AnastasiyaW
UserClaude Code, Codex, and multi-agent configuration system: principles, hooks, skills, and workflow patterns for AI-assisted development
Categories
Indexed Skills (84)
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. Do NOT use to design a multi-agent harness or Generator-Evaluator architecture from scratch; use harness-design for that. Do NOT use to review already-written code/diffs; use deep-review for that (this reviews plans, not implementations).
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. Do NOT use to design a multi-agent harness or Generator-Evaluator architecture from scratch; use harness-design for that. Do NOT use to review already-written code/diffs; use deep-review for that (this reviews plans, not implementations).
thermo-nuclear-code-quality-review
Run an opt-in strict maintainability review for giant files, spaghetti growth, misplaced logic, weak boundaries, unnecessary abstractions, and missed structural simplifications. Use for a thermonuclear review, harsh code-quality audit, code-judo review, or a file approaching 1000 lines. Do not use as an automatic rewrite mandate or for cosmetic cleanup.
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. Do NOT use just to orient in an unfamiliar codebase or get a structural symbol overview; use repo-map for that (this audits a concrete diff for defects, it is not a navigation map).
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. Do NOT use to design the agent/Generator-Evaluator architecture itself (use harness-design) or for a single one-shot subagent/review where no deterministic multi-stage script is needed; this writes the JS orchestrator, it is not for ad-hoc one-off a
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. Do NOT use for writing FLUX.2 Klein generation/edit prompts at inference time (use flux2-klein-prompting), nor for general non-training diffusion architecture/inference/memory work (use diffusion-engineering); this skill is about LoRA/VAE training, not prompting or serving.
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. Do NOT use to create the layer itself or its bounded-concern KB scaffold; use /layer-new for that (a feature lives inside an already-existing layer).
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. Do NOT use to stress-test or critique an already-written plan document; use plan-swarm-review for that (this skill designs the harness, it does not review plans).
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. Do NOT use to scaffold an individual feature narrative inside an existing layer; use /feature-new for that (a layer is the container, not the per-feature doc).
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
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. Do NOT use to design or build a new harness from scratch — this only scores an existing one; for greenfield harness/agent architecture use harness-design (or agent-harness-design).
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. Do NOT use for writing FLUX.2 Klein generation/edit prompts at inference time (use flux2-klein-prompting), nor for general non-training diffusion architecture/inference/memory work (use diffusion-engineering); this skill is about LoRA/VAE training, not prompting or serving.
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. Do NOT use to create the layer itself or its bounded-concern KB scaffold; use /layer-new for that (a feature lives inside an already-existing layer).
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. Do NOT use to stress-test or critique an already-written plan document; use plan-swarm-review for that (this skill designs the harness, it does not review plans).
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. Do NOT use to scaffold an individual feature narrative inside an existing layer; use /feature-new for that (a layer is the container, not the per-feature doc).
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. Do NOT use just to orient in an unfamiliar codebase or get a structural symbol overview; use repo-map for that (this audits a concrete diff for defects, it is not a navigation map).
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. Do NOT use to design the agent/Generator-Evaluator architecture itself (use harness-design) or for a single one-shot subagent/review where no deterministic multi-stage script is needed; this writes the JS orchestrator, it is not for ad-hoc one-off a
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. Do NOT use to design or build a new harness from scratch — this only scores an existing one; for greenfield harness/agent architecture use harness-design (or agent-harness-design).
remote-compute-ops
Operate GPU and remote compute across RunPod (Pods and Serverless), Massed Compute VMs, and owned or virtual remote servers through existing bridges, SSH sessions, MCP/API adapters, bounded polling, cost controls, and resumable lifecycle checks. Use when the user mentions RunPod, Massed Compute, a remote GPU/server/VM, SSH bridge/tunnel/bastion/Tailscale, training or inference on rented compute, GPU inventory, billing, or asks to minimize API/SSH connections and avoid rate limits. Do not use for generic cloud architecture, local-only GPU work, or application code with no remote-resource operation.
