AnastasiyaW
UserClaude Code skills, architectural principles, and alternative approaches for AI-assisted development
Categories
Indexed Skills (27)
plan-swarm-review
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.
flux2-lora-training
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.
feature-new
Scaffold a new feature narrative document in an existing layer following the ULTRAPACK-style template extended for feature-layer architecture (principle 28). Creates docs/layers/<layer>/features/feat-NNN-<slug>.md with Design / Plan / Verify / Conclusion sections, populates layer README features table, and adds entry to feature_list.json if present. Use when: "create a new feature", "start work on feature", "scaffold feature doc", "/feature-new", "new feature in <layer>", "begin feature narrative". Auto-allocates next F-NNN ID.
harness-design
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.
layer-new
Scaffold a new layer in a project's docs/layers/ tree following the feature-layer architecture (principle 28). A layer is a bounded concern (security, data, ui, infrastructure, domain) with its own invariants, decisions, gotchas, patterns, and feature narratives. Use when: "create a new layer", "add security layer", "scaffold layer", "start tracking <concern> separately", "/layer-new", "add bounded concern". Operates on the kb-skeleton structure; idempotent -- will not overwrite existing layers.
pixel-art-storyboard
Convert short scene descriptions, book/album cover briefs, or 2-paragraph synopses into seamless-loop animated pixel-art covers rendered as self-contained HTML+canvas. Companion skill to pixel-art-studio. Use when the user asks to "make a cover for", "animated book cover", "looped pixel scene", "convert this story description to pixel art", "create ambient pixel animation", "обложка для книги в пиксель-арте", "анимированная обложка", "封面 像素画", "픽셀 아트 표지", or provides a short narrative/synopsis and wants a visual result. Covers: 5-element scene framework (Subject + Setting + Lighting + Palette + Motion), iconographic shorthand for symbolic accents, seamless loop techniques (phase-based parametric, sub-pixel breathing, LCM-clean parallax, deterministic particle systems), loop period selection by mood, three prompt registers (LLM agent / human artist / SDXL LoRA), single-HTML-file deliverable in dark-atmospheric style with parametrized canvas rendering. Generates the same engine pattern as the user's `Grass Fiel
pixel-art-studio
Create production-quality pixel art and animations programmatically. Use when the user asks to "create pixel art", "draw a sprite", "make pixel animation", "generate sprite sheet", "convert image to pixel art", "pixelate this image", "make a pixel character", "пиксель арт", "пиксельная графика", "спрайт", "像素画", "像素艺术", "도트 그래픽", "픽셀 아트", "8-bit/16-bit/hi-bit style", "retro game art", "Aseprite-like output", "indie game sprite". Covers single-frame sprites, frame-by-frame animations, walk cycles, idle/attack/death animations, sprite sheets, GIF/APNG export, image-to-pixel-art preprocessing (downsample + quantize + dither), 30+ bundled palettes (NES, GameBoy, PICO-8, Endesga 32/64, DawnBringer 16/32, Sweetie 16, Resurrect 64, Korean 오방색/단청, Chinese 故宫/青花/五行, Russian Stoneshard-inspired), 5 dithering algorithms (Bayer 2/4/8, Floyd-Steinberg, Atkinson, Ordered, Blue Noise), automated quality scoring (orphan pixels, doublies, banding, pillow-shading, AI-slop detection), and Generator-Evaluator review via the pixe
deep-review
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.
workflow-orchestration
Написание и запуск Claude Code dynamic workflows (детерминированный JS-оркестратор субагентов, research preview 2026-05-28). Use when пишешь или запускаешь workflow, видишь keyword workflow в запросе, нужен fan-out на десятки-сотни агентов, codebase-wide аудит/миграция, cross-checked research, competency-review, batch-обработка списка элементов через стадии. Покрывает: примитивы phase/agent/parallel/pipeline/workflow, pipeline vs parallel, schema, budget, resume, quality-паттерны (adversarial verify, judge panel, loop-until-dry), и наши добавки к платформе (retry-обёртка, error policy, .runs observability, eval-harness, billing-дисциплина). Триггеры: workflow, воркфлоу, оркестратор, fan-out, ultracode, deep-research, 1000 агентов, детерминированный скрипт агентов.
harness-audit
Score a project's agent harness across 5 subsystems (Instructions / State / Verification / Scope / Lifecycle), identify the bottleneck, and produce a prioritized improvement plan. Use when assessing if a project is ready to graduate to [LONG-RUN] status, when an agent keeps failing despite good models, or when adopting our stack on a new codebase.
diffusion-engineering
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.
flux2-klein-prompting
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.
forensic-prompt-compiler
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.
vlm-segmentation-engineering
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.
repo-map
Ranked symbol map of a codebase within a token budget — a compact "what matters in this repo" before reading files. Use when starting work in an unfamiliar/large codebase, before a refactor or deep-review fan-out, when you need JIT context instead of dumping whole files, or asked "give me a map of this repo / where are the important functions / what's the structure". Zero-dependency (stdlib only); faithful reimplementation of Aider's repo-map (tree-sitter+PageRank → ranked tags).
frontend-design
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.
ios-development
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.
desktop-sessions-discovery
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.
gemini-delegate
Делегирование задач в Gemini CLI (несколько OAuth-аккаунтов, свитчер, квоты, передача контекста). Use when - спроси/делегируй gemini, second opinion от другого вендора, bulk-курация картинок/данных, нужен 1M-контекст на чтение, квота gemini выгорела (TerminalQuotaError), переключи gemini аккаунт, прогнать N задач через gemini пока Claude занят.
article-structure-review
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.
english-text-humanization
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.
russian-text-humanization
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.
product-meaning-extractor
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-production-guide
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.
script-evaluator
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.
video-narrative-arc
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
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.
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.