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Plan, deploy, operate, and troubleshoot Azure Local (formerly Azure Stack HCI): sizing and prerequisites, Arc registration, lifecycle updates, workloads (Azure Local VMs, AKS on Azure Local, images, disks, logical networks), SDN and network security, and failure triage — starting read-only and confirming before risky changes. WHEN: Azure Local, Azure Stack HCI, Arc resource bridge, custom location, Azure Local VM, Arc VM, AKS on Azure Local, AKS hybrid, SDN, Lifecycle Manager, Azure Local update, disconnected site.

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whisper", "OpenAI .NET".

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas.

Use when the user asks to query project memory, ask an oracle, use supported browser-backed reviewers, run Tau roundtable/single-handler workflows, ask Pi-native subagents from within Pi, run persona/deep-review workflows, generate image prompts, check OS/project health through composed skills, or run an ask DAG. This skill is the executable /ask runtime; do not replace it with an informal subagent, plain web search, or hand-written review; inside Pi, explicit Pi-native subagent targets are routed through the pi-subagents tool as an Ask target type.

Build video clips from reference images, reference videos, and reference audio with Wan-AI Wan 3.0 Prime Reference to Video on RunComfy. Up to 10 reference images, 5 reference videos and 5 reference audio clips are bound to a prompt that names them as "Image 1", "Video 1", "Audio 1", giving character, product and scene consistency across a 2-30 second shot at 480p, 720p or 1080p with a synchronized audio track. Documents the full input schema, the counted-second pricing model (reference videos are billed as duration, images and audio are not), and when to route to Wan 3.0 Prime text-to-video / image-to-video, Wan 2.7 or Seedance 2.0 Pro instead. Calls `runcomfy run wan-ai/wan-3.0-prime/reference-to-video` through the local RunComfy CLI. Triggers on "wan 3 prime reference to video", "wan 3.0 prime", "wan3 prime", "reference to video", "ref2v", "keep the same character across shots", "video from reference images", or any explicit ask to generate video from references with this model.

Generate reference-guided 1080p video with ByteDance Seedance 2.5 Reference to Video on RunComfy via the `runcomfy` CLI. Feed up to 9 reference images, 1-3 reference video clips, and 3 reference audio files into one call and get a 4-30 second 1080p clip with native synchronized audio, identity and style locked to your references. Documents the full input schema (images / videos / audios / aspect_ratio / duration / generate_audio), the counted-seconds billing model ($0.53 per second of reference video duration plus output duration), the 480p draft-then-deliver workflow, and when to route to Seedance 2.5 text-to-video, image-to-video, or Seedance 2.0 Pro instead. Calls `runcomfy run bytedance/seedance-2.5/reference-to-video/1080p`. Triggers on "seedance 2.5", "seedance 2.5 reference to video", "reference to video", "reference-to-video", "seedance 1080p", "ByteDance Seedance 2.5", "consistent character video", "style-locked video", or any explicit ask to generate video from reference images and clips.

Animate a single still image into a 4-30 second 720p cinematic clip with optional synchronized native audio using ByteDance Seedance 2.5 Image to Video on RunComfy. Documents the four-field schema (prompt, image, duration, generate_audio), the $0.35/s pricing, the fact that output aspect ratio follows the input image, and when to route to the Seedance 2.5 480p, text-to-video, reference-to-video or first-last-frame pages instead. Calls `runcomfy run bytedance/seedance-2.5/image-to-video/720p` through the local RunComfy CLI. Triggers on "seedance 2.5 image to video", "seedance image to video", "seedance i2v", "animate this image with seedance", "bytedance image to video", or any explicit ask to turn a still into video with Seedance 2.5.

通过 COM 自动化操控 Adobe Photoshop 的 CLI harness。支持项目管理、图层操作、选区控制、文字编辑、图像调整和多格式导出。

Strip C2PA and AI-generation metadata from images (PNG, JPEG, WebP) to remove "Generated by AI" / "ChatGPT" / "DALL-E" labels that platforms like LinkedIn, Instagram, and X display. Use when the user wants to clean AI-generated images before posting, remove AI attribution from photos, strip C2PA manifests, sanitize image metadata for professional use, or verify whether an image contains C2PA data.

