「fea」の検索結果

Turn a feature request into a minimal, file-level implementation plan before any code.

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。

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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 a repo-local verification skill and exhaustive feature map for driving a real app through its UI, CLI, or API. Use for "create a verification skill", "make a verify skill for this repo", or "document how agents can verify this app".

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。

Index of all COSS UI particle examples. Use when implementing UI features to find copy-paste-ready component patterns built on coss primitives. Each particle has a description and a JSON URL for easy installation.

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 zero-context implementation plans with bite-sized tasks — use for multi-step feature planning

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。

Package and finalize completed work for delivery — use when a feature is done and ready to ship

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 a PR or feature needs both specification and code-quality review

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 an AI-optimized PRD using multi-AI orchestration — use when scoping a new feature or product

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。

Clarify a feature, change, or refactor through questions, then write a build prompt for another agent. Use for /ask-then-build or requests to ask questions before producing an implementation prompt.

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。

Swift Concurrency review and remediation for Swift 6.2+. Use when asked to review Swift Concurrency usage, improve concurrency compliance, or fix Swift concurrency compiler errors in a feature or file. Concrete actions include adding Sendable conformance, applying @MainActor annotations, resolving actor isolation warnings, fixing data race diagnostics, and migrating completion handlers to async/await.

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 any feature or bugfix, before writing implementation code

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 MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before 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。

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

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 starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

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。

Open or file a GitHub issue, bug report, feature request, docs issue, or chore. Add evidence to an existing issue. Run gh issue create or gh issue comment.

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。

持久战略：面对长期、复杂、短期不可能速胜的任务，客观评估态势，划分战略防御 / 相持 / 反攻三阶段，判定当前所处阶段，设置阶段转换条件，并在困难阶段仍给出局部进攻点。当任务跨多个会话或多轮迭代、资源暂时不足、或出现"想放弃"与"一把梭"两种情绪时触发；任务能在当前会话完成时不触发。 English: Protracted strategy. For long-horizon work that cannot be won quickly, assess the situation objectively, divide the effort into defense / stalemate / counter-offensive stages, name the current stage and its exit condition, and always identify a local offensive even while accumulating. Trigger when work spans many sessions or iterations, resources are temporarily short, or you notice either defeatism or a rush to finish; skip when the task fits in the current session.

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 this skill when the user mentions "pinme", or needs to upload files, store to IPFS, create/publish/deploy websites or full-stack services (including frontend pages, backend APIs, database storage, email sending, etc.), or any feature requiring backend database/server support.

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。

Analyze an existing web application, identify safe user-visible capabilities across routes, forms, server actions, handlers, and schemas, then implement first-party WebMCP tools and validate discovery and invocation with Stagehand. Use when the user asks to make a codebase agent-ready, expose website features as WebMCP tools, or add WebMCP directly to an app rather than generating a standalone injection script from a URL.

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。

Migrate browser-use (Python) browser-automation scripts to Stagehand v3 (TypeScript) on Browserbase. Use when the user wants to convert, port, rewrite, or migrate a browser-use Agent script to Stagehand, map browser-use features/APIs to Stagehand primitives (act/extract/observe/agent), or move agentic browser automation onto Browserbase with more determinism. Triggers on "browser-use", "browser_use", or "Agent(task=...)".

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 technical implementation plans from feature specifications. Use after creating a spec to define architecture, tech stack, and implementation phases. Creates plan.md with detailed technical design.

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。

Execute all tasks from the task breakdown to build the feature. Use after task generation to systematically implement the planned solution following TDD approach where applicable.

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。

Root-cause route for actual bugs, failing tests, build errors, crashes, stack traces, and unexpected behavior. Do not use for test-first/TDD feature work, test-report packaging, review-feedback handling, or final completion evidence.

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。

Test-first development route for TDD, writing failing tests first, RED -> GREEN -> REFACTOR, and behavior-changing feature/bug/refactor work. Do not use for already-failing test root-cause debugging, test-report packaging, or final completion evidence.

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。

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

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。