architecture-first
Decide the shape BEFORE the first file, and keep the boundaries honest afterwards: what the modules are, which way dependencies point, where state is owned, and what each module is allowed to know. Merges the layering rules (dependency rule, SOLID, component cohesion, Humble Object, entities vs use cases, frameworks-and-DB-as-details) with domain boundaries (ubiquitous language, bounded contexts, aggregates, domain events, repositories). Use when starting a project, service, site, API or new subsystem; when adding a feature that does not obviously belong to an existing module; when asked "where should this live", "how do we structure this", "what are the modules"; when writing an ARCHITECTURE.md or an ADR; when a dependency points the wrong way or a circular import appears. Do NOT use for a one-file script or a throwaway experiment, for a bug fix inside an existing seam, for word-level naming and function shape (use code-complexity), for splitting a module that is ALREADY too large (use refactoring-safely), o
code-complexity
Keep each unit comprehensible while the code grows: deep modules over shallow ones, information hiding, honest names, small functions with one job, error handling that does not lose the error, DRY and orthogonality, design by contract, and not leaving broken windows. Merges module-complexity management with naming/function/error-handling discipline and the pragmatic meta-rules. Use when writing or reviewing a function, class or module; when an interface feels wide, a class feels shallow, or a change in one place forces edits in three; when the user says "this is hard to follow", "clean this up", "review this code", "is this a good abstraction", "too many parameters", "pass-through method", "duplicated logic", or asks about naming, comments, error handling or unit tests. Do NOT use to decide where a new module LIVES or which modules exist (use architecture-first), to plan capacity or storage (use system-and-data-design), to execute a named transformation on a file that is already too large (use refactoring-saf
harness-feedback
Use when an agent says a test, VM, proof, evaluator, or release gate is overloaded, too strict, blocking staging, or causing false positives; split checks by profile, measure the burden, preserve high-risk evidence, and verify the smallest corrected workflow. Do not use for ordinary test selection, a single test failure, or a full security audit without a harness-scope question.
refactoring-safely
Change the structure of code that already exists without changing what it does: smells as triggers, the named transformations (extract/inline, move feature, organise data, simplify conditionals), and above all the workflow that makes it safe — characterization tests first, one transformation at a time, green between every step. Use when a file or function is already too large; when asked to "split this module", "extract this", "break up main.py", "clean up this legacy code", "reduce coupling here"; when a shape advisory fires on a grown file; or before any restructuring of code that has users. Do NOT use to decide the target layout of a NEW project (use architecture-first), for unit-level naming and function quality in code you are writing fresh (use code-complexity), for capacity or storage decisions (use system-and-data-design), or to strip over-engineering on request (use lean-code). This is the transformation with a net; deciding WHERE things should end up is a different question, and doing both at once i
system-and-data-design
Decide whether the system will hold, and where the data lives: requirements and load first, then back-of-the-envelope numbers, building blocks (cache, queue, load balancer, CDN), and the data layer in depth — storage engines, indexes, replication, partitioning, transactions and consistency, batch vs stream. Use when sizing or scaling anything; choosing a database, cache, queue or index; when asked "will this hold", "how many machines", "which database", "do we need a queue", "read replica", "sharding", "eventual consistency", "why is this query slow at scale"; when designing an ingestion or processing pipeline; or when a service is slow under load rather than wrong. Do NOT use for module layout, dependency direction or domain boundaries (use architecture-first), for function- and naming-level quality (use code-complexity), for restructuring code that is already too large (use refactoring-safely), or for a low-traffic internal tool where the honest answer is one process and one database.
testing-strategy
Use when planning or reviewing tests for a code change, choosing between unit, focused regression, integration, contract, end-to-end, performance, security, property-based, or agent-evaluation checks; classify change risk first and select the smallest evidence set that proves the behavior. Do not use for a single obvious test command, pure documentation changes, or a full security audit without a testing question.
