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Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.

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This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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SolanaOS Hub — skill registry, gateway management, and control UI. Use when managing skills via ClawHub CLI, connecting to the SolanaOS gateway (TCP bridge on port 18790, WebSocket on port 18793), or working with the Lit-based control UI at ui/.

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Add, change, or debug external API integrations, vendor SDK adapters, and bridges to legacy business workflows. Use when an Action or domain service needs an outer technical dependency.

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Work from a registered surface or transverse route by inspecting nodes, neighbors, edges, contracts, owners, tests, and gaps before editing.

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Knowledge base from NIST SP 800-160 Vol 1 (Engineering Trustworthy Secure Systems) and Vol 2 (Developing Cyber-Resilient Systems). Use for systems security engineering (SSE): the three SSE framework contexts (problem/solution/trustworthiness), the 30+ design principles for trustworthy secure design, trustworthiness and assurance/assurance cases, security across the ISO/IEC/IEEE 15288 life-cycle process groups, and the Vol 2 cyber-resiliency engineering framework — 4 goals (anticipate/withstand/recover/adapt), objectives, 14 techniques, implementation approaches, and design principles for the APT/adverse-conditions threat model. Bridges systems engineering and the NIST security cluster (CSF/SSDF/RMF). Does not reproduce ISO/IEC/IEEE 15288 process text, nor cover SP 800-53 control catalogs in depth.

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Knowledge base from the NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.

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Knowledge base from the FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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Bindet das model-agnostische Computer-Use-Modul open-compute (Screenshot-Wahrnehmung, Windows-UIA-Elementklicks, sicherheitsgegatete Aktionen) fuer ALLE Agenten des Systems ein -- Claude Code, Codex und agy/Antigravity. Nutzen, wenn ein Dienst eine interaktive GUI- oder Browser-Aktion braucht, die kein reiner Text-/API-Weg loesen kann (Paradebeispiel: Tailscale-Reauth im Browser, sonstige Login-/Consent-Dialoge, Dialogfenster-Klicks). Standard ab 2026-08-02: open-compute statt Aufgeben oder Abwarten nutzen.

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pi-cua-integration is a Pi/Senpi extension that bridges Cua (trycua/cua) for computer-use sandboxes and host control. MUST USE when the user asks to drive a desktop UI, take screenshots, run a "computer use" task, control a sandbox, or invoke any cua_* tool. Triggers - "cua", "computer use", "sandbox", "screenshot the desktop", "click the screen", "take over the browser", "샌드박스 켜줘", "스크린샷 찍어", "내 컴퓨터에서 자동화".

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⭐ ПРАВИЛО (anton 23.09 голосом): «подними сессию/декомпозируй» адресовано ТЕБЕ = поднимай У СЕБЯ, своими средствами (Cursor → Task-tool/background agents, Codex → свои сессии, Claude → планировщик); нести задачу в другую LLM = ересь. Мост spawn_queue → Claude-планировщик — ТОЛЬКО когда нужна именно видимая scheduled-задача приложения. Триггеры: «подними сессии», «декомпозируй на сессии», lift-spawn.

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Bring an EPA SWMM (.inp) or EPANET (.inp) model into MIKE+ via the mike-plus MCP server — the bridge from an INP builder such as SWMMCanada or agentic-swmm into a MIKE+ database that mike_model_info / mike_run can use. Use when the user has an .inp and wants it in MIKE+. Requires MIKE+ installed + a checked-out license (demo mode caps the model size).

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Create and process videos using AI generation (Runway, Pika, Replicate, Sora, Veo) and FFmpeg processing. MCP bridge provides frame extraction (extract_video_frames); the Design Stack media worker handles full FFmpeg processing (resize, trim, compress, GIF, format conversion). Covers social-media-optimized video output. Use when generating AI videos, extracting frames/thumbnails, transcoding videos, creating GIFs from clips, or preparing video for social platforms.

