「recipes」の検索結果

Author or revise a cloud-api-operations recipe (config/source/skills/cloud-api-operations/references/recipes/). Encodes why the long tail of control-plane capabilities is deliberately NOT wrapped as MCP/CLI tools, the API-first admission test for whether a capability deserves a recipe, the negative and positive lists, the fixed recipe structure, the writing rules distilled from writing-for-agents, the user-facing copy red lines, and the product regeneration/verification pipeline. Also routes contributors (and their agents) to the contribution flow and the pre-PR self-test protocol in references/contributing.md. Use when adding a recipe, judging whether a scenario belongs in the recipe set, updating the recipes index, re-checking existing recipes against the admission test, or onboarding someone who wants to submit one.

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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.

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Select, validate, patch, and deploy existing NVIDIA Dynamo Kubernetes recipes. Use for model/backend/GPU/deployment-mode recipe bring-up; use router-starter for router-only mode work and troubleshoot for broken deployments.

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。

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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。

Use when the user's video is on disk - VidSeeds MCP probe, frame/clip extraction, ffmpeg recipes, precision trim, and midroll analysis. Hosted tools return commands/recipes to run locally; media stays on the user's machine.

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Use when the user wants a genre-specific scene template, starting point for a type of video (action, horror, romance, product ad, documentary, sci-fi, dance, etc.), or asks for prompt examples for a specific style of content.

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Write correct coroutine scopes, lifecycle collection, and dispatcher injection in Android production code. Use for suspend functions, coroutine scopes, and dispatcher mechanics; defer ViewModel StateFlow/LiveData architecture, Fragment lifecycle recipes, persistence/notifications, and unit-test recipes to their specific skills.

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omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.

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Curated collection of ready-to-use Claude Code hook recipes for safety, automation, formatting, logging, and context injection. Use when configuring hooks, setting up safety guards, automating workflows, or when user mentions 'hooks', 'PreToolUse', 'PostToolUse', 'guard', 'automation', 'claude hooks'.

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開発の目的に合うECCのコマンド群を選び、実行順序と停止条件を案内します。現在のファイルを基に一覧も整理し、複数コマンドの使い分けを助けます。

• バグ修正のコマンド順序を知りたいとき
• MVP開発のコマンドを選びたいとき
• ECCの作業手順を一覧で見たいとき

Set up, review, debug, or validate an Orca per-workspace environment recipe: the on-demand, disposable runtime (cloud sandbox, VM, SSH host, or local container) Orca creates fresh for each workspace. Use to stand up a new recipe end to end, fix an `environmentRecipes` entry in `orca.yaml`, scaffold provider lifecycle scripts, or resolve an `orca vm recipe doctor` failure. Use `orca-cli` for ordinary worktree and workspace creation with no recipe involved.

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Use when applies to product pages, local business pages, recipes, apps, books, and any page that aggregates user reviews. Use when auditing rich result eligibility for e-commerce or review sites.

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Use when working with a person on a HyperFrames project in Studio: first, whether their message asks for a change at all (questions, loose ideas and "don't change anything" get an answer and a plan, not an edit); for a new film, the plan, storyboard and build order that the HyperFrames launch films follow; and how the timeline should be laid out so it reads well (one caption track, one element kind per track, every scene a sub-composition) and where captions and key content may sit (safe zones). Don't use for how to perform an individual edit (split, trim, retime, volume, copy, swap): that is `creator-editing-recipes.md` in `/hyperframes-core`.

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Analyzes user-requested Screenpipe history windows to detect repeated research workflows, match existing scientific skills, and stage new skill drafts or composition recipes for review. Requires a reachable Screenpipe HTTP API, normally on localhost:3030. Detection and embedding inference run locally; the selected LLM receives redacted app/title cluster summaries and matched skill descriptions. Use only when the user explicitly asks to analyze their recent work and propose skills.

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Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, choosing rank/alpha/target modules, or deciding between LoRA, QLoRA, and full fine-tuning.

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Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.

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Triage OpenSEO production errors in Cloudflare Workers Observability — verified query recipes, counting gotchas, and a known-noise filter list applied automatically. Use when asked to review Cloudflare logs/observability, count OOMs or worker errors, compare error rates between periods, or investigate a prod error spike.

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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

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Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/stas00/ml-engineering/blob/master/skills/ml-engineering/SKILL.md

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Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

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Authoring guide for the Native markup (.native) view tier of native-rendered Native SDK apps. TypeScript cores are the default and use this skill together with ts-core; Zig cores are an explicit alternative and this guide labels their lower-level UiApp recipes. Use when building or modifying native UI (widgets, layout, bindings, messages), writing .native files, wiring views to Model/Msg/update, testing markup views, or verifying a native app through the automation harness.

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Create or edit the active iPolloWork video's visual assets, installed recipes, editable composition and seek-safe motion; use for assembly or local picture/text/theme changes.

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Turn any ordered process into a data-driven vertical or horizontal scroll story with a base line, progress fill, active step states, responsive collapse, semantic fallback, and reduced-motion behavior. Use for onboarding, checkout, roadmaps, recipes, case studies, service processes, histories, or narratives where progress through the sequence should become visible while scrolling.

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。