「functional」の検索結果

Use when you need to apply functional exception handling best practices in Java — including replacing exception overuse with Optional and VAVR Either types, designing error type hierarchies using sealed classes and enums, implementing monadic error composition pipelines, establishing functional control flow patterns, and reserving exceptions only for truly exceptional system-level failures. This should trigger for requests such as Improve the code with Functional Exception Handling; Apply Functional Exception Handling; Refactor the code with Functional Exception Handling; Model Java errors with Result or Either types; Replace exception-heavy flows with functional error handling. Part of Plinth Toolkit

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Use when you need to apply functional programming principles in Java — including writing immutable objects and Records, pure functions, functional interfaces, lambda expressions, Stream API pipelines, Optional for null safety, function composition, higher-order functions, pattern matching for instanceof and switch, sealed classes/interfaces for controlled hierarchies, Stream Gatherers for custom operations, currying/partial application, effect boundary separation, and concurrent-safe functional patterns. This should trigger for requests such as Improve the code with Functional Programming; Apply Functional Programming; Refactor the code with Functional Programming; Refactor Java code to use streams or Optionals safely; Improve immutability and pure functions in Java. Part of Plinth Toolkit

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Facilitates conversational discovery to create Architectural Decision Records (ADRs) for non-functional requirements using the ISO/IEC 25010:2023 quality model. Use when the user wants to document quality attributes, NFR decisions, security/performance/scalability architecture, or design systems with measurable quality criteria. This should trigger for requests such as Create ADR for Non-functional requirements; Document Non-functional requirements; Capture Non-functional requirements; Generate Non-functional requirements in an ADR. Part of cursor-rules-java project

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I/O + 계산 혼재 또는 Mock stub + 계산 혼재 메서드를 Functional Core / Imperative Shell(빵속빵, Impure-Pure-Impure Sandwich)로 분리. "순수 함수 분리", "I/O와 계산 나눠", "mock 없이 테스트하게", "functional core", "/segregate-functional-core" 요청 시 사용. 단, 값 반환과 부수효과 분리(CQS)만이면 /separate-query-modifier가 적합. /segregate-functional-core [commit-ref]로 호출.

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Write a Functional Requirements Document. Use when the user says "write an FRD", "functional requirements", "system requirements", "SRS", "what should the system do", "document the functional specs", "software requirements specification", "detailed requirements for engineering", "functional spec" - even if they don't explicitly say "FRD".

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要件定義と実装の間を埋める技術仕様書を体系的に作成するスキル。 画面設計、API設計、DB設計、シーケンス図、状態遷移図をMermaid形式で生成し、 IEEE 830/ISO 29148準拠の仕様書を出力する。Use when creating functional specifications, API design documents, database design documents, screen design specifications, or sequence/state diagrams from requirements. Triggers: "technical specification", "functional spec", "API design", "database design", "screen design", "画面設計書", "API設計書", "DB設計書", "技術仕様書", "シーケンス図", "状態遷移図"

Refactor Swift code toward declarative functional programming with immutability, value types, pure functions, transformations, and closure-based APIs. Use when asked for functional refactoring or `/refactor:functional`.

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Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.

Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.

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Profile functional potential of metagenomes using HUMAnN3 and similar tools. Use when obtaining pathway abundances, gene family counts, or functional annotations from metagenomic data.

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Guide for translating Python code to functional Scala style. Use when converting Python code involving higher-order functions, decorators, closures, generators, or when aiming for idiomatic functional Scala with pattern matching, Option handling, and monadic operations.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Applies Functional Core, Imperative Shell to isolate logic from side effects. Use when business logic is entangled with I/O or unit tests are slow and brittle.

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Create, maintain, or migrate functional skills; supports creating from a requirement brief and migrating an existing legacy skill directory into a functional skill structure.

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Starter template for a functional agent skill.

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Think and work like an expert Functional Genomics Scientist. Use when a task calls for Functional Genomics Scientist judgment. Reasons from perturbation as causal probe, genotype-to-phenotype linkage, library representation, and effect-size-plus-FDR statistics through MAGeCK/BAGEL/CERES-Chronos, CRISPRcleanR, CRISPResso2, MPRAnalyze, and Perturb-seq pipelines while treating MOI/bottleneck artifacts, copy-number and p53/DSB toxicity, RNAi seed effects, and guide-assignment or gating errors as first-class failure modes.