architecture-quality
Keep web applications, APIs and services readable as they grow: choose feature or domain seams, assign state ownership, enforce dependency direction, keep adapters thin, and verify file shape. Use when starting or extending a web app, backend, frontend, API or multi-page product; when a change makes a module hard to read; when architecture review finds a god file, cross-feature imports, a circular dependency or a framework-heavy domain. Load architecture-first first for a new system, and refactoring-safely for an existing oversized module. Do not use for a one-file script, throwaway spike or a purely local naming change.
control-cli
Drive and inspect an interactive CLI or TUI with a repeatable local harness, deterministic input, transcripts, and optional profiling. Use for CLI UX checks, prompt flows, startup regressions, hangs, interrupts, resize behavior, or memory growth. Do not use for a non-interactive command that a normal test can cover.
control-ui
Drive and inspect a local web, IDE, or Electron UI with browser or CDP automation and evidence. Use for UI regressions, screenshots, accessibility snapshots, visual diffs, focus, keyboard, scrolling, resize, console, network, performance, or heap checks. Do not use for backend-only changes or for a screenshot without a behavior claim.
deslop
Remove AI-generated code noise from the current diff while preserving behavior. Use for deslop, AI slop cleanup, unnecessary comments, defensive checks, any casts, or needless nesting after an agent-generated change. Do not use as permission for a broad rewrite or when a real bug has not been isolated.
verify-this
Prove a concrete behavior, performance, UI, CLI, API, or memory claim with fresh baseline-versus-treatment evidence and one explicit verdict. Use when asked to verify, prove, compare before and after, show evidence, or confirm that a fix works. Do not use for vague claims such as cleaner code, a full plan-based release verification, or a known bug that needs a red-to-green reproducer.
massed-compute-ops
Operate Massed Compute GPU virtual machines through its MCP server. Use when the user mentions Massed Compute, massedcompute, MassedCompute, asks what GPU is available, wants to choose or launch training/inference VMs, inspect running instances, configure SSH access, audit hourly spend or billing runway, restart a VM, or terminate rented compute safely.
proof-verify
Plan-based verification - freeze acceptance criteria before building, then verify after with an independent fresh-context agent (the builder must not verify their own work). Use when - "verify against plan", "proof check", "independent review", "check the implementation", or confirming a feature built from a plan meets spec. Do NOT use for quick one-off checks with no plan, or for letting the builder self-verify.
cross-harness-continuation
Use when work moves between Claude Code and Codex, a session resumes from a handoff, or an agent may rewrite an existing implementation. Loads the shared CONTINUITY.json contract, preserves accepted decisions, checks Git baseline and scope, and requires explicit replan mode for intentional redesign. Do NOT use for fresh work with no prior agent's decisions to preserve, for a single-harness session that no one else is continuing, or as a general handoff-writing helper (see the session-handoff rule); this skill governs the contract between agents, not the notes one agent leaves itself.
agent-harness-design
Designing agent harnesses and tool systems — risk taxonomy for tools, permission decisions, draft/commit pattern, structured tool results, agent budgets (10 types), context trust labels against prompt injection, plan-artifact, approval records, observability and traces, evals (13 categories), event model, streaming buffering, 3rd-party skill install checklist, agentic RAG, self-improving SOP loops, model policy, reasoning effort, and Programmatic Tool Calling adoption gates. Use when building a new Agent SDK app, custom orchestrator, MCP server, Cloudflare Worker with tool calls, agentic RAG pipeline, model router, or model-tier policy; when designing tools and permissions; when writing an agent loop; or when you need trust labels for external content. Do NOT use for improving or auditing an already-built harness (use harness-audit / harness-design instead), nor for ordinary Claude Code sessions where the harness is already given.
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. Do NOT use for writing FLUX.2 Klein generation prompts (use flux2-klein-prompting) или для FLUX.2 Klein / Qwen-Edit LoRA-тренинга и edit-LoRA (use flux2-lora-training); это общая диффузионная инженерия, не специфика FLUX.2 Klein.
