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概要と使いどころ
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
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。
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。
Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.
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。
Orchestrates an end-to-end CRISPR editing experiment design from target gene to delivery-ready, validatable constructs. Sequences guide design, off-target assessment, edit-modality selection (knockout, base editing, prime editing, HDR knock-in), and template/donor design, with a QC checkpoint at each handoff. Use when designing a complete CRISPR experiment for knockout, point correction, or tagging and the order of operations, the modality decision, and the cross-cutting traps are needed rather than a single step. Defers each step's mechanics to the genome-engineering skills.
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。
Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.
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。
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
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。
Senior software engineer for story execution and code implementation. Use when the user asks to talk to Elliot or requests the developer agent.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Builds production-quality, accessible, responsive user-facing UIs. Use when building or modifying interfaces and pages, creating components, implementing layouts, meeting WCAG accessibility requirements, managing state, or when the output needs to look and feel production-quality rather than AI-generated.
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。
Research customer pain points, trends, and competitors through the OMA market engine. Use for market discovery or voice-of-customer analysis.
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。
Installs CARLA's ScenarioRunner (the official scenario engine) and picks the branch that matches the CARLA in use — master for 0.9.14-0.9.16 (UE4), ue5-master for 0.10.0 (UE5), leaderboard-1.0/2.0/2.1 when driving the Leaderboard. Clones or checks out the branch, installs requirements, wires PYTHONPATH (including the `agents` package the carla wheel does not ship), and verifies the import. Use when the user asks to "install scenario runner", "set up SR", "run CARLA scenarios", "use OpenSCENARIO", or has a scenario_runner that fails to import.
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。
Builds CARLA (branch ue4-dev, Unreal Engine 4.26) from source on Linux end-to-end — UE4 fork, the Carla server (editor C++ modules), the LibCarla Python client wheel, and content — then verifies them against a from-source server. Optionally builds the native ROS 2 interface in (ROS2=1 → --ros2, Fast-DDS/CycloneDDS/Zenoh), which is compile-time only and cannot be enabled later. Use when the user asks to "build CARLA from source", "compile CARLA ue4-dev", "set up CARLA on Ubuntu (incl. 24.04)", "build CARLA with ROS2 support", or "produce a CARLA server + client wheel". Cooking a distributable Dist/ package is a separate skill (package-carla-ue4); running the build is run-carla-server.
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。
Repository-specific AGENTS.md authoring and maintenance workflow. Use when creating, refreshing, or expanding AGENTS.md coverage for a codebase, including deciding whether nested AGENTS.md files are needed for subsystems with distinct rules, workflows, or AI-sensitive constraints.
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。
Senior backend engineer specializing in scalable API development and microservices architecture. Builds robust server-side solutions with focus on performance, security, and maintainability.
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。
Specialized reverse engineering analysis workflows for binary analysis, pattern recognition, and vulnerability assessment
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。
Accessibility testing toolkit using Selenium WebDriver 4+ with Java 21+ and axe-core engine. Use when asked to validate WCAG 2.1/2.2 compliance, scan pages or components for a11y violations, test keyboard navigation, audit color contrast, check ARIA semantics, generate accessibility reports, filter axe rules, debug screen reader issues, or implement POUR principles (perceivable, operable, understandable, robust).
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。
Integrate Red Hat Ansible Automation Platform (AAP) with HashiCorp Vault Enterprise for dynamic SSH credential management. Use when: (1) Configuring Vault SSH secrets engine with AppRole auth for AAP, (2) Creating AAP credentials backed by Vault signed SSH certificates, (3) Provisioning infrastructure with Terraform/Ansible for AAP-Vault integration, (4) Setting up multi-tenant credential management, (5) Configuring golden images to trust Vault SSH CA, (6) Implementing credential rotation strategies. Based on HashiCorp validated pattern.
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 when building projects end-to-end. Orchestrates all 12 team roles — automatically switches between CTO, architect, PM, engineers, SRE, security, DBA, QA, and EM based on the current phase of work. Starts with brainstorming before any 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。
Technical SEO for AI search engines. Checks AI crawler accessibility, llms.txt compliance, robots.txt configuration, server-side rendering. Outputs problems + fixes (generates llms.txt, robots.txt modifications). Use when user says "AI crawlers", "llms.txt", "GPTBot", "GEO technical".
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。
指定レースの出走馬に対して10要素スコアリング（直近成績・コース適性・距離適性・上がり3F・G1実績・ローテ適性・騎手能力・馬場適性・枠順効果・調教師）を実行する。「スコア計算」「スコアリングして」といった依頼で発動する。
agent 向けテキスト指示（skill / slash command / task プロンプト / コード生成プロンプト）を、バイアスを排した実行者に動かしてもらい、両面（実行者の自己申告 + 指示側メトリクス）で評価して反復改善する手法。改善が頭打ちになるまで回す。Use when user says 'プロンプトを改善して', 'スキルをチューニング', 'empirical-prompt-tuning', or after creating/heavily revising a skill or prompt.
会場別のコース適性を分析する。会場を省略すると全会場の成績を表示する。「コース分析」「会場別の適性を見たい」といった依頼で発動する。
有馬記念分析システムのアーキテクチャ/レイヤー境界レビュー。docs/ARCHITECTURE.md のレイヤー構成（commands / domain / repositories / models / features / constants / database / utils / types）に対し、レイヤーディレクトリの意味と配置、参照系(queries)と更新系(aggregates)の分離、DB 接続の注入可能性とテスト容易性、リッチドメインモデルの維持（commands 肥大・ドメイン貧血症）、重み・閾値の constants 単一定義、ファイル配置と命名語彙の一致、不要な抽象化、種別分岐のポリモーフィズム化、生成引数の凝集（5個以上は分割シグナル）を検証。純粋性・依存方向の逆流・DB 接続の生成箇所は lint が error で担保するため扱わない。Use when user says 'アーキテクチャレビュー', 'レイヤー境界チェック', 'review architecture', or '依存方向の確認'.
review-all を実行し、指摘内容をコードに反映、bun run c / bun run lint / bun test で自己検証し、CONVERGED/NOT_CONVERGED の収束判定付き完了レポートを /tmp/arima-review-fix-result.md に出力する。レビュー→修正→検証→レポートを一気通貫で行うスキル。スコープ引数は /review-all と同じ（wt / PR #N / vs:<branch> / range / ファイル列挙）。review-fix-loop からの自動呼び出し（mode=non-interactive）にも対応。Use when user says 'レビューして直して', 'review and fix', '自動修正', 'レビュー反映', or when invoked by review-fix-loop.