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🇺🇸 Your dev team ships features nobody asked for while user-reported bugs pile up for months. Operations blames engineering for ignoring users; engineering blames operations for not understanding technical constraints. This gives you the complete Product × Engineering × Operations alignment SOP — from unified backlog to 10-day sprint cadence to veto power rules. What's inside: • Dual-layer Kanban system (master backlog + sprint board with unified tagging) • 10-day sprint standard process (Day 1 dev → Day 6 testable build → Day 10 ship) • Issue template with reproducibility requirements (3x reported = auto-severe) • Tri-party alignment meetings (daily standup / sprint planning / sprint review) • Operations veto power on releases (P0 bug = block shipping) • User feedback → product iteration closed loop (beta testing + interview SOP) • Core metrics framework (acquisition → activation → retention → monetization → referral) • Technical debt management (20-30% sprint capacity reserved) • Ready-to-use templates: Bug Report, Sprint Planning, Responsibility Matrix Built from: Real product strategy meetings + beta testing frameworks. References Supabase sprint model, Manus/DeepSeek commercialization alignment. By @WeiYipei. 🇨🇳 你的研发团队在做没人要的新功能，用户反馈的 Bug 堆了三个月没人动。运营觉得研发不听用户，研发觉得运营不懂技术。这份 SOP 给你从统一看板到 10 天迭代节奏到一票否决权的完整产研运协同框架。 🇯🇵 開発チームは誰も求めていない機能を作り、ユーザーから報告されたバグは何ヶ月も放置。このSOPは、統一バックログから10日スプリント、リリース拒否権まで、プロダクト×エンジニアリング×オペレーションの完全な連携フレームワークを提供します。 🇰🇷 개발팀은 아무도 요청하지 않은 기능을 만들고, 사용자가 보고한 버그는 몇 달째 방치됩니다. 이 SOP는 통합 백로그부터 10일 스프린트, 릴리스 거부권까지 제품×개발×운영 완전 협업 프레임워크를 제공합니다. Triggers: "product ops" | "engineering operations" | "product development SOP" | "sprint planning" | "iteration management" | "cross-functional alignment" | "product engineering ops" | "dev ops collaboration" | "产研运协同" | "迭代管理" | "产品研发运营" | "プロダクト開発運営" | "제품개발운영"

Systematically interpret infrared spectra to identify functional groups present in a sample. Covers diagnostic region analysis (4000-1500 cm-1), fingerprint region assessment (1500-400 cm-1), hydrogen bonding effects, and compilation of a functional group inventory with confidence levels.

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Angular Router (built-in `@angular/router`) — route configuration for standalone and NgModule projects, functional guards (Angular 14.1+), lazy loading, route resolvers, typed params via signals/observables, programmatic navigation, route data and meta. Use this skill to: - Configure routes (standalone-style or NgModule-style). - Use functional guards (canActivate as function, preferred over class-based in 17+). - Lazy-load components or feature modules. - Implement auth guards via route meta + functional guards. - Read params/queries via `inject(ActivatedRoute)` + signals or RxJS. Do NOT use this skill for: - General conventions (see angular-conventions). - State management (see angular-state-and-rx). - Forms (see angular-forms). - Testing routes (see angular-testing).

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Analyze ENCODE functional genomics screens including CRISPR screens, MPRA (Massively Parallel Reporter Assays), and STARR-seq. Find screen data in ENCODE, process results, identify functional elements, and integrate with epigenomic annotations.

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Signpost (not a knowledge pack) for the functional safety standards landscape: IEC 61508, ISO 26262, ISO/SAE 21434, UL 4600, SAE J3016, and ASPICE. Contains NO source content: designation, edition, owner, status, and an official catalogue URL only. Use when you need to identify or locate a functional safety standard. All six are paywalled or carry no redistribution grant and cannot be packaged; open paths point at US Government packs, which are not equivalents.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Performs pathway and gene-set enrichment analysis on gene lists or ranked gene data and interprets the results. Used when the user has a set of genes (differentially expressed genes from PyDESeq2/Scanpy, CRISPR-screen hits, cluster marker genes, proteomics hits) and wants to know which biological pathways, GO terms, or gene sets are over-represented or enriched. Covers over-representation analysis (ORA / Enrichr / Fisher / hypergeometric), ranked Gene Set Enrichment Analysis (GSEA / preranked), single-sample scoring (ssGSEA/GSVA), and functional profiling via gseapy, g:Profiler, Enrichr libraries, MSigDB, GO, KEGG, Reactome, and WikiPathways — plus gene-ID mapping, choosing the right background universe, multiple-testing correction, redundancy reduction, dotplots/enrichment maps, and publication-ready tables. Use this for "pathway analysis", "enrichment analysis", "GO enrichment", "KEGG/Reactome pathways", "GSEA", "over-representation", "functional annotation", or "what pathways are my genes in".

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Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.

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