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概要と使いどころ
Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the user wants to train a WAN or Z-Image LoRA; covers local + RunPod setup, dataset prep, key params, and using the result in a ComfyUI workflow.
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Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node. Render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video completes but looks wrong, such as artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, or a two-stage refiner or upscale degrading the result. For errors/OOM/missing nodes use the troubleshooting skill.
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Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a contact sheet. Covers the "washed out" signature, why reference color-match (mkl/ColorMatch/ColorMatchAdobe) CAN'T add contrast a flat source lacks, the levels/contrast-stretch fix (core AdjustContrast / CurveEditor), the measure→fix→re-measure loop, the side-by-side sandbox pattern, and where to place the fix in a render graph (after decode, before save). Use when a render looks washed out / flat / dull / over-saturated / color-cast, or when deciding between a color-match and a contrast/levels fix.
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Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.
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Full production pipeline covering story to scenes, Z-Image start frames, Qwen Edit end frames, WAN FLF video clips, ffmpeg concatenation
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Generate text, images, video, and audio, and make typed decisions from the terminal.
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。
Ship a Drizzle schema change end to end — generate the migration, merge, hand Matt the migrate command, redeploy apps/remote, confirm it is green. Use whenever a change adds or edits a file under packages/core/db/migrations/ or changes packages/core/db/schema.ts, or when an apps/remote deploy failed with "production has not applied".
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End-to-end implementation workflow. Use when user wants to implement a feature, fix a bug, or make changes and have everything validated and committed.
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Break a PRD into native GitHub sub-issues attached to the parent PRD. Project-local variant of /to-issues, adapted for this repo's PRD-as-parent + native sub-issues + agent:implement multi-session workflow. Argument is the parent PRD issue number.
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Install @effect/* packages safely. Use whenever adding a new Effect ecosystem dependency.
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Survey the codebase, pick ONE high-leverage deepening opportunity (filtering out anything already proposed), and publish it as a PRD-shaped GitHub issue via /to-prd-project. Designed to run unattended in the daily architecture-review workflow.
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Turn the current conversation context into a PRD and publish it as a GitHub issue that is ready to receive sub-issues from /to-issues-project. Project-local variant of /to-prd, adapted for this repo's PRD-as-parent-issue + sub-issues + agent:implement workflow.
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Generate or update documentation for the AI Hero API by reading the source code at ~/repos/ai/course-builder. Use when user wants to document, update docs for, or understand the AI Hero API endpoints.
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Designing for visionOS — spatial layout and ergonomics (60pt eye targets, field-of-view placement, dynamic scale), eyes-and-hands input with hover rules, motion and visual comfort (the 0.2 Hz rule, vection, depth-cue agreement), immersion strategy, spatial sound, and the video-format decision guide. Use when designing or reviewing any visionOS app, window, volume, or immersive experience.
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Generates a watermark overlay system for images and video with configurable positioning, opacity, tiling, and paywall integration for automatic removal on subscription or purchase. Use when user wants to add branding, attribution, or free-tier watermarks to visual content.
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Design or redesign frontend UI, presentations, and graphics on the Superdesign canvas with a choice of leading AI models. Use whenever the user wants to design a page, feature, flow, slide deck, or brand-new product; improve or reproduce existing UI; compare design results across top models; explore visual variants; set or extract a design system; build reusable components or multi-page flows; create presentations; or create posters and marketing graphics, even if they never say the word 'design tool'. Also supports generating supporting image or video assets when a design needs them.
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Render a short (~10s) video on any topic with Remotion - the agent writes a storyboard JSON, a bundled React/Remotion project renders it to an MP4, and the clip is committed to the repo and delivered by URL. No editor, no external generative API.
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Friday bulk football matchday simulations on MiroShark - pay $1 USDC per sim via x402 (Finance District agent wallet), launch Premier League, Serie A, and La Liga matchday sims in parallel, collect share links and full reports, and render or hand off one video per sim.
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Provides guidance for writing and benchmarking optimized CUDA kernels for NVIDIA GPUs (H100, A100, T4) targeting HuggingFace diffusers and transformers libraries. Kernels must be kernel-builder/ABI3-compliant: no pybind11, no setup.py, TORCH_LIBRARY_EXPAND bindings only. Supports models like LTX-Video, Stable Diffusion, LLaMA, Mistral, and Qwen. Includes integration with HuggingFace Kernels Hub (get_kernel) for loading pre-compiled kernels. Includes benchmarking scripts to compare kernel performance against baseline implementations.
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Create ad concepts that stop attention in the first 3 seconds. Trigger on "ad creative", "thumbstopper", "video ideas".
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Build a User Generated Content (UGC) strategy — getting customers to create content, review generation, UGC briefs for creators, social campaigns, contest mechanics, legal rights management, and repurposing UGC in paid ads. Use when the user says "UGC", "user generated content", "get customers to create content", "customer content", "UGC ads", "UGC creators", "organic UGC", "UGC strategy", "how do we get customers to post about us", "brand hashtag", "UGC brief", "influencer UGC", "UGC for ads", "customer photos and videos", or wants to use real customer content to build trust and reduce content production costs.
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Optimize an app's store listing for maximum visibility and downloads — keyword strategy, title and subtitle optimization, screenshots, preview videos, rating and review management, and A/B testing on the App Store (iOS) and Google Play (Android). Use when the user says "ASO", "app store optimization", "optimize my app listing", "improve app store ranking", "app store keywords", "app store screenshots", "app store rating", "Google Play optimization", "app visibility", "increase app downloads", "app store SEO", "app store conversion", "my app isn't getting downloads", or wants to improve how their app appears and converts in the App Store or Google Play.
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Writes the character-performance layer of a video prompt as behavior under pressure, not displayed emotion — objective, obstacle, tactics, beats, subtext, listening, body/status/proxemics, and mandatory eye life. Produces a reusable 150–220-word acting master profile per character plus a per-scene rewrite of it, and a locked voice prompt. Use whenever a prompt needs acting, performance, or emotion direction; whenever characters read wooden, dead-eyed, or 'AI'; whenever a character must stay themselves across many shots; or when the user asks for character behavior, mannerisms, tics, a gait, or how someone reacts. Pairs with higgsfield-facs (muscle-level AU codes) and higgsfield-seedance (the prompt the paragraph goes into).
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Use when the user wants to run a full ad-campaign pipeline on top of Higgsfield Marketing Studio — 'create a campaign', 'build a content plan', 'run the content pipeline', 'generate 100 UGC videos', 'plan and schedule a launch', 'make a batch of ads from my product', or 'how much did this save vs traditional production'. Covers the 5-stage orchestration (Research → Plan → Generate → Publish → Report), the UGC-first 5-format campaign mix (UGC Entertainment, Street Interview, Unboxing, Product Review, ASMR), the even-split allocation math, button-driven onboarding, the per-batch generation gate, and the publish + cost-report tail (satellite). Defers all Marketing Studio API ground-truth (presets, params, hooks, avatars) to higgsfield-marketing-studio.
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。