「md」の検索結果

generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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将学术论文转换为交互式 HTML 课程 + Markdown 文档 + PPTX 演示文稿，一键生成全部三种输出

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Scans frontend files for common design system violations using grep-based checks.

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Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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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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Consolidated Galyarder Framework Product intelligence bundle.

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Create QA checklists for verifying design implementation accuracy.

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Turn the current conversation context into a PRD and publish it to the project issue tracker. Use when user wants to create a PRD from the current context.

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。

Dual-mode UX quality skill — runs a heuristic/accessibility/motion audit on the current codebase, or validates implementation against design mockups. Fixes all issues found and commits.

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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An inspired interpretation of Shopifi's design language — a cinematic commerce platform that runs two parallel design tracks. The marketing-hero and product-narrative pages live on near-black canvases with full-bleed photography of merchants, giant Neue Haas Grotesk display type at thin weights, and a single black-pill CTA stroked in white. The transactional pages (pricing, signup, dashboards) flip to a cream-mint canvas with pastel aloe and pistachio greens, the same pill button vocabulary, and Inter for UI body. The two tracks share typographic DNA but diverge sharply in canvas polarity — and that choice is the brand.

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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.

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Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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Identifies reusable UI patterns, components, and design tokens, then extracts and consolidates them into a design system for systematic reuse.

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Guide for using the weather plugin — data queries, radar imagery, model maps, and meteorological calculations

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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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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.

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Connects Claude Code, Codex, Cursor, and other MCP hosts to Writingmate for model discovery, text comparison, image generation, and video generation with Seedance, Sora, Veo, Kling, PixVerse, and other available 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。

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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。

Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.

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Overlay doctrine for the embedded-captions workflow — the caption MODEL (drop / rail / embed) and the rule that captions are an OVERLAY composited on top of the film, never a reserved bottom band you shift content up to avoid. Load when adding captions/subtitles to a talking-head or launch video, when deciding whether a phrase should be dropped, ride the verbatim rail, or be promoted to a scarce embedded climax, when laying out a composition that will carry captions (do NOT reserve a keep-out band), or when centering a composition on the true frame center under captions. Quotes the rail+embed model from embedded-captions and constraint

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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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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.

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 for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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。