typescript-rules

176

shinpr

Applies type safety, component design, and state management rules for React + TypeScript. Used when implementing React components.

technical-spec

176

shinpr

Defines frontend environment variables, component design, and data flow. Used when configuring a React environment.

project-context

176

shinpr

Provides project-specific prerequisites for AI execution accuracy. Use when checking project structure.

documentation-criteria

176

shinpr

Guides PRD, ADR, Design Doc, and Work Plan creation with templates and decision matrix.

implementation-approach

176

shinpr

実装戦略（垂直スライス、水平、ハイブリッド）をリスク評価で選択。機能の実装計画時に使用。

typescript-testing

176

shinpr

Vitestテスト設計と品質基準を適用。カバレッジ要件とモック使用ガイドを提供。ユニットテスト作成時に使用。

task-analyzer

176

shinpr

Analyzes task essence and selects appropriate skills. Returns scale estimates and metadata. Use when starting tasks or selecting skills.

subagents-orchestration-guide

176

shinpr

Coordinates subagent task distribution and collaboration. Controls scale determination and autonomous execution mode.

coding-standards

176

shinpr

Detects code smells, anti-patterns, and readability issues. Use when implementing features, reviewing code, or refactoring.

integration-e2e-testing

176

shinpr

統合テストとE2Eテストを設計。モック境界と振る舞い検証ルールを適用。E2Eテスト、統合テスト作成時に使用。

testing-principles

162

shinpr

Language-agnostic testing principles including TDD, test quality, coverage standards, and test design patterns. Use when writing tests, designing test strategies, or reviewing test quality.

frontend-ai-guide

162

shinpr

Frontend-specific technical decision criteria, anti-patterns, debugging techniques, and quality check workflow. Use when making frontend technical decisions or performing quality assurance.

ai-development-guide

162

shinpr

Technical decision criteria, anti-pattern detection, debugging techniques, and quality check workflow. Use when making technical decisions, detecting code smells, or performing quality assurance.

coding-principles

162

shinpr

Language-agnostic coding principles for maintainability, readability, and quality. Use when implementing features, refactoring code, or reviewing code quality.

mcp-local-rag

146

shinpr

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.

coding-rules

35

shinpr

Applies coding standards for clean, maintainable code. Use when: writing functions, handling errors, refactoring, or reviewing code style.

metacognition

35

shinpr

Validates approach and checks assumptions before/after tasks. Use when: starting work, encountering errors, or switching phases.

testing-strategy

35

shinpr

Prioritizes tests based on ROI and critical user journeys. Use when: deciding test coverage, selecting test types, or budgeting effort.

testing

35

shinpr

Applies TDD process, test quality criteria, and mock guidelines. Use when: writing unit tests, using mocks, or reviewing test quality.

prompt-optimization

7

shinpr

Model-agnostic prompt analysis and optimization patterns based on 2025–2026 research. Use when analyzing prompts for issues or generating optimized versions. Provides eight patterns (BP-001 through BP-008) and a three-step optimization flow.

worktree-execution

7

shinpr

Git worktree management for isolated parallel prompt execution. Use when creating isolated environments for prompt comparison or managing worktree lifecycle. Provides creation, cleanup, and orphan detection scripts.

knowledge-base

7

shinpr

Project-specific prompt optimization knowledge management. Use when storing or retrieving learned patterns from comparisons. Provides schema, extraction criteria, capacity management, and retention scoring.

sub-agents

6

shinpr

Execute external CLI AIs as isolated sub-agents for task delegation, parallel processing, and context separation. Use when delegating complex multi-step tasks, running parallel investigations, needing fresh context without current conversation history, or leveraging specialized agent definitions. Returns structured JSON with agent output, exit code, and execution status.