Results for “features”

Write release notes, changelogs, migration guides, deprecation notices, and version announcements that communicate changes clearly to users and developers. Use when shipping a new version, deprecating features, or guiding users through breaking changes. Also trigger for 'release notes', 'changelog', 'migration guide', 'breaking changes', 'deprecation notice', 'version update', 'what's new', or 'upgrade guide'.

Redis in-memory data store. Covers data structures, caching, and pub/sub. Use for caching and real-time features. USE WHEN: user mentions "redis", "caching", "session storage", "rate limiting", "pub/sub", "sorted sets", "in-memory database", "cache invalidation" DO NOT USE FOR: relational data - use `postgresql` or `mysql` instead, document storage - use `mongodb` instead, full-text search - use `elasticsearch` instead

Capture a visual demo reel (GIF, terminal recording, screenshots) for PR descriptions. Use when shipping UI changes, CLI features, or any work with observable behavior that benefits from visual proof. Also use when asked to add a demo, record a GIF, screenshot a feature, show what changed visually, create a demo reel, capture evidence, add proof to a PR, or create a before/after comparison.

Interpretabilidade e explicabilidade de modelos usando SHAP (SHapley Additive exPlanations). Use essa skill ao explicar predições de modelos de machine learning, computar importância de features, gerar plots SHAP (waterfall, beeswarm, bar, scatter, force, heatmap), depurar modelos, analisar vieses ou justiça de modelos, comparar modelos ou implementar IA explicável. Funciona com modelos baseados em árvores (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), modelos lineares e qualquer modelo black-box.

Java language (17+). Covers modern features, patterns, and best practices. Use when writing Java applications, Spring Boot backends, or enterprise systems. USE WHEN: user mentions "java", "records", "sealed classes", "streams", asks about "pattern matching", "switch expressions", "Optional", "collections", "generics" DO NOT USE FOR: Spring Boot specifics - use `backend-spring-boot` skill instead DO NOT USE FOR: Lombok annotations - use `lombok` skill instead DO NOT USE FOR: MapStruct - use `mapstruct` skill instead

Turn "they bought it but nobody uses it" into a plan to fix it. Reads what is unused inside an account, picks the one habit to build next, and writes the enablement plan that gets there. Built for B2B customer success teams, customizable to your product and your onboarding. Trigger on "drive adoption", "which features are unused", "why aren't they using it", "seats sitting idle", "build an enablement plan", or any account-adoption diagnostic.

Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", or "decompose conditional". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation to apply to a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

Kotest — flexible, idiomatic Kotlin testing framework. Multiple specification styles (StringSpec, FunSpec, BehaviorSpec, DescribeSpec, FeatureSpec, FreeSpec), rich matcher library, property-based testing, data-driven tests, coroutine support, KMP-friendly. Drop-in alternative or complement to JUnit. USE WHEN: user mentions "Kotest", "io.kotest", "shouldBe", "StringSpec", "BehaviorSpec", "DescribeSpec", "kotest property testing", "Arb.list", "forAll", "kotest matchers", "kotlin tests" DO NOT USE FOR: JUnit-specific patterns - use junit skill (or framework-specific test skills) DO NOT USE FOR: Flow testing - use `testing/turbine` DO NOT USE FOR: Compose snapshot tests - use `testing/compose-snapshot` DO NOT USE FOR: Mobile E2E - use `testing/maestro`

Klasy ORM i wzorce kodu biznesowego platformy Soneta (enova365, Soneta Enterprise, Triva). Używaj gdy użytkownik: (1) pisze, modyfikuje lub refaktoruje kod biznesowy enova365/Soneta/Triva (Row/Table/Module, sesje i transakcje, selector, typy wierszy); (2) pyta o konkretny mechanizm ORM — Session, Commit/Save, optimistic lock, Context, RowCondition, Datapack, ViewInfo, Features, thread-safety; (3) prosi o code review kodu biznesowego Soneta (safe-code); (4) pisze worker, extender, akcję w menu Czynności, folder/listę; (5) chce zinwentaryzować moduły, pola, workery lub formularze/zakładki (pola, sekcje, kolejność wprowadzania) z bibliotek DLL; (6) chce rozpocząć nowy dodatek/rozszerzenie Soneta — wygenerować szkielet źródeł z CLI (`dotnet new soneta-addon`, Soneta.MsBuild.SDK, szablony Soneta Platform Developer). Sięgnij też, gdy inny skill potrzebuje warstwy ORM/kodu biznesowego Soneta.

Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC sc