982 件 ・ 関連度順
概要と使いどころ
Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.
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。
Render Mermaid diagrams as ASCII/Unicode art for terminal display or as SVG files. Use when visualizing flowcharts, state machines, sequence diagrams, class diagrams, or ER diagrams. Supports 17 themes (including vellum, tokyo-night, catppuccin, nord, dracula, github) plus custom colors via --colors JSON.
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。
Discover, message, and coordinate AI coding agents (Claude Code, Codex CLI) running on the same machine via a shared Unix socket broker backed by SQLite. The broker wakes recipients natively (Claude inbox socket, codex queue) and turn-boundary hooks catch missed wakes. Supports cross-session task delegation, peer summaries, and session lifecycle management. Triggers on 'peer agents', 'cross-session communication', 'message Codex', 'inter-agent messaging', 'coordinate agents'.
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 and query fully local RAG knowledge bases from documents (PDF, MD, code, etc.) using RLAMA and Ollama — no cloud, no data leaving the machine. Triggers on 'local knowledge base', 'search documents', 'document Q&A', 'RAG query', 'ingest files', 'semantic search'.
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 hand-crafted web animation patterns as self-contained HTML: typewriter/rotator effects, progress bar simulations, file review state machines, staggered reveals, terminal status displays, streaming AI-response demos, popover/dialog/toast lifecycles, Lottie orchestration, scroll-driven sequences. Behavioral animations that simulate live software. Triggers on typewriter effect, progress animation, stagger reveal, terminal animation, streaming text, AI demo animation, toast animation, data simulation, product demo animation, loading sequence, conductor motion.
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 human-visible, machine-readable website layers for AI assistants, RAG, and agentic search. Use this skill when the user asks for llms.txt, LLM discovery plans, visible TLDR or direct-answer blocks, source trails, entity pages, markdown alternates, AI crawler policy, topic-to-URL maps, assistant-readable public content, or LLM-friendly architecture that must preserve canonical SEO strategy without hidden bot-only facts or duplicate story bodies.
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。
Adversarial, security-first review of a pull request from an EXTERNAL or untrusted contributor — a fork PR, a first-time or unknown author, anyone without write access — before a maintainer merges it. Use this whenever you're reviewing, triaging, or deciding whether to merge a PR opened against tbh-meter by someone outside the maintainer team: 'review PR #123', 'someone opened a PR — is it safe to merge?', 'check this contributor's changes', 'can we take this fork PR?', 'is this contribution legit?'. This is supply-chain DEFENSE, not ordinary code review: every merged PR auto-updates onto every user's machine and ships a memory-reading .exe plus an unsandboxed Electron app, so the author and the PR description are UNTRUSTED and the diff is the only evidence. It posts its verdict as a real GitHub review — an inline comment pinned to each problematic line (via the pulls/reviews API), not just one summary comment. NOT for your own or a fellow maintainer's branch (use self-review for that), and prefer this over the generic review flow for anything coming from outside the team.
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。
Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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。
Manage R package dependencies using renv for reproducible environments. Covers initialization, snapshot/restore workflow, troubleshooting common issues, and CI/CD integration. Use when initializing dependency management for a new R project, adding or updating packages, restoring an environment on a new machine, troubleshooting restore failures, or integrating renv with CI/CD pipelines.
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。
Install and configure the putior R package for workflow visualization. Covers CRAN and GitHub installation, optional dependencies (mcptools, ellmer, shiny, shinyAce, logger, plumber2), and verification of the complete annotation-to-diagram pipeline. Use when setting up putior for the first time, preparing a machine for workflow visualization tasks, when a downstream skill requires putior to be installed, or restoring an environment after an R version upgrade or renv wipe.
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。
Deploy machine learning models to production serving infrastructure using MLflow, BentoML, or Seldon Core with REST/gRPC endpoints, implement autoscaling, monitoring, and A/B testing capabilities for high-performance model inference at scale. Use when deploying trained models for real-time inference, setting up REST or gRPC prediction APIs, implementing autoscaling for variable load, running A/B tests between model versions, or migrating from batch to real-time inference.
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。
Cut gemstones using cabochon and faceting techniques. Covers rough assessment, orientation for optimal color and yield, dopping, cutting angles, and crown/pavilion geometry for standard brilliant cuts. Use when you have rough gemstone material to cut into a cabochon or faceted stone, planning cutting orientation for optimal colour or optical phenomena, setting up a cabbing or faceting machine, or selecting the appropriate cutting approach for a given material.
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 terminal output for a CLI tool with chalk colors, Unicode glyphs, multiple verbosity levels (human, verbose, quiet, JSON), and consistent voice rules. Covers color palette selection, status indicator design, reporter function architecture, ceremony/narrative output variants, and cross-terminal compatibility. Use when building a new CLI reporter module, adding warm narrative output to an existing tool, standardizing output across multiple commands, or designing machine-readable JSON alongside human-readable text.