Generate images with OpenAI GPT Image 2 via the Codex CLI, billed through the user's ChatGPT Plus subscription (no OpenAI API key, no per-image API cost). Use when the user asks to create, generate, or make an image, picture, illustration, icon, hero graphic, or concept art from a text prompt.

Convert, crop, resize, and compress images and videos for optimal LinkedIn upload quality and file size. Use when the user wants to prepare a photo, image, or video for LinkedIn (profile picture, cover photo, post image, article featured image, post video, or video ad), optimize file size for LinkedIn, crop and resize for LinkedIn dimensions, convert HEIC/RAW/PNG/MOV to LinkedIn-ready JPEG or MP4, or mentions LinkedIn media requirements, specs, or limits.

Turn any n reference images (with at least one person) into one exhaustively locked, always de-slopped, JSON-only AIGC image prompt whose every variable is pinned so each generation is nearly identical. Use for replicating a person's look, locking a character, or revising such a JSON after comparing a generated image to the reference.

Turn any n reference images (with at least one person) into one exhaustively locked, always de-slopped, JSON-only AIGC image prompt whose every variable is pinned so each generation is nearly identical. Use for replicating a person's look, extracting portrait features into a prompt, locking a character, or revising such a JSON after comparing a generated image to the reference.

Turns a plain-language brief into a production-ready prompt for OpenAI GPT Image models (ChatGPT Images 2.5, gpt-image-2.5-flare, gpt-image-2.5-sunburst, gpt-image-2). Returns a seven-block prompt, a settings block for the chosen surface, and three concrete levers for the next iteration. Covers text inside the image, reference binding, edits that do not drift, transparent backgrounds, and the hard API limits on size, quality and input images. Triggers: 'write an image prompt', 'gpt image prompt', 'chatgpt images 2.5', 'make me a poster', 'product shot prompt', 'article cover image', 'edit this image without changing X', 'transparent background logo', 'why does my text come out garbled', 'my edits keep drifting'. Not for video models (they use different syntax) and not for typography that must be pixel-exact.

Find, download and place free いらすとや (irasutoya.com) illustrations. Use whenever a slide deck, document, blog post, web page or design canvas would be better with an illustration or icon.

This skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English.

Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.

Designs and directs the visual assets that carry content — carousels, infographics, quote graphics, diagrams, and social imagery — including the generation prompts where they are AI-produced. Use this to turn a written piece into a visual format, design a carousel or infographic, create social graphics, or fix visuals that are not stopping the scroll.

Turn a reference image, screenshot, or mockup into token-driven, accessible code — infer the design system from the reference (palette, type scale, spacing, radius, layout archetype), map it to the 3-tier tokens, rebuild it, then verify with the kit's gates. Use when the user provides a design/screenshot and wants matching UI code.

Use when the user wants to post or schedule to Instagram through Publora: a single image, a 2-10 image carousel, a Reel or a Story. Requires a Business account and an image or video; text-only posts are rejected by the API. Not for Threads (use threads-post).

Write and refine prompts for OpenAI GPT Image 2.5 Flare and Sunburst, including image generation, reference-image editing, and iterative refinement. Use when the user explicitly targets GPT Image 2.5 or either GPT Image 2.5 model ID; do not apply these model-specific rules to other image models.

Visually rich AI PowerPoint slides with editable text and charts. Personal noncommercial use; other uses require written authorization.

Evidence-first application-security audit workflow for authorized repositories and environments. Use when reviewing a codebase, pull request, API, web application, AI-generated code, agent or MCP integration, cloud configuration, authorization (BOLA, IDOR, BFLA), business logic, payments, race conditions, secrets, supply chain, or release readiness for security weaknesses. Maps trust boundaries, selects applicable checks, runs parallel specialist review, independently verifies every material finding and refutes false positives, fixes root causes, and requires regression proof before reporting High or Critical issues.

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