「with」の検索結果

Use when the user asks to "build our social posting calendar", "set weekly slots and queue depth per channel", or "plan the evergreen recycle rotation"; produces the always-on brand calendar — pillar allocation with hero/hub/help balance and give:ask targets (all labeled Estimated starting heuristics to calibrate against own analytics, never scored rules), per-channel recurring slots with queue depth, a batching workflow, an evergreen recycle cycle with freshness re-checks, deliberate open slots for realtime/trend moments fed by trend-spotter go/skip verdicts, and first-comment/publish-order ops — cadence commitments filed to the channel registry, and no batch ships to the queue without a SHIP verdict from social-quality-auditor pre-publish mode. Not for repurposing an existing asset or the paid-amplification calendar — use content-amplifier. 社媒排期/内容日历/栏目配比/常青循环复用

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新規・変更 skill の fixture/eval と、WITH/WITHOUT の paired ablation による限界寄与の評価可能性を確認し、activation と effectiveness を分離して案内する。

Provider (v6.x) state management for Flutter using ChangeNotifier and InheritedWidget with MVVM architecture pattern. Use this skill when implementing Provider-based state management, using Consumer/Consumer2 widgets for targeted rebuilds, accessing state with context.read/context.watch/context.select, setting up ChangeNotifierProvider or StateNotifierProvider, configuring MultiProvider for multiple providers, implementing ProxyProvider for dependent state, using Selector for optimized rebuilds, migrating from StatefulWidget to reactive state, maintaining legacy Provider codebases, debugging provider not found errors, preventing memory leaks with proper ChangeNotifier disposal, fixing context access issues in initState, or resolving .value constructor anti-patterns. Essential for apps with shared state across widgets (shopping carts, authentication, global settings, form state, theme management), when teaching state management fundamentals, or when debugging memory leaks and resource cleanup in Provider-based applications.

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Implements type-safe Flutter routing using AutoRoute v11 with code generation and @RoutePage annotations. Activates when setting up AutoRoute navigation, configuring AutoRouterConfig or RootStackRouter, generating route files with build_runner, using @RoutePage, @PathParam, or @QueryParam annotations, implementing AutoTabsRouter for bottom navigation tabs, adding navigation guards with AutoRouteGuard, setting up nested or child routes, handling type-safe route parameters, or migrating from string-based Navigator routing to compile-time-verified routes. Ideal for large apps requiring refactoring confidence and zero string-based routing errors.

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Manages local UI state and side effects in Flutter using the flutter_hooks package (v0.21.x) with HookWidget, useState, useEffect, useMemoized, useAnimationController, and custom hooks. Use this skill when replacing StatefulWidget boilerplate with hooks, reusing stateful UI logic across widgets, managing controller lifecycles (TextEditingController, AnimationController, ScrollController) without manual dispose, implementing countdown timers or subscriptions in widgets, mixing flutter_hooks with Riverpod via HookConsumerWidget, or any time the words 'flutter hooks', 'useEffect', 'useState', 'HookWidget', or 'flutter_hooks' appear in the request.

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Implements type-safe reactive SQL persistence in Flutter using Drift v2.32 (formerly Moor) built on SQLite with automatic code generation. Activates when defining table schemas with Drift DSL, writing type-safe join or subquery operations, handling schema migrations with MigrationStrategy, using reactive watch() streams for live UI updates, implementing ACID transactions, defining foreign key constraints or cascade deletes, enabling WAL mode for write performance, troubleshooting generated .g.dart type mismatches, migrating from sqflite or sqlite3 v2 to v3, using DAOs for query separation, or building offline-first apps requiring strong relational data integrity and compile-time SQL verification.

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Builds native iOS apps in Swift — SwiftUI and UIKit screens, state and data flow, NavigationStack, lists, forms and accessibility; networking with URLSession and Codable, persistence with SwiftData, Core Data, files and the Keychain; permissions, push notifications, universal links, background tasks, widgets, App Intents and StoreKit 2 purchases; performance with Instruments and MetricKit; and release with signing, TestFlight and App Store Connect. Use when writing or changing native iOS code, adding a platform feature, preparing a build for TestFlight or the App Store, or when the user asks how to do something on iOS, even if they do not name the framework.

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Marp形式プレゼンテーションの専門レビュー・評価・改善スキル。 10軸スコアリング（100点満点）で品質を定量評価し、具体的な改善コード付きで提案する。 Use when the user asks to: - Review, evaluate, or critique a Marp presentation / プレゼンをレビュー / 評価 / 添削 - Improve, polish, or upgrade existing slides / スライドを改善 / ブラッシュアップ - Check slide quality, score slides, or audit a deck / スライドの品質チェック / 採点 - "このプレゼンどう？" "スライドを見てほしい" "プレゼン資料のフィードバック" Reads .md files with Marp frontmatter, scores across 10 dimensions, and outputs a detailed review report with concrete before/after code improvements. Works as a companion to the marp-slides skill (generation → review → improvement cycle).