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. Do NOT use for training a FLUX.2 Klein / Qwen-Edit LoRA (use flux2-lora-training), nor for reconstructing a prompt FROM an existing source image (use forensic-prompt-compiler); this skill is for authoring generation/edit prompts only.
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. Do NOT use for composing prompts from scratch with no source image (use flux2-klein-prompting), nor for training a LoRA on edit pairs (use flux2-lora-training); this skill reconstructs a promp
ml-research-lab
Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. Use when working on ML experiments, training data, model benchmarks, RunPod/GPU runs, classifier quality, vLLM/GGUF serving, SHAP-style model explanations, or research-to-code iterations. Do not use for a simple code edit that has no ML dataset, metric, model, or experiment artifact.
notebooklm-grounded-research
Use when: NotebookLM, notebooklm MCP, large documentation sets, courses, books, papers, or citation-backed research are mentioned. Retrieves a small grounded answer from a stable corpus, preserves citations, and verifies claims against primary documentation, repository code, and tests. Do not use when: the answer is already in a small local file, the source is rapidly changing, or a live runtime/test is the authority.
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. Do NOT use for pure diffusion-only work without a VLM/segmentation component —
distill-feedback
Turn captured user-correction signals into durable rules (learn-from-corrections loop). Use when - /distill-feedback, "process feedback queue", "what corrections did I give you", "encode lessons from my corrections", session-feedback-capture queued sessions, "обнови правила по моим поправкам", "разбери очередь обратной связи". Reads ~/.claude/feedback/queue.jsonl, LLM-semantically detects durable corrections, proposes atomic rules, applies human-gated via delta-merge. Do NOT use to act on a single in-session correction (just apply the fix directly) or to hand-edit settings.json behaviors; this only mines the queued feedback backlog into durable rules.
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). Do NOT use to find correctness/security defects in a change or to audit a diff; use deep-review for that (this only ranks and lists symbols, it does not evaluate code quality).
frontend-design
Создание высококач��ственных, 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. НЕ используй для нативных мобильных приложений (iOS/Swift/SwiftUI, Android) — для iOS бери скилл ios-development; этот скилл только про веб-UI (браузер).
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. Do NOT use for web frontends (HTML/CSS/JS, React/Vue, browser UI) — even a WebView's page content is web work; use frontend-design for that. This skill is native Apple-platform code only.
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. Do NOT use for Claude Code CLI session history or resuming work within one session — this only recovers the native desktop app's per-accountId session files; for in-session continuity use handoffs.
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. НЕ используй для делегирования в OpenAI Codex (другой вендор/CLI — это ск��лл codex) и не передавай секреты во внешний LLM.
observability-monitoring
Design, audit, and troubleshoot production monitoring and observability using user-impact checks, layered telemetry, USE/RED, SLI/SLO/SLA, error budgets, cardinality controls, actionable alerting, burn-rate response, and postmortems. Use when asked about monitoring, наблюдаемость, алерты, Prometheus, Grafana, OpenTelemetry, logs, traces, profiles, service health, or incident evidence. Do not use for generic dashboard styling, frontend-only UI work, or unrelated code review.
plan-to-tickets
Use when a large plan, PRD, feature, refactor, research plan, or multi-step coding task must be split into small ready-for-agent tickets with acceptance criteria, verification commands, blockers, and vertical tracer-bullet slices. Do not use for small tasks that should be implemented directly, single-bug fixes, or chat-only summaries.
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. Do NOT use for writing the script or scene timing (use video-narrative-arc), scoring an existing script (use script-evaluator), or rendering video (use remotion-production-guide); this is the upstream brief-only step before any script is written.
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. Do NOT use to write the script or narrative beats (use video-narrative-arc), to critique a script's impact (use script-evaluator), or for FFmpeg audio mastering/captions/platform color-grade on a finished render (use video-post-production); this stage builds and renders scenes in React/code.
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. Do NOT use to generate a new script or scene structure from scratch (use video-narrative-arc), to build the product brief (use product-meaning-extractor), or to render/finish the video (use remotion-production-guide or video-post-production); this only critiques an existing script/scene.