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Optimize images for web performance — format conversion (WebP, AVIF), responsive sizing, compression, background removal, lazy loading, and WordPress media library integration. Uses MCP bridge tools (resize_image, remove_background) as primary; the Design Stack media worker (Sharp) as fallback for advanced/batch processing. Use when you need to resize images for social media, convert formats for faster page loads, remove backgrounds from product photos, generate alt text, or batch-process an entire media library.

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Publish and schedule content to social media platforms (Twitter/X, Facebook, Instagram, LinkedIn) via the MCP bridge schedule_social_post tool as primary. The Design Stack media worker API is the fallback for advanced platform management. Covers platform-specific formatting, media attachment, scheduling, and WordPress-to-social publishing workflows. Use when posting to social platforms, scheduling future posts, attaching media to posts, checking optimal timing, or debugging API delivery failures.

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End-to-end media pipeline automation — from AI generation through optimization to social publishing. Covers workflow design, WordPress integration, Redis job queuing, batch processing, multi-site pipelines, and orchestration. MCP bridge tools are the primary interface for individual operations; the Design Stack media worker handles batch and advanced processing. Use when you need to automate image generation→optimization→publishing chains, set up cross-site media sync, design batch processing pipelines, or orchestrate multi-step media workflows triggered by post publish or cron.

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Complete operational guide for the NV oOS (Open Operator System) WordPress plugin in Docker/WSL2 — setup, assistant creation, credential tokens, MCP tool calling, API key auto-detection, env var bridging, common fixes, and IGCSE study configuration. Use when setting up the plugin for the first time, creating assistants programmatically, generating MCP bridge tokens, troubleshooting Docker path issues, bridging API keys, or calling tools via JSON-RPC over HTTP.

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Generate images using AI models — Gemini, DALL·E, Stable Diffusion, Midjourney, Flux, Leonardo, Ideogram, and more. MCP bridge tools are primary; the Design Stack media worker is the fallback for advanced providers. Covers prompt engineering, model selection, size/aspect ratios, style parameters, batch generation, multi-site awareness, and WordPress integration. Use when you need to create hero images, social graphics, product mockups, brand illustrations, or any visual asset for marketing, social media, or e-commerce.

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Orchestrate any tdmcp test-coverage work: run the coverage harness, inspect coverage gaps, plan focused Vitest/msw or bridge tests, delegate coverage writers, verify coverage deltas, and re-run gates. Use whenever the user asks to raise coverage, add broad regression coverage, create a test harness, improve tests, re-run a coverage wave, or fix a coverage gate.

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How to author one new tdmcp MCP tool the house way — the file shape (XImpl + registerX), the Python-bridge pattern (buildPayloadScript/parsePythonReport over executePythonScript), the result helpers (errorResult/guardTd/jsonResult/structuredResult), fail-forward error handling, ESM/.js + Biome style, and the offline msw unit test that proves it. Load this whenever building, extending, reviewing, or testing a tdmcp tool file under src/tools/** (and its test under tests/unit/**). Use it for every Phase-13 / v0.5.0 feature-build tool and any re-run after a gate failure.

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Apply the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.

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Apply Gang of Four design patterns to solve architectural problems in TypeScript. Use when refactoring code architecture, implementing extensible systems, decoupling components, or following SOLID principles. Covers all 22 GoF patterns: Creational (Factory Method, Abstract Factory, Builder, Prototype, Singleton), Structural (Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy), and Behavioral (Chain of Responsibility, Command, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor).

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Use when a task requires exhaustive unabridged output, complete files, or strict prevention of placeholders and skipped code.

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素材库 ↔ 创作词库 ↔ 画像：harvest 用去 AI 味筛子抽专属词， guide 出用词包，acquire 按画像缺口出搜索计划。只读，不 apply。 触发：抽创作词、用词包、金句抽词、写之前看词、素材库词库打通、画像补词。

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