「fea」の検索結果

Trace an end-to-end execution flow for a request, command, or feature

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。

Explain a module, feature, or architecture path in this repository

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 standout, technically ambitious interfaces that feel extraordinary through cinematic transitions, advanced motion, heavy rendering, or other high-ambition implementation work. Use when the user wants to wow, impress, go all-out, or push a feature beyond conventional UI polish—not when simple visual confidence or routine cleanup is enough.

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 and realign UI to match design system standards, spacing, tokens, and patterns. Use when the user mentions consistency, design drift, mismatched styles, tokens, or wants to bring a feature back in line with the system.

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。

Reduce visual intensity, saturation, and noise while preserving hierarchy and character. Use when the structure is basically sound but the design feels too loud, bold, garish, aggressive, or overstimulating—not when the real problem is structural clutter, too many features, or not enough personality.

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。

Phase 2 of the Forge pipeline. Turn a research document into an implementation plan that is built around Beads issues and TDD. Reads plans/research/<slug>.md, designs the build, sets up red-green-refactor test strategy using the tdd skill, materializes a Beads epic/feature/tasks with dependencies and priorities, and defines demo/test checkpoints. Use when starting /forge-plan or the plan step of /forgemaster.

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。

Phase 1 of the Forge pipeline. Gather real context from the codebase, answer everything the code can answer through exploration, then grill the user one question at a time (with a recommended answer) for only what code cannot answer, and write a research document. Use when starting /forge-research or the research step of /forgemaster, or when the user wants a research-first investigation before planning a feature.

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。

Synthesize current context into a PRD and file it as a Beads feature/epic issue.

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。

Run the full Forge pipeline unattended: each phase is reviewed by a fresh evaluator subagent, the findings feed back, and the run advances itself. Use when the user runs /forgemaster-auto or wants a feature built end to end without approval gates.

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。

Classify a task and route it to the right workflow (fix / feature / epic). Use when the user runs /go or wants the adaptive workflow router.

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。

Produce a higher-level architecture and context map for unfamiliar code. Use when you need to understand how a file, module, feature, or issue fits into the bigger picture, including relevant modules, callers, callees, dependencies, and next 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。

Route a task by complexity and run the gated Forge pipeline (research → plan → implement → ship). Use when the user runs /forgemaster or wants the full guided feature pipeline.

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。

Triage GitHub issues through gh and a label-based state machine. Use when the user intentionally wants GitHub issue triage, incoming bug or feature review, issue workflow labels, needs-info follow-up, or ready-for-agent briefs.

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。

Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.

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。

You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.

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。

Expert prompt engineer specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use when building AI features, improving agent performance, or crafting system prompts.

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。

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI. Use PROACTIVELY for Python development, optimization, or advanced Python 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。

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in uv, ruff, pydantic, and FastAPI.

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。

Write idiomatic Ruby code with metaprogramming, Rails patterns, and performance optimization. Specializes in Ruby on Rails, gem development, and testing frameworks. Use PROACTIVELY for Ruby refactoring, optimization, or complex Ruby features.

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。

Write idiomatic PHP code with generators, iterators, SPL data structures, and modern OOP features. Use PROACTIVELY for high-performance PHP applications.

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。

Master Rust 1.75+ with modern async patterns, advanced type system features, and production-ready systems programming. Expert in the latest Rust ecosystem including Tokio, axum, and cutting-edge crates. Use PROACTIVELY for Rust development, performance optimization, or systems programming.

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。

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns. Use PROACTIVELY for Java development, microservices architecture, or performance optimization.

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。

Develop native iOS applications with Swift/SwiftUI. Masters iOS 18, SwiftUI, UIKit integration, Core Data, networking, and App Store optimization. Use PROACTIVELY for iOS-specific features, App Store optimization, or native iOS development.

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 production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

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。