「definitions」の検索結果

Build or update a CONTEXT.md domain glossary for the current project. Establishes shared vocabulary with term definitions, avoid-aliases, and relationships so agents use consistent, concise language. This skill should be used when starting a new project, when domain terminology is inconsistent across sessions, when the user says "build a glossary", "define our terms", "shared vocabulary", or "domain language", or when an existing CONTEXT.md needs updating after new domain concepts emerge.

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Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

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 a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.

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 a custom MCP (Model Context Protocol) server that exposes domain-specific tools to AI assistants. Covers server implementation in Node.js or R, tool definitions, the resources and prompts primitives, transport configuration, and testing with Claude Code. Use when you need to expose custom functionality beyond what mcptools provides, when building specialized domain-specific AI integrations, or when wrapping existing APIs or services as MCP tools.

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Create parameterized Quarto or R Markdown reports that can be rendered with different inputs to generate multiple variations. Covers parameter definitions, programmatic rendering, and batch generation. Use when generating the same report for different departments, regions, or time periods; creating client-specific reports from a single template; building dashboards that filter to specific subsets; or automating recurring reports with varying inputs.

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Flask-specific delta on top of python-foundation:sqlalchemy-patterns: Flask-SQLAlchemy extension setup, db.Model declarative models (3.x Mapped style and 2.x legacy db.Column), synchronous db.session lifecycle bound to the app context, Flask-Migrate integration. Used by flask-architect (model definitions) and flask-migrate-specialist (column finalization and migration). Activated automatically by flask-plugin/stack.md. Use this skill to: - Set up the SQLAlchemy and Migrate extensions with the app-factory pattern. - Write Flask-SQLAlchemy models with the db.Model base. - Query with db.session and manage the request-scoped session lifecycle. - Integrate Flask-Migrate for Alembic-based migrations managed via flask db commands. Do NOT use this skill for: - Framework-agnostic model, column, relationship, and querying rules — see python-foundation:sqlalchemy-patterns (load it first). - Flask routing and template/API patterns — see flask-plugin:flask-conventions. - Migration execution (flask db migrate, flask db upgrade) — that's flask-migrate-specialist's job.

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Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

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Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.

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。

Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.

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。

Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.

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 Codex needs language-server diagnostics, definitions, references, symbols, or rename safety checks in the current workspace.

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。

Review the UI and style files changed in the working tree against this repo's own design system, before they are saved or committed. Runs the same deterministic check as the CLI's --check flag (no tokens, about a second): hardcoded colours where a token exists, near-identical colour twins, off-scale spacing, radii, font sizes and shadows, typefaces outside the system, arbitrary Tailwind values, static inline styles, !important, duplicate component definitions and imports of the copy to avoid, new tokens that copy an existing one, chart colours written by hand where the repo keeps a chart palette (and one calm warning asking for a palette where it keeps none), on a repo built on MUI, Mantine, Chakra or Ant Design, colours or pixel sizes written onto kit components where the theme has a value, and on a Tailwind theme or a shadcn repo, palette classes such as text-gray-500 where the theme names a colour of that kind. Findings name the file, the line and the fix. This is the small, fast check for what just changed; the roast skill is the full scan of the whole repo with a report.

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 and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.

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 AGENT.md files with proper YAML frontmatter, role definitions, expertise areas, and prompt templates following Babysitter SDK conventions.

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 Agent definitions (.md files) based on user intent and standard templates.

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。

Validates agent definitions against the Antigravity audit rubric.

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。

Best practices for designing Claude Code agent files (.claude/agents/*.md). This skill should be used when writing or reviewing agent markdown files to ensure proper design with focused domains, correct tool access, reusable definitions, and separation of capabilities from lifecycle. Combines Anthropic's official guidance with battle-tested patterns from agent team usage.

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。

This skill should be used when the user asks to "create an agent", "write a subagent", "generate agent definition", "add agent to plugin", "write agent frontmatter", "create autonomous agent", "build subagent", needs agent structure guidance, YAML frontmatter configuration, invocation criteria with examples, or wants to add specialized subagents to Claude Code plugins with proper capabilities lists and tool access definitions.

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Guides agents and users to create standards-compliant agent definitions using templates in markdown frontmatter format, ensuring context gathering, clarity, and proper metadata. Forces agents to ask clarifying questions when context is insufficient. Token-efficient documentation required. All content must be in English. Trigger: When creating a new agent definition, setting up project agents, or documenting agent workflows.

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Use when you need to install the embedded robot agents into either .cursor/agents or .claude/agents, selecting the destination interactively and copying the embedded agent definitions from project assets. This should trigger for requests such as Install embedded agents; Bootstrap .cursor/agents; Bootstrap .claude/agents; Copy robot agents. Part of cursor-rules-java project

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"Design customer service operations including tiered support (L1/L2/L3), response templates, SLA definitions, escalation procedures, and complaint handling. Use this skill when the user needs to set up a CS team, create service standards, design escalation flows, or improve response quality — even if they say 'our CS is a mess', 'how should we handle complaints', 'set up support tiers', or 'create CS SOPs'.".

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 or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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Write structured job descriptions with clear requirements, responsibilities, and employer value proposition. Follows SHRM BASK competency framework. Adapts tone to employer brand and industry context. Use when creating new positions or updating existing JDs. Do not use for internal performance criteria or career ladder definitions.

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Use the H1 Brain MCP Server to pull HackerOne program policies, scope definitions, and disclosed vulnerability reports directly into Claude's context. Based on Critical Thinking Bug Bounty Podcast Episode 166.

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。