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. Do NOT use to judge or score an already-written script (use script-evaluator), to derive the product insight/brief from scratch (use product-meaning-extractor), or to render the video in code (use remotion-production-guide); this stage only produces the structured script/beats.
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. Do NOT use to generate or animate the video frames/scenes in code (use remotion-production-guide), to write the script (use video-narrative-arc), or to evaluate script quality (use script-evaluator); this is the finishing stage applied to an already-rendered video.
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. Do NOT use for word-level / sentence-level style fixes or removing AI-generation markers (use humanize-english or humanize-russian for that); this skill only reviews macro-structure (thesis/proof balance, genre purity, limitations block), not phrasing.
humanize-english
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. Do NOT use for Russian text (use humanize-russian) or for macro-level article structure such as thesis/proof balance or a limitations block (use article-structure-review); this skill operates at the word/sentence level on English only.
humanize-russian
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. Do NOT use for English text (use humanize-english) or for macro-level article structure such as thesis/proof balance or a limitations block (use article-structure-review); this skill operates at the word/sentence level on Russian only.
agent-harness-design
Designing agent harnesses and tool systems — risk taxonomy for tools, permission decisions, draft/commit pattern, structured tool results, agent budgets (10 types), context trust labels against prompt injection, plan-artifact, approval records, observability and traces, evals (13 categories), event model, streaming buffering, 3rd-party skill install checklist, agentic RAG, self-improving SOP loops, model policy, reasoning effort, and Programmatic Tool Calling adoption gates. Use when building a new Agent SDK app, custom orchestrator, MCP server, Cloudflare Worker with tool calls, agentic RAG pipeline, model router, or model-tier policy; when designing tools and permissions; when writing an agent loop; or when you need trust labels for external content. Do NOT use for improving or auditing an already-built harness (use harness-audit / harness-design instead), nor for ordinary Claude Code sessions where the harness is already given.
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. Do NOT use for writing FLUX.2 Klein generation prompts (use flux2-klein-prompting) или для FLUX.2 Klein / Qwen-Edit LoRA-тренинга и edit-LoRA (use flux2-lora-training); это общая диффузионная инженерия, не специфика FLUX.2 Klein.
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. Do NOT use for training a FLUX.2 Klein / Qwen-Edit LoRA (use flux2-lora-training), nor for reconstructing a prompt FROM an existing source image (use forensic-prompt-compiler); this skill is for authoring generation/edit prompts only.
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. Do NOT use for composing prompts from scratch with no source image (use flux2-klein-prompting), nor for training a LoRA on edit pairs (use flux2-lora-training); this skill reconstructs a promp
ml-research-lab
Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability. Use when working on ML experiments, training data, model benchmarks, RunPod/GPU runs, classifier quality, vLLM/GGUF serving, SHAP-style model explanations, or research-to-code iterations. Do not use for a simple code edit that has no ML dataset, metric, model, or experiment artifact.
notebooklm-grounded-research
Use when: NotebookLM, notebooklm MCP, large documentation sets, courses, books, papers, or citation-backed research are mentioned. Retrieves a small grounded answer from a stable corpus, preserves citations, and verifies claims against primary documentation, repository code, and tests. Do not use when: the answer is already in a small local file, the source is rapidly changing, or a live runtime/test is the authority.
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. Do NOT use for pure diffusion-only work without a VLM/segmentation component —
distill-feedback
Turn captured user-correction signals into durable rules (learn-from-corrections loop). Use when - /distill-feedback, "process feedback queue", "what corrections did I give you", "encode lessons from my corrections", session-feedback-capture queued sessions, "обнови правила по моим поправкам", "разбери очередь обратной связи". Reads ~/.claude/feedback/queue.jsonl, LLM-semantically detects durable corrections, proposes atomic rules, applies human-gated via delta-merge. Do NOT use to act on a single in-session correction (just apply the fix directly) or to hand-edit settings.json behaviors; this only mines the queued feedback backlog into durable rules.
proof-verify
Plan-based verification - freeze acceptance criteria before building, then verify after with an independent fresh-context agent (the builder must not verify their own work). Use when - "verify against plan", "proof check", "independent review", "check the implementation", or confirming a feature built from a plan meets spec. Do NOT use for quick one-off checks with no plan, or for letting the builder self-verify.
