4,754 件 ・ 関連度順
概要と使いどころ
Translate a vague request or GitHub issue into a concrete, testable spec using five structured elements (problem, scope, acceptance criteria, edge cases, success criterion) before writing any code. Prevents scope creep, PR churn, and mid-implementation pivots.
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Use for writing, reviewing, debugging, testing, or optimizing Python code that uses PyArrow arrays, schemas, Tables, RecordBatches, RecordBatchReaders, compute kernels, Dataset scans, Parquet, or Arrow IPC and C data interfaces. Trigger on schema drift, null/type semantics, chunking, batch streaming, predicate projection pushdown, conversion, and memory ownership. Do not use for pandas-only transformations, Polars-only expressions, generic SQL, Arrow Flight service design, C++/Rust Arrow, or Parquet work that does not use PyArrow.
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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.
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A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.
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A warm-canvas editorial interface for Anthropic's Claude product. The system anchors on a tinted cream canvas with serif display headlines, warm coral CTAs, and dark navy product surfaces (code editor mockups, model showcase cards). Brand voltage comes from the cream/coral pairing — deliberately warm and humanist where most AI brands use cool blue + slate. Type voice runs a slab-serif display ("Copernicus" / Tiempos Headline) for h1/h2 and a humanist sans for body. The signature Anthropic black-radial-spike mark anchors the wordmark.
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 Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.
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。
Draft and explain persistent goal-loop prompts for long-running agent work with clear stop conditions.
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。
Identify, categorize, and prioritize accumulated design inconsistencies and structural problems across a product.
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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。
Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
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Generate IEEE 830 SRS from raw requirements. Use when the user says "write SRS", "generate requirements doc", "analyze requirements", "I have raw requirements from a client", or pastes a client email/chat/PRD.
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Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.
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三个 tab 的团队管理后台: 成员、详情、活动日志, 含图表 + CSV 导出
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Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
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。
Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.
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Find authoritative guidance for installing, configuring, operating, securing, and troubleshooting OpenClaw, including channels, model providers, Gateway operations, tools, plugins, automation, nodes, multi-agent routing, and CLI errors. Use for any question or maintenance task involving an OpenClaw installation, `openclaw` CLI command, Gateway daemon, configuration key, or integration.
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。
Audit design token usage across a product for consistency and coverage.
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问题 + 成功指标 + 范围 + user stories + 设计 + 发布 + 待解决
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。
Guide for using the weather plugin — data queries, radar imagery, model maps, and meteorological calculations
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。
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
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Create a detailed, phased implementation plan with documentation discovery. Use when asked to plan a feature, task, or multi-step implementation — especially before executing with do.
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 a Product Requirements Document (PRD) for a new feature. Use when planning a feature, starting a new project, or when asked to create a PRD. Triggers on: create a prd, write prd for, plan this feature, requirements for, spec out.
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。