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概要と使いどころ
Design and operate Azure Storage services for durability, performance, and secure access. Use this skill when users ask about Blob, Files, Queues, Tables, or Data Lake storage patterns. Covers redundancy, tiering, access models, lifecycle policies, and compliance.
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Drafts a defendant employer's Answer to a workplace harassment complaint. Responds paragraph-by-paragraph to allegations, asserts Faragher-Ellerth and other affirmative defenses, and follows federal/state civil procedure rules. Use when defending employers at the pleadings phase against Title VII, state FEHA, or other employment harassment claims.
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。
Unified verification: build→lint→test→type-check→coverage. Use for 'is everything green', 'run all checks', 'verify build'. Auto-called by mk:cook. NOT for lint/format only (see mk:lint-and-validate); NOT for test-to-requirement coverage mapping (see mk:nyquist).
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Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).
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Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
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Complete bedside prescribing reference for Mirago (mirabegron, beta-3 adrenoceptor agonist) for overactive bladder — covering contraindications, safety screening, drug interactions, dose selection in special populations, titration, and administration. Use when a clinician asks how to start mirabegron, what dose to use in renal or hepatic impairment, is mirabegron safe in this patient, mirabegron drug interactions, or Mirago for OAB.
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Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
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Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.
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Use when you need CRAP score and coverage risk analysis to find high-risk code before refactoring or release. Keywords: CRAP score, risk hotspots, cyclomatic complexity, test coverage gaps, coverage threshold.
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Use when working with Terragrunt — DRY multi-env configs, module dependencies, remote state orchestration — even when the user just says 'deploy this to staging and prod' without naming Terragrunt.
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Banco de dados vetorial gerenciado para aplicações de IA em produção. Totalmente gerenciado, com auto-scaling, busca híbrida (vetores densos + esparsos), filtragem de metadados e namespaces. Latência baixa (<100ms p95). Use para RAG em produção, sistemas de recomendação ou busca semântica em escala. Ideal para infraestrutura serverless e gerenciada.
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Framework para construir aplicações com LLM usando agentes, chains e RAG. Suporta múltiplos provedores (OpenAI, Anthropic, Google), 500+ integrações, agentes ReAct, tool calling, gerenciamento de memória e recuperação de vector stores. Use para construir chatbots, sistemas de perguntas e respostas, agentes autônomos ou aplicações RAG. Ideal para prototipagem rápida e deployments em produção.
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Framework de dados para construir aplicações LLM com RAG. Especializado em ingestão de documentos (300+ conectores), indexação e consultas. Inclui índices vetoriais, mecanismos de consulta, agentes e suporte multimodal. Use para perguntas sobre documentos, chatbots, recuperação de conhecimento ou construção de pipelines RAG. Ideal para aplicações LLM centradas em dados.
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Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。一次调用同时召回 PubMed + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv 等)+ 灰色文献,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(含全文 OA 链接、citations metadata、grants 资助信息)。当任务出现 Europe PMC、多源文献聚合、广召回、RAG 检索、预印本搜索、bioRxiv、medRxiv、全文链接、DOI 检索、跨库去重、灰色文献、资助检索 等触发词时使用。**定位**:广召回 · 多源聚合 · RAG 友好(与 pubmed-eutils 互补——后者做精准官方源、Clinical Queries;本 skill 做"撒网"式广召回)。**不做**:NCBI E-utilities 直检(交给 pubmed-eutils)、临床试验(clinical-trials-v2)、全文 XML 抽取(bioc-fulltext-fetch)、实体识别(pubtator-entity-search)、证据分级(medical-evidence-grading)。
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。
运用《智能体设计模式》(Antonio Gulli) 的 21 个模式,指导 AI Agent 系统的架构设计与选型。适用于用户在设计 Agent 工作流(选择链式/路由/并行/多Agent编排)、提升 Agent 输出质量与可靠性(反思/异常处理/Guardrails)、做 RAG 技术选型(混合检索/GraphRAG/Agentic RAG)、优化 Agent 成本与延迟(动态模型切换/回退)时使用。
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。
Review storage placement across framework global data, workspace data, host state, sidecars, caches, vaults, and external sources.
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。
WooCommerce Memberships storage and relationship map for membership plan CPTs, user membership CPTs, post statuses, plan/user membership meta keys, rule storage, profile-field storage, order grant meta, and WooCommerce Subscriptions-linked memberships. Use when code reads or writes wc_membership_plan, wc_user_membership, wcm-* statuses, wc_memberships_rules, _subscription_id, _has_installment_plan, _free_trial_end_date, membership plan access meta, user membership dates, product/order grant meta, or when an agent needs exact Memberships CPT/meta names and the Memberships-Subscriptions relation.
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。
Picks the right WordPress storage primitive for plugin data: options, user/post/term/comment meta, transients, site options, site transients, or custom tables. Covers grouped settings, autoload management, transient TTL rules, serialized/JSON blob trade-offs, multisite storage caveats, and naming conventions. Use when scaffolding settings, choosing persistence for plugin-owned data, or auditing update_option/get_option/get_post_meta/set_transient/autoload usage.
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。
Evaluates downstream tests for coverage and edge cases.
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。
diff-time で「影響範囲を repo 全体で調査した証拠」「失敗系を検証した証拠」「外部依存・レート制限・キャッシュ整合を確認した証拠」の欠落を検出する evidence-sufficiency 観点。defect そのものは既存 skill へ委譲し、本 skill は「そのリスク種別を調査した証拠が差分・PR 本文・テストに残っているか」の meta 評価のみを行う。証拠が同一 diff に同梱（テスト・canary・grep 記録）されていれば充足とみなす
SharedPreferences (v2.5.5) for Flutter providing simple persistent key-value storage for non-sensitive user settings and app preferences. Use this skill when storing user preferences (theme mode, language selection, font size), app settings and configuration, feature flags or A/B test assignments, simple cache data, onboarding completion status, last selected values (filters, sort options), user consent flags, or any non-sensitive data that survives app restarts. Supports String, int, double, bool, and StringList types. NOT suitable for storing tokens, passwords, API keys, or any sensitive/encrypted data (use flutter_secure_storage instead). Ideal for quick persistent storage without database overhead.
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。
FlutterSecureStorage (v10.x) for encrypted key-value storage of sensitive data using platform-native secure enclaves with biometric authentication support. Use this skill when storing OAuth tokens (access tokens, refresh tokens, ID tokens), API keys and secrets, user passwords or PINs, session tokens, encryption keys, private keys for cryptography, certificate data, biometric enrollment data, banking credentials, health data, or any sensitive information requiring platform-level encryption (iOS Keychain with Secure Enclave, Android Keystore with Hardware-backed keys). Supports Touch ID, Face ID, and Android biometric prompt integration. Handles biometric change invalidation (resetOnError), data migration from SharedPreferences/EncryptedSharedPreferences, Google Drive backup exclusion, automatic key rotation, configurable encryption algorithms (AES), and prevents data extraction even on rooted/jailbroken devices. Essential for authentication flows, secure credential management, or compliance requirements (PCI DSS, HIPAA, GDPR data protection).
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ベクトルデータベースとセマンティック検索を使用してLLMアプリケーションのためのRetrieval-Augmented Generation（RAG）システムを構築します。知識に基づいたAI、ドキュメントQ&Aシステムの構築、またはLLMと外部ナレッジベースの統合時に使用します。
Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues.
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。