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). Do NOT use to find correctness/security defects in a change or to audit a diff; use deep-review for that (this only ranks and lists symbols, it does not evaluate code quality).
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. НЕ используй для нативных мобильных приложений (iOS/Swift/SwiftUI, Android) — для iOS бери скилл ios-development; этот скилл только про веб-UI (браузер).
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. Do NOT use for web frontends (HTML/CSS/JS, React/Vue, browser UI) — even a WebView's page content is web work; use frontend-design for that. This skill is native Apple-platform code only.
cross-harness-continuation
Use when work moves between Claude Code and Codex, a session resumes from a handoff, or an agent may rewrite an existing implementation. Loads the shared CONTINUITY.json contract, preserves accepted decisions, checks Git baseline and scope, and requires explicit replan mode for intentional redesign.
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. Do NOT use for Claude Code CLI session history or resuming work within one session — this only recovers the native desktop app's per-accountId session files; for in-session continuity use handoffs.
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. НЕ используй для делегирования в OpenAI Codex (другой вендор/CLI — это скилл codex) и не передавай секреты во внешний LLM.
observability-monitoring
Design, audit, and troubleshoot production monitoring and observability using user-impact checks, layered telemetry, USE/RED, SLI/SLO/SLA, error budgets, cardinality controls, actionable alerting, burn-rate response, and postmortems. Use when asked about monitoring, наблюдаемость, алерты, Prometheus, Grafana, OpenTelemetry, logs, traces, profiles, service health, or incident evidence. Do not use for generic dashboard styling, frontend-only UI work, or unrelated code review.
plan-to-tickets
Use when a large plan, PRD, feature, refactor, research plan, or multi-step coding task must be split into small ready-for-agent tickets with acceptance criteria, verification commands, blockers, and vertical tracer-bullet slices. Do not use for small tasks that should be implemented directly, single-bug fixes, or chat-only summaries.
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. Do NOT use for writing the script or scene timing (use video-narrative-arc), scoring an existing script (use script-evaluator), or rendering video (use remotion-production-guide); this is the upstream brief-only step before any script is written.
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. Do NOT use to write the script or narrative beats (use video-narrative-arc), to critique a script's impact (use script-evaluator), or for FFmpeg audio mastering/captions/platform color-grade on a finished render (use video-post-production); this stage builds and renders scenes in React/code.
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. Do NOT use to generate a new script or scene structure from scratch (use video-narrative-arc), to build the product brief (use product-meaning-extractor), or to render/finish the video (use remotion-production-guide or video-post-production); this only critiques an existing script/scene.
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. Do NOT use to judge or score an already-written script (use script-evaluator), to derive the product insight/brief from scratch (use product-meaning-extractor), or to render the video in code (use remotion-production-guide); this stage only produces the structured script/beats.
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. Do NOT use to generate or animate the video frames/scenes in code (use remotion-production-guide), to write the script (use video-narrative-arc), or to evaluate script quality (use script-evaluator); this is the finishing stage applied to an already-rendered video.
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 чер��овика, 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. Do NOT use for word-level / sentence-level style fixes or removing AI-generation markers (use humanize-english or humanize-russian for that); this skill only reviews macro-structure (thesis/proof balance, genre purity, limitations block), not phrasing.
humanize-english
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. Do NOT use for Russian text (use humanize-russian) or for macro-level article structure such as thesis/proof balance or a limitations block (use article-structure-review); this skill operates at the word/sentence level on English only.
humanize-russian
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. Do NOT use for English text (use humanize-english) or for macro-level article structure such as thesis/proof balance or a limitations block (use article-structure-review); this skill operates at the word/sentence level on Russian only.
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