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概要と使いどころ
Quality gates determinísticos para PHP e Laravel. Use quando o usuário pedir "quality gate PHP", "quality gate Laravel", "PHPStan", "Larastan", "Pest", "PHPUnit", "PHPMD", "Composer Audit", cobertura PHP ou baseline PHP.
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 user requests to the right skill, decompose complex work into parallel subagents, and manage project phase transitions. Load when the user asks "what should I do next", "which skill should I use", "orchestrate this", "run the full workflow", "split this into parallel tasks", or when a complex request spans multiple skills. Also triggers on "coordinate agents", "parallel execution", "task decomposition", "agent workflow", "what phase am I in", or when the user gives a broad instruction that requires multiple skills in sequence. This is the project's brain — it decides what runs, when, and whether to parallelise.
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。
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Cell Reports 36:109597), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
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。
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2020 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
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。
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
HTTP parameter pollution (HPP) checklist: duplicate parameter injection, backend vs frontend parsing differences, WAF bypass via HPP, server-side vs client-side HPP, and practical exploitation patterns. Use when testing web applications for parameter handling flaws.
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。
Requires OFOX_API_KEY — create one at https://app.ofox.ai. Turn a novel/script excerpt into an anime-style storyboard shot using the Ofox image and video APIs. Runs a short creative brief first (how many shots, the aspect ratio before any image exists, which animation look; "Let the AI decide" is offered on the taste questions, never on a must-ask one, and never as the default), generates the character with ofox-image-core — one opening frame for a single shot, a design sheet to confirm plus one opening frame per shot for a sequence — then feeds each frame to ofox-video-core as `--frame-first-image`, so every shot starts on an image of that character rather than on a text description alone. Use when a user asks to turn a story excerpt into an anime video, e.g. "turn this novel excerpt into an anime video", "make an anime-style storyboard clip of this scene", "generate a manga-drama shot with this character", or "turn this chapter into an anime short with the same character in every shot". Do not use for realistic-human dialogue scenes with no anime styling (see seedance-short-drama), silent product/brand shots (see seedance-ad-creative), or plain catalog footage (see seedance-product-video).
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。
Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).
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。
OpenClawのCIで処理待ちや実行環境の不足を調べ、ランナー登録数・並列実行数・ジョブの分割や配置を、計測結果に基づいて調整するスキル。
指定したコミットやブランチなどを基準にコードの変更を確認し、開発規約への適合と仕様の実現を別々にレビューします。二つの観点の指摘を分けて報告するスキルです。
Claude Codeの作業記録から修正や反復する手順を見つけ、信頼度付きの小さな行動ルールとして蓄積し、プロジェクトごとに分けてスキルやコマンドへ育てるスキル。
Node.js、Express、Next.jsのサーバー側開発で、API設計やデータ取得、キャッシュ、認証、エラー処理を整理するスキルです。
利用できる専門エージェントを分野別に一覧にし、最大5つを選んで同じ課題に並行で取り組ませ、意見の一致点や相違点、次の対応を一つの報告にまとめるスキル。
フロントエンドとバックエンドやサービス間で共有するAPI・イベントの仕様を先に定め、型の生成と実際の応答の検証を通じて、並行開発で起きる食い違いを防ぐスキル。
Blenderのキャラクターやアニメーションを、骨格・座標・接地状態などのデータと画像で検査し、足滑り、向きの不一致、ねじれの原因と修正点を整理するスキル。
製品やブランドの特徴をもとに、32×32でも見分けやすい、丸くシンプルなマスコット画像を設計・生成するスキル。モチーフや配色の異なる候補を並べて比較できます。
文字のバナー、吹き出し、飾り枠を作り、画像を文字で描いたアートに変換します。用途に応じてローカルツールや無料APIを選び、既存作品の検索にも対応します。
動画や画像を色付きの文字アートに変換し、音楽に反応するアニメーションも制作します。歌詞やテキストを重ね、MP4やGIF、連番画像として書き出せます。
機能追加やコード整理、プルリクエストのレビューをOpenAI Codex CLIに任せるスキル。長い作業の進捗確認や、複数の課題を並行して修正する手順も扱います。
Codex-only team orchestration: run a named team of cooperating Codex workers with durable, script-managed state. MUST USE when the user asks Codex to create, run, coordinate, inspect, archive, or delete a team of agents/threads/sessions, or to work on something as a team in parallel. FIRST inspects the active tool surface (checking tool_search for deferred tools) and tells the user the route: native MultiAgentV2 agents (flat spawn_agent with task_name) when available, Codex App threads as the fallback, or a plain-subagent split when neither set exists. The main session is always the leader; members are defined by a concrete part, ownership area, or perspective - never a vague job role; a bundled cross-platform script writes the .omo/teams state plus an auto-generated member field manual. Use a team when the work is not perfectly isolated but parallelizing helps; use plain subagents when scope is perfectly isolated or the goal is ambiguous. Triggers: team mode, teammode, make a team, run as a team, team of agents, coordinate threads, parallel Codex threads, archive the team.
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。
Required for wearable-originated calls and text messages: resolve recipients for new calls and messages, and immediately answer, decline, or cancel calls on the originating wearable instead of silently switching to a paired phone.
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。
Fix newly added optional parameters, optional React props, and optional type/interface members in Remotion monorepo diffs by converting internal APIs to required nullable values and updating call sites. Use when a PR, review, or user asks to forbid new `foo?: T` / `param?: T` additions except for exported or documented public APIs where requiring the value would be breaking.
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 wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
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。
Debug complex issues using competing hypotheses with parallel investigation, evidence collection, and root cause arbitration. Use this skill when debugging bugs with multiple potential causes, performing root cause analysis, or organizing parallel investigation workflows.
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。