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概要と使いどころ
Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.
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Think and work like an expert Cryptographer. Use when a task calls for Cryptographer judgment. Reasons from IND-CCA/EUF-CMA games and tight reductions through AES-GCM/RSA-OAEP/ECDSA, ML-KEM/ML-DSA (FIPS 203/204), ProVerif/Tamarin/EasyCrypt, dudect constant-time, CAVP/ACVP and FIPS 140-3 CMVP—not pure number theory or vuln fuzzing.
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Think and work like an expert Deep Learning Scientist. Use when a task calls for Deep Learning Scientist judgment. Reasons from CNN/Transformer inductive bias, Li et al. loss landscapes, grokking/mode connectivity, and Kaplan/Chinchilla scaling (~20 tokens/param); designs ResNet/ViT/DiT/MoE/FlashAttention stacks with FLOPs-matched ablations; trains AdamW+cosine/WSD via Megatron-FSDP/DeepSpeed; evaluates FID/MMLU-Pro/MMLU-CF with lm-eval decontamination and Pineau/NeurIPS reproducibility checklists.
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Think and work like an expert Animal Geneticist & Breeder. Use when a task calls for Animal Geneticist & Breeder judgment. Reasons from additive genetic variance, response to selection (R = i h sigma_A), accuracy, and inbreeding depression through REML/BLUP and ssGBLUP pipelines (BLUPF90, ASReml, WOMBAT), economic selection indices, optimum-contribution mate allocation, and Interbull MACE while treating confounded contemporary groups, prediction bias/dispersion, popular-sire inbreeding and rising deleterious haplotypes, GxE reranking, and pedigree or genotype-calling errors as first-class failure modes.
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Report which kasetto-installed skills and MCP servers are actually being used across the AI agents on this machine, and render a branded HTML dashboard of the result. Use whenever the user asks what agent assets they actually use, which skills or MCPs are dead weight, what to prune or clean up from kasetto.yaml, why their context is bloated with unused MCP servers, whether a skill has ever been invoked, or wants a usage report, audit, or dashboard of their agent setup. Also trigger on "kst ai assets usage", "ai asset usage", "what skills do I actually use", "which MCPs are worth keeping", "audit my agent assets", "am I using all these skills", or any request to review, trim, or justify an installed agent toolkit.
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。
Design DNA capture — extracts palette, type stack, motifs, motion, and 3D signatures from a shipped HTML file and saves them as a reusable reference pack. Use when the user says /reimagine-it lock, "lock this design", "save design DNA", "capture this look", or wants to reuse a shipped design on a different target. Locks capture palette, type, motif, and motion from a shipped HTML file. Part of the reimagine-it Content-Derived Design suite.
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。
Generate several distinct design directions from one HTML source plus a contrast sheet that shows every direction side by side. Use when the user says /reimagine-it variations, "show me options", "give me three takes", "which direction works best", or wants to compare design directions before choosing one. CLI: npx reimagine-it variations -i page.html -n 4 -o review/. Part of the reimagine-it Content-Derived Design suite.
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。
Design Health — runs 19 deterministic quality checks on HTML output. Use when the user says /reimagine-it audit, "audit this page", "check design quality", or wants to verify craft-floor compliance before shipping. Catches blank plates, missing focus rings, non-compositor motion, off-palette accents, dead motion, fabricated content. No LLM, no API key. CI-ready with --json and exit codes. Part of the reimagine-it Content-Derived Design suite.
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。
Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.
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。
Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges, timeline, tables and citations are LLM judgment), assemble (deterministic validation, template injection, Q50 pins, Q47 overflow gate), render + optimize (Chrome headless per-page PNGs — the LLM reads them and iterates until every page is visually clean). Spec 046 Q46–Q50.
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。
Papers With Code CLI (`pwc`) for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, frameworks, and benchmark leaderboards, and submitting authenticated paper edits through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, `pwc`, or `pwc-cli`.
