2천건 · 관련도순
Guidelines for designing tools and action spaces for AI agents. Use when building agent harnesses, designing tool interfaces, creating elicitation mechanisms, or optimizing agent-tool interactions. Covers progressive disclosure, tool design patterns, and lessons from Claude Code development.
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Design, mock up, build and review terminal UIs — full-screen TUIs, interactive CLIs and pickers, and plugin panels or popups inside a host (herdr, tmux, Neovim, k9s, lazygit). Use when designing, reviewing (from code, a live capture or a screenshot) or redesigning a TUI's layout, keybindings, states or colors, including requests that name a known TUI (lazygit, k9s, btop, yazi, helix, fzf); writing terminal mockups; building or fixing one in Ink, Textual, Bubble Tea, Ratatui or gum; mapping a color scheme (Catppuccin, Dracula, Nord, Gruvbox, Tokyo Night, Rosé Pine…) onto a TUI's theme; fixing misaligned glyphs, flicker, NO_COLOR, exit codes, signals, pipes or a broken terminal after exit; suspend or $EDITOR handoff; or profiling a slow TUI. Not for web or native GUIs, terminal-emulator, editor or shell-prompt themes.
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Before designing, training, or auditing ANY model that replicates human-annotated labels, audit the annotation protocol's INPUT — the exact document/evidence the human labelers consulted — and give the model that same input. Use when: (1) designing a classifier/LLM extractor whose target is a hand-coded label set, (2) a label-replication model shows low recall concentrated in a label subset and the diagnosis on offer is "the label's information is not in the features", (3) reviewers propose construct splits (e.g. "designation vs record-evident"), adjudication sittings, or per-domain stop rules to explain residual disagreement with gold, (4) validating an extraction pipeline against labels transcribed from a source document. Symptom of the underlying failure: elaborate theory accumulates to explain why gold is "partially unpredictable" when the model was simply never shown the document the annotators read.
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Assists in provisioning instances and databases, designing performant schemas, and querying data in Spanner. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.
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Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
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Use this skill when designing, building, or troubleshooting FSC Action Plan templates for client-facing task sequences such as client onboarding, account opening, annual review preparation, and compliance tasks. Trigger keywords: Action Plan template, ActionPlanTemplate, ActionPlan object, FSC task sequence, onboarding checklist, review prep tasks. NOT for designing the onboarding process itself — use admin/client-onboarding-design. NOT for Health Cloud care plan templates — use admin/care-plan-configuration.
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Use when designing, redesigning, or improving a frontend interface in Angular, React, or Blazor. Use for landing pages, dashboards, components, forms, and responsive layouts that must work mobile-first and then scale to desktop.
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Use when designing new API endpoints, defining module boundaries or contracts between teams, creating component prop interfaces, or changing an existing public interface. Covers naming, versioning, error shapes, and designing surfaces that are hard to misuse.
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Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.
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採用・人材戦略の専門スキル。職務記述書（JD）作成、採用計画策定、コンピテンシーモデル設計、 面接評価基準設計、オンボーディング計画をサポート。採用要件の明確化から内定後のフォローまで 一貫した人材獲得プロセスを支援。日英両言語のテンプレートを提供し、グローバル採用にも対応。 Use when: creating job descriptions, designing competency models, planning recruitment strategies, developing interview evaluation criteria, or creating onboarding plans. Triggers: "JD作成", "採用計画", "面接評価", "オンボーディング", "コンピテンシー", "人材獲得", "job description", "recruitment plan", "interview evaluation", "hiring", "talent acquisition"
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KPI体系設計とOKR策定支援スキル。ビジネス目標に整合したKPI設計、バランススコアカード、OKRフレームワーク、 ダッシュボード設計を提供。SMART原則、リーディング/ラギング指標の選定を支援。 Use when designing KPI frameworks, implementing OKR methodology, or creating performance measurement systems. Triggers: "KPI design", "OKR", "performance metrics", "balanced scorecard", "dashboard design", "measurement framework".
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本番と同じ失敗を本番前に検出するためのテスト階層設計スキル。 DB方言差、実依存関係、永続化確認、timezone差、packaging整合性、 adversarial regressionをunit/integration/e2e/smoke/packagingに 適切に割り振り、PR CIで最低限の本番同等性を保証する。 Use when designing production-parity tests, closing test blind spots, building smoke suites for real dependencies, verifying persistence beyond UI success, or creating adversarial regression backlogs.
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AWS CLIの専門家スキル。EC2, S3, IAM, Lambda, RDS, ECS等の主要サービスのCLI操作を支援。IAMポリシー設計、クロスアカウント運用、セキュリティ設定、コスト最適化のベストプラクティスを提供。Use when working with AWS services via CLI, designing IAM policies, managing infrastructure, or automating cloud operations.
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Use when designing, auditing, or redesigning a web UI; select fast, prototype, or studio from the requested scope.
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Shared vocabulary for designing deep modules, interfaces, seams, adapters, leverage, and locality. Use when designing or reshaping module boundaries, making code more testable, or evaluating interface depth.
