1,896 件 ・ 関連度順
概要と使いどころ
Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewing API specifications, or establishing API design standards.
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。
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
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。
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
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。
Suggest complementary products that pair well with a given item — side tables for sofas, task lights for desks, etc. Use when the user asks "what goes with" a product, wants coordinating pieces, or asks to complete a furniture grouping.
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。
Configure AWS MCP servers for documentation search and API access. Use when setting up AWS MCP, configuring AWS documentation tools, troubleshooting MCP connectivity, or when user mentions aws-mcp, awsdocs, uvx setup, or MCP server configuration. Covers both Full AWS MCP Server (with uvx + credentials) and lightweight Documentation MCP (no auth required).
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。
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer
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。
Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".
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 premium hand-drawn architecture, workflow, and swimlane diagrams in a dark neon or light paper style, with editable Excalidraw sources, PNG/SVG previews, animated GIF/MP4 output, and interactive HTML. Use whenever the user asks for 岚叔动态架构图、手绘架构图、Excalidraw 风格图、DailyDoseOfDS 风格技术图、动态流程图、泳道图、系统可视化，或希望复刻并提升参考架构图。
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。
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
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。
Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery 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。
Auto-generates weekly KPI reports from multiple data sources including Supabase analytics, CRM data, financial spreadsheets, and email metrics. Produces executive-ready reports with dashboards, trends, highlights, concerns, and action items.
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。
Generates monthly/quarterly investor updates. Takes KPIs, milestones, challenges, financials. Writes professional investor-update.md with highlights, metrics dashboard, product updates, team news, financial summary, upcoming milestones, asks. Multiple tones from seed-stage casual to Series B formal. Use when founders need to communicate progress to investors.
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。
Convert game stats and highlights into engaging recaps. Multiple styles: Twitter thread, Instagram carousel, blog post, newsletter.
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。
Turn real customer reviews, quotes, and case-study results into polished social-proof videos built in HyperFrames -- single-testimonial spotlights, review-wall montages, and stat-driven proof reels with captions and voiceover, cut for vertical, square, and wide. The highest-converting content most businesses never make.
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。
Optimize job applications by tailoring resumes to job postings, generating customized cover letters, and preparing role-specific interview questions. Analyzes job descriptions to highlight relevant skills and experience. Use when users need to apply for jobs, customize resumes, or prepare for interviews.
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。
Convert a light-only site or app to a real dark mode -- semantic token mapping, elevation-based surfaces, recalibrated shadows and imagery -- not a naive inversion. Handles Tailwind, CSS variables, and styled-components, plus the toggle, persistence, and no-flash boot.
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 starting or inheriting a radiology research project and need to know what it is. Classifies the project type, summarizes its current state, lists missing inputs, recommends next steps and scaffolds lightweight project memory files.
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 choosing where to submit a manuscript. Matches the abstract against curated journal profiles and returns ranked picks with scope fit, AI-disclosure policy, an acceptance-readiness pre-flight and a reject-fallback cascade. Impact and APC figures may be stale.
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 fastMRI for MRI reconstruction data loading, MRI operators, reconstruction models, Lightning training, evaluation, and submission preparation.
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 Gaussian-SLAM for CUDA RGB-D dense SLAM, Gaussian-splatting mapping, dataset/configuration preflight, checkpoint evaluation, reconstruction metrics, and global-map refinement.
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。
Operate Baichuan-7B model loading, architecture inspection, C-Eval/MMLU evaluation preflights, and DeepSpeed pretraining setup from self-contained repo guidance.
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。
PL (Project Lead) role sub-skill for supervising in-flight executors. Loaded ONLY through the pl core routing table; NEVER auto-load from context, never invoke directly.
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。
精细化 AI 短剧 IP 创作技能（v0.6.0）。三阶段架构：Phase 1 创作（剧本+ref图，反复迭代）→ Phase 1.5 分镜图（每 grid 1-4 张候选静态图 = 视频首帧，工业级核心层）→ Phase 2 出片（按集解锁，4 模自动选）。v0.6.0 关键升级：① ref 库工艺偏置铁律（现代摩天楼易拟物，古建筑必出 chibi 人体，前期 IP 设计阶段就要避坑）② 即梦 5.0 失败模式 + 敏感词清单（3 类 fail 区分 / prompt 1500 字硬上限 / 反派词替换表 / 暧昧词清单）③ 分镜图 8 段 prompt 模板（CHARACTER/BACKGROUND/ACTION/SCENE/CAMERA/LIGHT/TEXT/STYLE）+ NOT humans 子句必加。沿用 v0.3.0 升级：Phase 1.5 分镜图层、4 模视频、ref 5-8 最优。v0.2.0：bash → Python subprocess、36 grid × 4-10s 变奏、红果必爆 7 招、工业级 ref 库 80-150 张、单 prompt 300-500 字。务必触发：用户提到短剧、微短剧、竖屏剧、AI 短剧、AI 漫剧、剧本创作、分镜、即梦/Seedance 出片、红果/番茄/抖音 IP 改编、爽剧、重生、穿越、赘婿、追妻、神医相师、AI 漫剧奇观、或"帮我做一部短剧"类请求。
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。
Build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS using Apple's open-source container CLI, no Docker daemon required.
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。