号关键词：论证、分析、推理、为什么、证明、理由、论述、阐述观点、法律分析
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Use when writing, reviewing, or deploying Solidity smart contracts — token contracts, signature-gated claim systems, merkle-gated staking vaults, allocation modules, role-based proxies, or factory patterns. Triggers on edits to `*.sol` files, `hardhat.config.*`, `foundry.toml`, deploy scripts, or mentions of "smart contract", "Solidity", "Web3", "EVM", "Hardhat", "Foundry", "ERC20/721/1155", "merkle", "staking", or "on-chain". For security audits and adversarial review see security-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。
Cross-stack security review — auditing vulnerabilities across infrastructure, smart contracts, CI/CD pipelines, and AI agent systems, plus auth/sessions/crypto and validating user input at API and infrastructure boundaries. Triggers on mentions of "vulnerability", "pentest", "OWASP", "access control", "injection", "CSRF", "JWT", "smart contract audit", "supply chain", "OIDC", or any review of security-sensitive code paths spanning more than one layer. For PII sanitization see [security](../security/SKILL.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。
This is the MistEye security gate skill. It is triggered by pre-installation risk checks (including Skill/MCP dependency manifests), pre-access security checks for domains or URLs, IoC malicious detection, and supply-chain risk blocking, especially for common requests such as "take a look at this address/website." It must use `https://app-api.misteye.io/functions/v1/detect`; in dependency and external-link scenarios it has the highest priority. If the API returns `status="malicious"` or any `matches`, the action must be blocked and explicitly reported as "blocked". Only after the first installation is complete should the user be reminded that daily patrols can be enabled with OpenClaw or Hermes (default once per day).
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。
Produce a UGC-style talking-head video ad end to end on the MaxFusion MCP — research the product with user-supplied competitor leads, tear down 5 competitor Meta ads and 5 organic TikToks, write a pain-driven script that sounds like a person talking (never like an ad), reverse-prompt a rights-clean photoreal actor from a user-supplied reference photo, generate two chained 10-second Gemini Omni clips that play as one continuous take, and ship a single seamless 9:16 MP4. Use whenever the user wants a UGC ad, a talking-head ad, an ad for an app or product, mentions Omni or Gemini Omni Flash, drops an actor photo and asks for a video ad, or says anything like "make me some UGC", "let's make ads for X", or "another variation". Built for Claude Code (local ffmpeg); degrades to Claude app with documented fallbacks.
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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith. Analyze agent behavior, investigate errors, and review tool calls and performance metrics.
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。
Detect cell-ordering / enchainement problems in Jupyter notebooks (canonical-order slippage, misplaced or forgotten cells). Arguments: [target] [--severity HIGH|MED|LOW] [--json]
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。
豆包环境专用的视觉物料生产：PPT / Keynote / HTML deck、公众号封面与正文配图、B站/YouTube 视频封面。存在的全部理由是走豆包内置 image_gen（默认 seedream_5.0_pro 模型），不花第三方图像 API 的钱——不在豆包环境就别用。用户说做PPT、幻灯片、演示文稿、deck、公众号封面、视频封面时触发。
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。
Agent system design patterns distilled from the OpenClaw codebase. Gateway-centric control, Session Key hierarchy, Binding routing, Lane Queue concurrency, Sub-agent Spawn+Announce, 4-tier automation, Plugin architecture, Policy Chain, Failover strategies. Triggered by "gateway", "session", "binding", "lane", "spawn", "announce", "heartbeat", "cron", "hook", "plugin", "policy", "failover" keywords.
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 extend pd-console — Port Daddy's GPU-native macOS operator console (GPUI 0.2.x, Zed's Rust UI). Covers the render-agnostic Block/Pane(Surface) contract, the two-thread reqwest↔smol refresh pipeline, Taffy flexbox layout, uniform_list virtual scroll, focus + keyboard nav, the OKLCH theme and ICS maritime flag badges, GPUI's missing text-input, and the real feature-gated cargo/CI gate. Use when adding panes, visual polish, or debugging GPUI rendering/layout/focus in core/pd-console. NOT for the TypeScript daemon, generic Rust toolchain/borrow-checker help (use rust-with-claude-code), or non-pd GPUI apps with a different theme/architecture.
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。
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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 bonded agent systems, escrow workflows, slashing rules, oracle trust, Sybil cost, and front-running risk in protocols where agents post bonds or stake reputation. Use for collateral sizing, griefing analysis, dispute design, and bonded-agent security reviews. NOT for smart contract auditing, DeFi tokenomics, blockchain consensus design, or trading strategy.
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。
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool 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。
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。
Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
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。
Vulnerability analysis: OWASP Top 10, current threat landscape, supply chain security, attack surface mapping, risk prioritization. Use for security audits. NOT for STRIDE/CSO infra audits (mk:cso).
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。
Chief Security Officer mode: infrastructure-first audit of secrets, dependency supply chain, CI/CD, LLM/AI security, OWASP Top 10, STRIDE, active verification. Daily/comprehensive, trend tracking.
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 error triage: detect language, load fix patterns, auto-retry. Chains into mk:verify. NOT for runtime errors (mk:fix); NOT for architecture debugging (mk:investigate).
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 a problem requires structured step-by-step reasoning before acting. Prevents jumping to conclusions or guessing root causes without evidence. Triggers on complex debugging, multi-step analysis, hypothesis verification, or when mk:fix invokes diagnosis phase. NOT for being stuck on approach (too many implementations, forced assumptions, innovation block) — use mk:problem-solving 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。
Build error triage: detect language, load fix patterns, auto-retry. Use for 'build failed', 'fix compilation', 'type error'. Chains into mk:verify. NOT for runtime errors (see mk:fix); NOT for architectural debugging (see mk:investigate).
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。
Assess package choices and generated dependency names, publisher or release anomalies, incident exposure, and changes in package risk reports. Use before trusting a new dependency, when a release changes unexpectedly, or when investigating a supply-chain alert.
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。
Passive, pattern-calibrated risk discovery for authorized security research over an open-source project, repository URL, dependency, release, or local checkout. Use when the security research agent needs to find highest-risk areas, map attack surface, identify likely bug-hunting targets, prioritize security review, inspect structural/logical/security-critical code, compare vulnerable versus patched versions, reason from broad high-severity vulnerability families, or remember areas of interest for later attack-chain research.
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。