Use OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.

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Guide for organizing MSBuild infrastructure with Directory.Build.props, Directory.Build.targets, Directory.Packages.props, and Directory.Build.rsp. Only activate in MSBuild/.NET build context. USE FOR: structuring multi-project repos, centralizing build settings, implementing NuGet Central Package Management (CPM) with ManagePackageVersionsCentrally, consolidating duplicated properties across .csproj files, setting up multi-level Directory.Build hierarchy with GetPathOfFileAbove, understanding evaluation order (Directory.Build.props → SDK .props → .csproj → SDK .targets → Directory.Build.targets). Critical pitfall: $(TargetFramework) conditions in .props silently fail for single-targeting projects — must use .targets. DO NOT USE FOR: non-MSBuild build systems, migrating legacy projects to SDK-style (use msbuild-modernization), single-project solutions with no shared settings. INVOKES: no tools — pure knowledge skill.

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Search, scrape, and interact with the web via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, get data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference external websites. This provides real-time web search with full page content and interact capabilities — beyond what Claude can do natively with built-in tools. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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Implement, review, or improve widgets, Live Activities, and controls using WidgetKit and ActivityKit. Use when building home screen, Lock Screen, or StandBy widgets with timeline providers; when creating interactive widgets with Button/Toggle and AppIntent actions; when adding Live Activities with Dynamic Island layouts (compact, minimal, expanded); when building Control Center widgets with ControlWidgetButton/ControlWidgetToggle; when configuring widget families, refresh budgets, deep links, push-based reloads, or Liquid Glass rendering; or when setting up widget extensions, App Groups, and entitlements.

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Write and migrate tests using the Swift Testing framework with @Test, @Suite, #expect, #require, confirmation, parameterized tests, test tags, traits, withKnownIssue, XCTest UI testing, XCUITest, test plan, mocking, test doubles, testable architecture, snapshot testing, async test patterns, test organization, and test-driven development in Swift. Use when writing or migrating tests with Swift Testing framework, implementing parameterized tests, working with test traits, converting XCTest to Swift Testing, or setting up test organization and mocking patterns.

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Implement, review, or improve accessibility in iOS/macOS apps with SwiftUI and UIKit. Use when adding VoiceOver support with accessibility labels, hints, values, and traits; when grouping or reordering accessibility elements; when managing focus with @AccessibilityFocusState; when supporting Dynamic Type with @ScaledMetric; when building custom rotors or accessibility actions; when auditing a11y compliance; or when adapting UI for assistive technologies and system accessibility preferences.

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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

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Vendor (copy-import) a single external Claude Code skill into your own skills repo correctly — with license compliance and full provenance/attribution. Deterministic, idempotent primitive: given an upstream ref (owner/repo/path@sha or a GitHub URL) it fetches the skill, detects and gates on the license, copies the folder, injects a provenance frontmatter block (license + metadata: author/source/upstream_commit/vendored_at/provenance), ships the upstream LICENSE, adds a ## Credits section, and records a CREDITS.md ledger entry. Use when the user asks to "vendor a skill", "import a skill", "clone a skill", "borrow someone's skill", "add an external skill with attribution/credit", or スキルを取り込む / vendoring / 他人のスキルを自分のリポに取り込む. This is the import primitive that a skill-scout/curator orchestrator calls; it does NOT discover or evaluate — it correctly imports ONE skill you already chose.

Hand off a task with only the context needed to continue in a fresh session. Use for "handoff", "引き継ぎ", "別セッションへ渡す", or "別マシンで続ける". The orca subcommand launches an independent Orca workspace or terminal tab; without it, produce a paste-ready prompt backed by a verified remote branch or PR. Do not launch sessions for capability questions or design requests.

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Assigns pathogen lineages (SARS-CoV-2 Pangolin via UShER mode; Nextclade clade + QC; pango-designation alias_key.json resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage fitness modelling (Wenseleers / Bedford-Figgins multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data version pinning (mandatory for reproducibility), Nextclade dataset versioning (lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme version churn (V3 / V4 / V4.1 / V5.3.2 / Midnight 1200) with documented dropout regions, recombinant X-prefix Pango designation lag, GISAID vs INSDC dual-deposition tensions, and the Karthikeyan 2022 wastewater early-detection signal with explicit reproducibility caveats. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater pooled samples into lineage frequencies with Freyja, tracking lineage frequencies and growth advantages over time, pinning pangolin-data / Nextclade dataset versions for reproducibility, handling ARTIC primer dropouts (V4.1 amplicons 64 / 76 / 88-90), or running variant surveillance for SARS-CoV-2 / influenza / Mpox / RSV / H5N1 / measles.

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Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded databases.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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。