claude-code-config

Практическая инженерия диффузионных моделей: архитектуры, обучение, инференс, оптимизация памяти. Использовать при любых задачах с диффузионными моделями: проектирование или модификация архитектуры (UNet/DiT/Flow/Flux), выбор и настройка schedulers/samplers, дообучение (LoRA/DreamBooth/full fine-tune), оптимизация памяти (AMP/checkpointing/ZeRO/FSDP/quantization), замена или fusion текст-энкодеров (CLIP/Qwen), работа с Diffusers, отладка диффузионных пайплайнов, оценка качества (FID/CLIPScore/LPIPS), latent diffusion, VAE, guidance/CFG, rectified flow, Stable Diffusion, SDXL, Flux. Также применять при вопросах про GPU-память при обучении генеративных моделей, text-to-image пайплайны, ControlNet, multi-encoder fusion, WebDataset.

Expert prompt engineering for FLUX.2 [klein] image generation and editing model. Use this skill whenever the user wants to create prompts for FLUX.2 [klein], generate images, edit photos with the klein model, work with multi-reference image editing, or needs templates for T2I/I2I tasks. Trigger for any mention of: FLUX.2, flux klein, BFL API, image editing prompts, text-to-image prompts for FLUX, product mockups, poster generation, UI mockups, sticker packs, character design, seamless textures, or any request to write/improve/translate prompts for FLUX-family models. Also trigger when user asks about guidance_scale, inference steps, distilled vs base modes, or multi-reference workflows.

Comprehensive reference for training LoRAs on FLUX.2 Klein 9B and Qwen Image Edit 2511 models. Use this skill whenever the user asks about: training LoRAs for flux2/flux 2 klein/qwen-image-edit, before/after edit LoRAs (head swap, face swap, image editing), inpainting LoRAs, training at larger resolutions, latent space expansion, VAE fine-tuning, multi-reference training (2 input images → 1 output), dataset preparation for edit models, zero_cond_t, ai-toolkit/SimpleTuner/DiffSynth configs, BFS head swap LoRA methodology, Qwen Edit architecture, consistency mode, dual encoding, FuseAnyPart, ACE++, maximum training resolution, или любые вопросы об обучении диффузионных моделей. ВСЕГДА используй этот скилл.

Forensic image-to-prompt compiler for image generation models. Use this skill whenever the user wants to: convert/describe an existing image into a generation prompt, reconstruct a scene as a prompt, generate prompts from reference images for AI image tools (Midjourney, FLUX, Stable Diffusion, DALL-E, or any diffusion model), write prompts that preserve exact visual properties of a source image, or needs precise control over identity-safe subject description, geometry lock, lighting reconstruction, color anchoring, or handler-based special cases (floating scenes, collages, close-ups, jewelry, garments, surreal elements). Also trigger for requests involving: image editing prompts, reference-driven generation, pose description, camera angle locking, fabric/material description, or any "turn this image into a prompt" task.

Экспертный скилл по прикладной инженерии VLM, сегментационных моделей и диффузионных архитектур для GPU-деплоя. Используй ВСЕГДА когда речь идёт о: SAM2, SAM3, Florence-2, LLaVA, Grounding DINO, OWLv2, YOLO-World, EdgeTAM — выбор модели, интеграция, pipeline, код; диффузионных моделях — UNet/DiT/Flow/Flux, schedulers, LoRA, AMP, ZeRO/FSDP, text encoders (CLIP/Qwen), VAE, CFG; GPU-деплое — MIG, MPS, torch.compile, TorchAO, Triton, memory optimization, два инстанса на H100; open-vocab сегментации и phrase grounding; part-level labeling и instance masks из текстового промпта; замене/fusion текст-энкодеров; fine-tune/LoRA/DreamBooth диффузионных моделей. Триггеры: SAM, Florence, LLaVA, Grounding DINO, YOLO-World, diffusion, UNet, DiT, Flux, LoRA, scheduler, guidance_scale, VAE, CLIP embeddings, Qwen embedder, MIG, MPS, TorchAO, Triton inference, сегментация по тексту, instance masks, open-vocab detection, text-conditioned segmentation.

Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.

Iterative plan review using multisampling + focused decomposition. Launches parallel independent agents to find issues that single-pass review misses. 4 escalating rounds: broad -> multisample -> focused -> focused+multisample. Use when: "swarm review", "review plan thoroughly", "multisample review", "deep plan review", "plan swarming", "stress test the plan", or before implementing any plan >500 lines or with >3 interacting components. Also use proactively when a large plan is about to be implemented — catch issues before code, not after.

Parallel competency-based code review. Launches independent Agent reviewers per competency (security, performance, architecture, database, concurrency, error-handling, frontend, testing), each with a focused checklist and isolated context. Synthesizes findings into unified report with FIX/DEFER/ACCEPT triage. Use when: "deep review", "thorough review", "parallel review", "review by competency", "full code review", or for large diffs (200+ lines) where /review may be too shallow. Complements /review (pre-landing) — this is for deep dives.