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。
Papers With Code MCP tools for searching and reading AI/ML papers, discovering recent and trending research, finding related work and paper lineage, browsing tasks, methods, conferences, organizations, and frameworks, and inspecting benchmark leaderboards with model-size and metric filters through the public Papers With Code catalog. Use whenever the user asks to find papers, survey literature, compare research, inspect an arXiv paper, explore AI/ML taxonomy or conferences, discover benchmarks or state-of-the-art models, or mentions Papers With Code, the PwC MCP server, or paperswithcode.co/mcp.
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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
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。
Deep learning-based variant calling with Google DeepVariant. Provides high accuracy for germline SNPs and indels from Illumina, PacBio, and ONT data. Use when calling variants with DeepVariant deep learning caller or when highest germline calling accuracy is required.
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Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value assessment.
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Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML. Use when determining clinical significance, triangulating conflicting interpretations, or aggregating evidence against the ACMG/AMP framework with ClinGen SVI specifications.
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。
ローカルで検証済みのブラウザ向け HTML、CSS、JavaScript を CodePen Prefill launcher に変換し、承認後に未保存の Pen を開く。フロントエンドのデモを CodePen で共有して、記事へ埋め込む準備をして、と依頼されたときに使用する。Node.js、サーバー、CLI、ビルド、複数プロセスが必要な例には使用しない。
技術記事の内容から読者の理解度をチェックする4択クイズを指定のYAML形式で生成するスキル。ユーザーが「この記事のクイズを作って」「理解度チェックを作成して」「記事を読んだ人向けの問題を作って」「selfAssessment形式でクイズを作って」と依頼したときに必ず使う。ブログ記事、技術ドキュメント、チュートリアル、解説記事、技術書の章など、読者の学習内容を確認する問題を作る必要があるあらゆる場面で使う。記事のURL・マークダウン・テキスト・ファイルパスのいずれを渡されても動作する。
Generates MLS-ready property listing descriptions with attention-grabbing headlines, feature highlights, neighborhood context, and SEO keywords across multiple buyer-persona styles
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。
Symfony UX TwigComponent for reusable UI building blocks -- server-rendered components with PHP classes and Twig templates. Use when creating buttons, cards, alerts, badges, navbars, or any reusable UI element with props, blocks/slots, computed properties, or anonymous (template-only) components. Code triggers: AsTwigComponent, #[AsTwigComponent], ExposeInTemplate, PreMount, PostMount, <twig:Alert />, <twig:Button>, component(), computed properties, anonymous component, HTML syntax. Also trigger when the user asks "how to create a reusable component", "how to make a component library", "how to pass props to a component", "how to use slots/blocks in a component", "how to build a design system in Symfony", "what is the HTML syntax for components", "how to create a component without a PHP class". Do NOT trigger for components that re-render dynamically on user input (use live-component), for JS behavior (use stimulus), or for page navigation (use turbo).
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。
Create polished HTML technical reports and multi-page documents for consulting deliverables. Includes a customizable design system with dark cover page, warm cream content pages (8.5x11 portrait), professional typography, and a curated component library (KPI cards, data tables with severity coloring, callout boxes, recommendation cards, priority banners). Trigger on report, audit report, technical document, written report, assessment, or multi-page document. Outputs standalone HTML files exported to PDF via Chrome headless.
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。
Memory systems specialist for hierarchical memory, consolidation, and outcome-based learningUse when "memory system, memory hierarchy, memory consolidation, forgetting strategy, salience learning, outcome feedback, temporal memory levels, entity resolution, memory, zep, graphiti, mem0, letta, hierarchical, consolidation, salience, forgetting, ml-memory" mentioned.
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。
AI Dev Jobs MCP workflow skill. Use this skill when the user needs Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AI Dev Jobs MCP workflow skill. Use this skill when the user needs Search 8,400+ AI and ML jobs across 489 companies, inspect listings and employers, match roles, and view salary and market stats via AI Dev Jobs MCP and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。