「design」の検索結果

Adhoc workflow for small targeted changes to existing behavior or appearance, without the full story ceremony. Use when the orchestrator encounters a bug with a clear root cause and surgical solution, OR a small enhancement to something already built (a different icon, wording, spacing, color). Triggers on: 'fix this', 'this is broken', 'the alignment is off', 'that button doesn't work', 'quick fix', 'patch this', 'tweak this', 'change the icon', 'adjust the wording', post-story corrections, or any situation where the write gate blocks a small targeted change. Acts NOW - a defect to file and fix later belongs in /craft:bugs, other deferred work in the notebook. Do NOT use for new features, design exploration, or changes requiring creative spark.

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Use this skill when reviewing, designing, or improving a workshop, lesson, training session, facilitation plan, talk, or educational explanation. Use it when the user provides a topic, concept, or explanation and wants better framing questions, curiosity hooks, opening prompts, discussion questions, learner reflection prompts, or ways to make participants think before being taught.

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When an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.

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Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.

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Use when creating a new feature development task — scaffolds a structured task file at .claude/tasks/{feature-name}.md with phased breakdown (Design, Implementation, Testing, Documentation), acceptance criteria, context preservation, and TaskCreate tracking. Activates on "create a feature task", "set up development tracking", "plan a feature implementation", or when preparing work for python-cli-architect or python-pytest-architect agents.

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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

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Reviews Python code across type safety, error handling, security, performance, modern patterns, design clarity, typed-boundary compliance, test quality, and documentation. Use when performing code review, PR review, pre-merge quality checks, or assessing Python for security vulnerabilities, bare except clauses, Any usage outside boundaries, or missing input validation at system boundaries.

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Use when the user asks for a read-only, actionable audit of Python code quality, modernization, maintenance burden, ecosystem opportunities, or Pythonic design across a PR, git diff, staged/unstaged changes, files, or directories.

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Use when independently assessing how bounded Python code could accomplish the same behavior with less maintained code, more Pythonic design, newer language/stdlib capabilities, or well-maintained ecosystem libraries.

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Analyze specific pytest failures using authoritative contracts and a cited product/test evidence chain. Use for failing test names or pytest output, distinguishing product, interface, oracle, and harness defects without automatically changing expectations or forcing an architectural redesign.

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 independently hunting Python code smells, deviations from engineering standards, maintenance hazards, or suspicious design choices in a bounded review surface.

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 implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

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Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

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Guides pytest test architecture for Python 3.11+ using behavioral contracts, risk, and maintenance-adjusted value. Use when choosing unit/integration/property/e2e boundaries, fixture strategy, coverage measurement, or mutation testing. Preserves existing project gates but does not invent test-pyramid ratios, coverage percentages, mutation-score targets, or tests for implementation detail.

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Design and run controlled experiments using the experiment-registry MCP server — domain-agnostic, pluggable, mechanically enforced. Use when you need evidence that a change actually improves behaviour.

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Activates on any Python task involving *.py files, uv, ruff, ty, pytest, or pyproject.toml — loads the shared Python 3.11+ standards and routes the task to the specialist skill that applies them: TDD, CLI, web, data, async, typing, packaging, publishing, documentation sites, test design, test-failure analysis, or stdlib-only constrained environments.

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Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.

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Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.

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 or adapt Claude Code agent definitions. Use when creating an agent, changing subagent configuration, selecting agent scope, or designing a specialized delegation role.

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 creating a new Claude Code plugin from scratch — orchestrates prerequisite check, user discussion, parallel research, design with verification, atomic implementation, multi-layer validation, documentation, and final verification. For existing plugin improvement, use /plugin-creator:plugin-lifecycle instead.

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Start a complete plugin refactoring workflow that analyzes plugin structure, creates a refactoring plan with tasks, and guides through execution. Use when you need to refactor an entire plugin — triggers assessment, design, planning, and parallel agent execution phases.

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Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

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 the full plugin development lifecycle from blank canvas to marketplace-ready. Use when creating a new plugin, improving an existing plugin, fixing validation errors, or taking a plugin through assessment, research, design, creation, debugging, optimization, and verification. Complements /plugin-creator:plugin-creator which provides the detailed new-plugin creation workflow with discussion capture, parallel research, and atomic implementation.

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。

Assess a plugin and produce an executable refactoring design, task plan, and context manifest. Use when preparing systematic plugin refactoring.

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。