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SDDワークフロー全体を統括するオーケストレーター。要求管理・タスク計画・実装・逆順レビューの一連のフローを管理する。複数フェーズにまたがるワークフロー管理、エラー・バグの体系的な分析と修正に使用する。要求は requirement-management、設計はPR本文が既定の記録先であり、requirements-defining と software-designing は非推奨。Do NOT use for 個別フェーズのみの作業（requirement-management、task-planningを直接使用すること）。
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要求・ユーザーストーリー・理由・用語・要求間の関係をYAMLレジストリで管理し、追加・変更・廃止のたびに要求間の整合性と矛盾を機械的に検証する。要求の追加/変更/削除、矛盾チェック、トレーサビリティ確認、ユーザーストーリーと受入条件の管理が必要な場合に使用する。設計は実装時にPR本文・GitHubコメントに書くだけで永続化しない運用を前提とする。requirements-defining / software-designing / sdd-document-management の移行先。Do NOT use for 設計書の作成・永続化（設計はPRに書く）。
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Product marketing and go-to-market strategy for B2B SaaS launches and positioning. Use this skill when planning a product launch, developing positioning and messaging, analyzing competitive landscape, building customer advocacy programs, designing a win/loss interview program, working out why we lose deals, designing GTM strategy, creating category positioning, planning analyst briefings, or deciding which country to expand into and how far to localize. Also use it for brand marketing and demand creation — how much to spend on brand versus performance, what to measure when a brand campaign produces no conversion event, and how to answer "we cannot attribute it". Also triggers on: positioning, product launch, competitive intelligence, messaging, customer advocacy, win/loss analysis, win/loss interviews, why we lose deals, closed-lost analysis, loss reason, close-reason field, competitive displacement, displace an incumbent, rip and replace, competitive takeout, switch from a competitor, win deals from a competitor, incumbent contract, renewal window, auto-renewal notice period, switching costs, EU Data Act, cloud switching rights, why do we lose to no decision, GTM, category design, analyst briefing, G2 review, international expansion, market entry, expand into Europe, which country should we launch in next, localization, translate our site, in-country go-to-market, regional GTM, is cold email legal in Germany, brand marketing, brand awareness, brand vs performance, brand versus demand gen, demand creation, demand capture, 95-5 rule, out of market buyers, category entry points, distinctive brand assets, mental availability, brand tracking, brand health, brand lift, share of search, how do we measure brand, justify brand spend, brand budget.
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Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flutter, Postgres, GraphQL, Go, Python. Includes architecture diagram generation, system design patterns, tech stack decision frameworks, and dependency analysis. Use when designing system architecture, making technical decisions, creating architecture diagrams, evaluating trade-offs, or defining integration patterns.
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日本語のUI文言（ボタン・見出し・ラベル・入力欄の説明・空状態・エラー・確認ダイアログ・完了通知・チャットbotの応答文）を書く・直すときの規則。画面やbotがユーザーに見せる日本語を新しく書く時、既存の文言を見直す・統一する時、英語のまま残った表示を日本語にする時、サーバーのエラーメッセージを画面に出す経路を作る時に使用。公開されている日本語UIガイドライン（SmartHR・デジタル庁・Microsoft・Apple・公用文）の一致点を規則にし、食い違う点は採否と理由を定めてある。境界: 人に宛てた文章（PR本文・メッセージ・ドキュメント）は ukwhatn-writing、画面に何を出すかと提示前の完了基準は context/ui-artifact-standards.md、ナビゲーション・重ね方・通知の型とレイアウトは designing-ui、コードの実装原則は writing-code。
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Design clean, scalable, and maintainable REST and GraphQL APIs following industry best practices. Use when designing public or internal APIs, planning endpoint structures, defining request/response contracts, establishing versioning strategies, implementing authentication patterns, designing data models, creating API documentation, ensuring consistent error handling, optimizing for performance, or establishing service contracts between microservices.
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Design scalable, normalized database schemas with proper relationships, indexes, constraints, and migration strategies for relational and NoSQL databases. Use when designing database schemas, planning table relationships and foreign keys, creating indexes for query optimization, defining constraints and validations, designing data models for scalability, planning database migrations, choosing between SQL and NoSQL, implementing sharding strategies, optimizing query performance, or establishing data integrity rules.
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Classify every destructive/gated action an agent body can attempt (git, filesystem, network, shell, github) into block/approve/allow tiers, and audit whether the pre-tool and post-tool governance backing that classification is real: blocked actions proven zero-side-effect by a negative fixture, every denial backed by a receipt and a transcript event, every block-tier denial paired with a safe alternative, and no unmanaged or same-UID body ever marked "contained." Use when building or reviewing Agent Harbor's C5 governance gate (destructive git blocker, approval request/result, denial receipts), gating a new tool surface before it can run destructive commands, or auditing whether an existing "destructive-action blocked" claim actually has evidence behind it. NOT for proving an existing sandbox boundary actually contains an adversary once an action is isolated (use sandboxed-adversarial-test-harness), deciding where in a DAG to place a human review node or designing the approval UX itself (use human-gate-designer), or designing the general work-receipt schema across an entire agent task (use agent-work-receipt-designer).
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Apply GDPR-compliant engineering practices across your codebase. Use this skill whenever you are designing APIs, writing data models, building authentication flows, implementing logging, handling user data, writing retention/deletion jobs, designing cloud infrastructure, or reviewing pull requests for privacy compliance. Trigger this skill for any task involving personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines that process real data, or any question framed as "is this GDPR-compliant?". Inspired by CNIL developer guidance and GDPR Articles 5, 25, 32, 33, 35.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.