1,312 件 ・ 関連度順
概要と使いどころ
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or EnFormer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
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。
Run build, deploy, inspect, and fix loops for .NET MAUI apps that already have MAUI DevFlow integrated. USE FOR: launching MAUI apps, selecting devices or emulators, waiting for or recovering agent connections, broker/port/adb connectivity issues, visual tree inspection, screenshots, UI interaction, Blazor WebView CDP debugging, reading DevFlow logs, and iterative app debugging. DO NOT USE FOR: first-time DevFlow package setup (use maui-devflow-onboard), or generic desktop automation unrelated to MAUI. INVOKES: maui devflow CLI, dotnet CLI, Android adb/android tools, and Apple simctl tools.
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 single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", "single URL", "check this page", "page analysis", or provides a single URL for review.
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。
Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical. Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "service area", or "multi-location".
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 an Agent SKILL.md fallback for an existing find-XXXX finder that relies on a fragile discovery foundation — above all LLM_DECOMPILE (patterns C/D/E), which matches the decompiled shape of a predecessor function against a stored reference and breaks when a symbol is inlined or de-inlined in a way the reference does not cover. The generated fallback coexists with the preprocessor and runs only when it returns failure, recovering every target robustly by decompiling the predecessor and following the inline/de-inline boundary with semantic anchors. Use when a finder broke on a game update, or you want to durably backstop one before it does. The recipe generalizes to any finder foundation. Triggers: create agent skill fallback, add SKILL.md fallback, robust fallback for finder, backstop LLM_DECOMPILE finder, final guarantee skill
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。
Full-stack Convex development guidelines covering React, Vite, TypeScript, mutations, auth, design system, and documentation practices. Use when building features, writing Convex functions, or making code changes in this project.
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。
Comprehensive China A-share stock analysis covering fundamental analysis, technical analysis, policy impact assessment, and market-specific features (T+1 trading, price limits, northbound capital flow). Use when user asks about A股分析, Chinese mainland stocks, Shanghai/Shenzhen listed stocks, or needs analysis considering China market characteristics.
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。
Accessibility audit for CSS covering focus styles, color contrast, text sizing, screen reader support, and WCAG compliance. Provides actionable fixes. Use when auditing accessibility or fixing a11y issues.
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。
Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies.
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。
Master Bevy's Entity Component System (ECS) in Rust, covering Systems, Queries, Resources, and parallel scheduling.
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。
API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best practices.
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。
Expert guidance for building modern Android UIs with Jetpack Compose, covering state management, navigation, performance, and Material Design 3.
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。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
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 and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
Start here. Introduces what NemoClaw is, what agent skills are available, and which skill to use for a given task. Use when discovering NemoClaw capabilities, choosing the right skill, or orienting in the project. Trigger keywords - skills, capabilities, what can I do, help, guide, index, overview, start here.
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 this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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 a delightful, unexpected "wow" experience for the user by dynamically discovering and creatively combining other enabled skills. Triggers when the user says "surprise me" or any request expressing a desire for an unexpected creative showcase. Also triggers when the user is bored, wants inspiration, or asks for "something interesting".
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。
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
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。
Expert sales execution covering pipeline management, discovery, demos, negotiation, and deal closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
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。
Comprehensive API design patterns covering REST, GraphQL, gRPC, versioning, authentication, and modern API best practices
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。
Systematic debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".
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。
Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath).
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。
Comprehensive technology-agnostic prompt generator for documenting end-to-end application workflows. Automatically detects project architecture patterns, technology stacks, and data flow patterns to generate detailed implementation blueprints covering entry points, service layers, data access, error handling, and testing approaches across multiple technologies including .NET, Java/Spring, React, and microservices architectures.
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。
Knowledge and guardrails for the mise + fnox + infisical secrets toolchain, covering secret injection, secret providers, and env var hygiene.
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。