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。
Deploy machine learning models to edge devices using Google AI Edge Gallery, TensorFlow Lite, ONNX Runtime, and MediaPipe. Covers model quantization (INT8/INT4), on-device inference with Gemma 4 models, Android/iOS deployment via AI Edge Gallery, hardware delegate selection (GPU/NPU/DSP), and performance benchmarking on constrained devices. Use when deploying models to mobile phones, IoT devices, or embedded systems where cloud inference is impractical due to latency, cost, or connectivity constraints.
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 new team composition file following the agent-almanac team template and registry conventions. Covers team purpose definition, member selection, coordination pattern choice, task decomposition design, machine-readable configuration block, registry integration, and README automation. Use when defining a multi-agent workflow, composing agents for a complex review process, or creating a coordinated group for recurring collaborative tasks.
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 Dockerfile for R projects using rocker base images. Covers system dependency installation, R package installation, renv integration, and optimized layer ordering for fast rebuilds. Use when containerizing an R application or analysis, creating reproducible R environments, deploying R-based services (Shiny, Plumber, MCP server), or setting up consistent development environments across machines.
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 -O3 leaves a hot loop scalar, spills appear in assembly, or a PGO or BOLT deployment is planned or stalls. Not for machine lowering: use code-generation-and-backends.
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 modeling a domain, encoding a state machine, hardening APIs, making invalid states unrepresentable, or parsing instead of validating. Not for TypeScript: use typescript-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。
Apply the Minimum Sufficient Language (MSL) principle through the MSL Kernel to write anything a reader must act on. Bind the reader and what they already know, partition facts from the machinery that produced them, emit each admitted fact as an action, a verification, a judgment, or a state, and stop when every claim is checkable and the reader can act. Use for status updates, work-in-progress reports to your own user, closeout and convergence comments, tickets, pull request and review write-ups, incident and postmortem notes, emails, memos, and any draft for readers outside the work. Also use when the user says MSL, Minimum Sufficient Language, write this for stakeholders, drop the jargon, make this readable, or asks for a paste-ready draft.
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。
Drive the machine itself, mouse, keyboard and screen, without needing vision
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。
把一个目标/主题/文章/截图/数据转成有观点、有节奏、有视觉约束的单文件 HTML 演示（网页 PPT、科技战报、产品拆解、行业观察、路演/发布会 deck、社媒可传播长图）。**7 套各锚定公认设计体系的视觉人格**——技术崇高派 Technical Sublime（工程冷光·技术战报/算力）/ 构成主义 Constructivism（宣言/号召）/ 信息·数据设计（Tufte·数据/图表）/ 判断震慑极简 Luxury（高端品牌/判断）/ De Stijl 新造型（标准/秩序）/ 编辑主义 Editorial（衬线·深度观察）/ 演示禅 Zen（暗墨·思想/品牌 keynote）——用户可指定，否则按内容自动判选并透明告知。token + validator 机检（P0/P1/P2）+ 32 维审计**机器强制设计一致性**；反伪造（不造数据/logo/截图/来源·示意标注）；字体本地子集；图按角色路由（默认 CSS/SVG·要图先探测生图环境）。Generates single-file HTML decks with 7 design-system-anchored visual personas, a semantic layout grammar, locked design tokens, evidence-grading, machine validation (P0/P1/P2 + 32-dimension audit), an anti-fabrication discipline, and image-generation environment detection. Use when the user wants a presentation / deck / slides / 网页 PPT / 战报 / 产品拆解 / 行业观察 / 路演 / 发布会 / 社媒长图, or asks to turn an article / screenshot / dataset into slides. Do NOT use for large data-table training material, strict legal/medical/financial compliance documents, multi-author collaborative PPT editing, plain-text summaries, requests to replicate a brand's official keynote visuals, or any request to fabricate realistic-looking data / fake logos / fake screenshots / fake sources.
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。
计算算子（如 matmul/GEMM）的 MFU（Machine FLOP Utilization），并给出清晰的公式和推导过程。
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。
First-run setup wizard for a freshly cloned NowStack Mobile. Installs the machine's CLIs, connects every account, wires Convex, creates YOUR own clean Git repo, gathers the product brief, then rebrands the boilerplate into your app. Use for "set up my project", "initial setup", "onboard me", "ns onboard", "I just cloned this", or the very first run.
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。
Find App Store Connect API credentials (key ID, issuer ID, .p8 file) on the user's machine and signed-in browser session, then log in with asc CLI. Use when asc auth is needed, credentials are unknown, user says "login to App Store Connect" or "find my ASC key", or before ns-ios-deploy-app.
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。