Plan-based verification: freeze acceptance criteria BEFORE building, verify AFTER with independent agents.

Создание высококачественных, визуально выдающихся фронтенд-интерфейсов. Используй ВСЕГДА когда пользователь просит создать веб-страницу, компонент, лендинг, дашборд, UI-кит, форму, карточки, навигацию, анимации, или любой другой веб-интерфейс. Скилл покрывает: HTML/CSS/JS компоненты, React/Vue/Svelte, Tailwind CSS, адаптивный и мобильный дизайн, визуальные стили (glassmorphism, neomorphism, material, flat, градиенты, тёмная тема), интерактивность (drag-and-drop, анимации, hover-эффекты, transitions), верстку (Flexbox, Grid, Container Queries), производительность, доступность (WCAG/ARIA), дизайн-системы и токены. Если пользователь хочет что-то "красивое", "современное", "стильное" в вебе — обязательно используй этот скилл.

Comprehensive iOS app development skill. Use this skill for ANY iOS-related task: writing Swift/SwiftUI/UIKit code, architecting apps, debugging crashes, setting up navigation, networking, data persistence, animations, performance optimization, App Store submission, Xcode configuration. Trigger when user mentions: iOS, Swift, SwiftUI, UIKit, Xcode, iPhone/iPad app, Combine, CoreData, SwiftData, MVVM, TCA, URLSession, async/await, @State/@Binding/@ObservableObject, NavigationStack, XCTest, TestFlight, provisioning profiles, or any Apple platform development. Always use this skill before writing iOS code or architecture.

Discover, search, and selectively restore Claude desktop app sessions hidden across multiple accountIds. Use when user mentions "missing sessions after account switch", "lost desktop sessions", "where do my old sessions live", or runs multiple Claude accounts on the same machine.

Deep product analysis before creating videos, presentations, or ads. Use when: 'analyze product', 'extract value', 'product brief', 'what makes this product special', 'prepare brief', 'understand the product', 'video brief'. Takes a URL or product description and outputs a structured brief with core insight, enemy, transformation, proof, mechanism, and emotional hooks. Based on JTBD, StoryBrand, Obviously Awesome (April Dunford), and Value Proposition Canvas frameworks.

Remotion (React video framework) production guide with Apple-style design rules. Use when: 'create video with remotion', 'remotion project', 'render video', 'product demo video', 'animated video', 'video from code'. Covers project setup, animation library, spring presets, typography rules, color palettes, pacing tables, scene templates, 3D integration, and export settings for all platforms.

Evaluate video scripts and presentations for flatness, tension, and emotional impact. Use when: 'is this script good', 'review script', 'evaluate video', 'why is this boring', 'flatness check', 'script review', 'improve script', 'rate this video'. Scores 6 dimensions (tension, specificity, emotional arc, hook, customer voice, visual variety), identifies specific problems, and suggests concrete fixes with examples.

Proven narrative arc templates for product videos and ads. Use when: 'write script', 'video structure', 'narrative arc', 'scene plan', 'storyboard', '15 second video', '30 second video', '60 second video', 'how to structure the video', 'video script template'. Provides beat-by-beat templates with timing, emotional arc mapping, hook formulas, and pacing rules. Covers 10s-90s formats for social, product demos, launches, and pitches.

Video post-production rules: audio mastering, color, captions, platform export. Use when: 'add music', 'add voiceover', 'export for tiktok', 'add captions', 'color grade', 'audio levels', 'master audio', 'export settings', 'platform requirements'. Covers FFmpeg patterns, audio chain, subtitle standards, and platform-specific export configs.

Структурный self-review технической статьи перед публикацией. Покрывает три дыры, которые не ловятся точечными скиллами типа humanize/infostyle: thesis/proof balance, жанровая чистота, обязательный блок ограничений. Применяется ПОСЛЕ написания первого черновика, ПЕРЕД humanize + infostyle. Основано на фидбеке реальных читателей на опубликованные статьи - классический паттерн "много тезисов / мало доказательств" и отсутствие честного блока про то, что не решено. Use AFTER first draft is done, BEFORE word-level audits.

Make AI-generated English text sound natural and human. Use when: writing blog posts, articles, marketing copy, any English content that must not read as AI-generated. Covers: burstiness, perplexity, banned words, sentence patterns, transitions, tone. Based on: Liang et al. (arxiv 2406.07016, 15M+ abstracts), GPTZero/Originality research. Use BEFORE publishing any AI-generated English text.

Натурализация русскоязычного текста - убрать маркеры ИИ-генерации. Использовать когда: Хабр-статьи, блоги, маркетинг, любой русский текст который не должен читаться как ИИ. Покрывает: слова-маркеры, калькирование, канцелярит, ритм, отглагольные существительные. Источники: Liang et al. (arxiv 2406.07016), gramota.ru, Хабр 918226, Sber GigaCheck. Use BEFORE publishing any AI-generated Russian text.