6,575 件 ・ 関連度順
概要と使いどころ
Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.
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。
Design and audit a pricing function for variable-cost agent labor. Choose among per-seat, metered, credits, hybrid, and outcome pricing; name buyer value and cost metrics separately; calculate a reproducible cost floor; require pre-commitment guardrails; and stress-test declared personas. Use for offline pricing-design evidence, not billing implementation or runtime control. NOT for production billing, payment collection, or runtime spend enforcement.
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 a compact Coval test set from product requirements, observed conversation failures or a coverage gap. Design realistic scenarios with separate expected behaviors, provenance and controlled voice/persona variation.
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。
Validate one Coval judge against independent human labels, with grouped development/test separation, class-specific errors and uncertainty. Use for trust claims or calibration; without real labels, prepare review rather than inventing ground truth.
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。
Full website SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to up to 15 specialists (8 always + 7 conditional), generates health score. Use when user says audit, full SEO check, analyze my site, or website health check.
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。
Orchestrator that runs one UI brief through all four ui-* posture skills in parallel, then picks and merges a winner. Use when a screen matters enough to explore several directions, or the user says 'build it a few ways', 'design options', 'bake-off', 'show me variations', or 'compare approaches'.
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。
Redux Toolkit selector rules against re-renders, recalculation warnings, and render loops. Use when writing useSelector/useAppSelector or createSelector, deriving state in a connected component, or seeing 'Selector returned a different result when called with the same parameters'.
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。
Pre-ship safety proof for replacing a live implementation with a canonical one. Use when repointing nodes, tools, or services to a new contract, superseding a schema, retiring a legacy or parallel system, or any 'new way replaces the old way' change. NOT for greenfield or unconsumed additive work.
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 an Agent SKILL.md fallback for an existing find-XXXX finder that relies on a fragile discovery foundation — above all LLM_DECOMPILE (patterns C/D/E), which matches the decompiled shape of a predecessor function against a stored reference and breaks when a symbol is inlined or de-inlined in a way the reference does not cover. The generated fallback coexists with the preprocessor and runs only when it returns failure, recovering every target robustly by decompiling the predecessor and following the inline/de-inline boundary with semantic anchors. Use when a finder broke on a game update, or you want to durably backstop one before it does. The recipe generalizes to any finder foundation. Triggers: create agent skill fallback, add SKILL.md fallback, robust fallback for finder, backstop LLM_DECOMPILE finder, final guarantee skill
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 algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use when users request generative art, algorithmic art, flow fields, or particle systems. Create original works rather than copying existing artists.
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 PR review request to the correct tool — code-review plugin (quick parallel scan across all dimensions) or pr-review-toolkit (deep-dive on a specific quality dimension such as type design, error handling, test quality, comments, or simplification). Use when the user asks for a code review, PR review, or mentions a specific quality concern before creating a PR.
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。
Configure a Node.js project with pnpm, TypeScript, latest versions, and parallel script patterns following modern best practices
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エージェントのキャラクター定義ファイルを生成するスキル。ユーザーがキャラクター設定（名前、二つ名、年齢、属性、性格、支配の流儀、口癖、外見、絵文字など）を入力すると、それに基づいてIDENTITY.md、SOUL.md、USER.md、AGENTS.md、TOOLS.md、BOOTSTRAP.md、HEARTBEAT.mdの7つのファイルとavatar.pngを配置したキャラクターディレクトリを作成する。また、agent-identity/README.md にキャラクター情報とアバター画像を追加する。ユーザーが「キャラクターを作って」「新しいキャラクターを追加して」「○○っていうキャラクターのファイル作って」と言った時や、キャラクター設定を詳細に提供した時に使用する。
ガントから限定された成果群の分担を提案し、承諾後に各タスクchatのPMとそのワーカーが独立して実装・検収・正式化まで進める場合に使う。
既存キャラクター画像を、承認制でピクセルアートの正準画像へ変換し、3状態パイロット、クロマ前処理、状態間ジオメトリ検証、9状態生成、遷移QA、仮配置、アプリ内確認、ロールバック可能な正式配置まで行う。キャラクターからCodexペットを作る依頼に加え、ジャンプで小さくなる、状態ごとに身長が変わる、足元が跳ねる、クロマ縁が出る、アニメーションを修復したい依頼で使用する。
Gives coding agents temporal awareness by reading the session transcript the host already writes, so the agent stops guessing about time and stops repeating commands that just failed. Activates whenever an agent reasons about recency, elapsed time, retry windows, deploy cooldowns, memory or file staleness, idle detection, or any "when / how long ago / how long did that take" question. Required for autonomous, parallel, or long-running agents.
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。
Amplify safe or boring designs to make them more visually interesting and stimulating. Increases impact while maintaining usability. Use when the user says the design looks bland, generic, too safe, lacks personality, or wants more visual impact and character.
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。
Elite frontend image-direction skill for generating premium, conversion-aware website design references. CRITICAL OUTPUT RULE — generate ONE separate horizontal image FOR EVERY section. A landing page with 8 sections produces 8 images. Never compress multiple sections into one image. Enforces composition variety (not always left-text / right-image), background-image freedom, varied CTAs, varied hero scales (giant / mid / mini minimalist), narrative concept spine, second-read moments, and a single consistent palette across all images. Optimized for landing pages, marketing sites, and product comps that developers or coding models can accurately recreate.
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。
Detects code smells in PHP codebases. Identifies God Class, Feature Envy, Data Clumps, Long Parameter List, Long Method, Primitive Obsession, Message Chains, Inappropriate Intimacy. Generates actionable reports with refactoring recommendations.
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。
Generates API documentation for PHP projects. Creates endpoint documentation with parameters, responses, and examples.
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。
Generates DI Container configuration for PHP 8.5. Creates module classes, service providers, container configuration, and autowiring setup. Supports Symfony, Laravel, and PHP-DI patterns. Includes unit tests.
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。
When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.
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。
Manage git worktrees for isolated development. Use when creating isolated workspaces, managing parallel development, handling worktree lifecycle, or merging completed work.
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。
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
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。