502 件 ・ 関連度順
概要と使いどころ
Create professional infographics using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3.6 Flash for quality review. Integrates research-lookup and web search for accurate data. Supports 10 infographic types, 8 industry styles, and colorblind-safe palettes.
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。
Structure software around the Dependency Rule: source code dependencies point inward from frameworks to use cases to entities. Use when the user mentions "architecture layers", "dependency rule", "ports and adapters (hexagonal)", "onion architecture", "screaming architecture", "where should business logic go", "decouple from the database", "swap the framework without a rewrite", or "keep business rules independent". Also trigger when deciding which layer code belongs in, isolating core logic from infrastructure, defining module boundaries, or debating whether the framework should call your code or the reverse. Covers component principles, boundaries, and SOLID. For code-level quality, see clean-code. For domain modeling, see domain-driven-design.
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。
Guided journey from an app idea to a deliberate architecture: boundaries, domain model, data decisions, and resilience, making only the expensive-to-reverse decisions and deferring the rest. Orchestrates eight skills phase by phase - clean-architecture, domain-driven-design, system-design, ddia-systems, software-design-philosophy, release-it, pragmatic-programmer, 37signals-way - asking the user questions at every decision point and recording results in the project docs/ folder (ARCHITECTURE.md, DESIGN-CODE-ARCHITECTURE-PLAN.md) so the journey resumes across sessions. Use when the user wants to design a new app's architecture, choose boundaries and a domain model, decide monolith versus microservices, or says 'how should I structure this app'. If a codebase already exists, use remove-technical-debt (aged), improve-code-quality (fresh prototype), or architecture-optimization (slow but working); if unvalidated, run create-business or create-app first. For one framework in isolation, invoke that skill directly.
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。
Post-writing SEO validation with pass/fail checklist covering title tag length and keyword placement, meta description quality, heading hierarchy and keyword density, internal/external link audit with anchor text analysis, canonical URL verification, Open Graph meta tags (og:title, og:description, og:image), Twitter Card validation, structured data presence and validity, URL structure optimization, and image alt text presence. Produces prioritized fix list with specific recommendations. Use when user says "seo check", "check seo", "validate seo", "blog seo", "seo validation", "on-page seo", "title tag check", "meta description check", "heading check", "link audit".
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。
Process CRISPR screening data to identify essential genes and hit candidates. Performs quality control, statistical analysis (RRA), and hit calling for pooled CRISPR screens including viability screens and drug resistance/sensitivity studies.
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。
Detect copy number variations from whole genome sequencing data and generate publication-quality genome-wide CNV plots. Supports CNV calling, segmentation, and visualization for cancer genomics and rare disease 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。
Generates the Methods section for a meta-analysis paper, including search strategy, screening, quality assessment, data extraction, and statistical 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。
Designs retrospective or prospective clinical cohort study protocols for biomedical and clinical research. Always use this skill when the user needs a cohort-based study plan rather than a general study idea, evidence summary, or mechanistic experiment design. Focus on cohort appropriateness, enrollment logic, baseline time-zero definition, follow-up structure, endpoint definition, variable collection, confounding control, and a coherent primary statistical analysis line. Do not invent data availability, follow-up completeness, outcome ascertainment quality, sample size adequacy, or causal interpretability.
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。
Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
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。
Applies proven testing patterns — Arrange-Act-Assert (AAA), Given-When-Then, Test Data Builders, Object Mother, parameterized tests, fixtures, spies, and test doubles — to help write maintainable, reliable, and readable test suites. Use when the user asks about writing unit tests, integration tests, or end-to-end tests; structuring test cases or test suites; applying TDD or BDD practices; working with mocks, stubs, spies, or fakes; improving test coverage or reducing flakiness; or needs guidance on test organization, naming conventions, or assertions in frameworks like Jest, Vitest, pytest, or similar.
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。
Tech debt assessment that identifies, classifies, and prioritizes maintainability problems — helping teams build a refactoring roadmap — drawing on twelve classic engineering books. Triggers when: user asks about tech debt, refactoring priorities, what to clean up first, or asks "why is this so hard to change?", "what should we fix first?", or "how do I justify refactoring to management?". Do NOT trigger for: server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime — "health" in those contexts is infrastructure, not code quality. Also not for single-function refactoring questions.
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。
Use when asked 质量追溯体系怎么建, 产品追溯方案, 批次追溯, 一物一码, 客户要求可追溯性, 召回演练, 正向追溯和反向追溯, or plan product and batch traceability in a Chinese factory. Produces a traceability plan in Chinese: the trace unit and coding rule, the data to capture at each process step (material lots, work orders, equipment, people, inspection results), forward and backward trace paths, record retention, a mock recall drill with a target time, gaps against the current system, and a phased rollout, with industry rules (food, medical devices, automotive) marked to confirm.
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。
Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, automated + human evals, a pass bar, and a regression gate.
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。
Help users build a professional eye for quality, articulate the reasoning behind successful product paradigms, and develop the conviction to make high-stakes decisions before data is available.
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 generated or changed WordPress code — plugins, themes, and blocks — before it ships. Best used reactively after an agent writes, edits, or reviews code touching WordPress APIs: add_action/add_filter, shortcodes, meta boxes, AJAX handlers, REST routes, WP_Query or $wpdb, widgets, or WP-CLI commands. Use on 'review this plugin', 'is this safe to ship', 'make this translatable', 'speed up this query', or after tasks like 'write a plugin' or 'add an endpoint/shortcode/meta box'. Enforces escaping and sanitization, nonces plus capability checks, prepared database queries, core-API-first development, translation-ready strings, and query/caching discipline. DO NOT USE for WooCommerce-specific order, product, or checkout logic (use woo-guard), non-WordPress PHP, generic code quality review (use clean-code-guard), test code review (use test-guard), server or hosting configuration, or conceptual WordPress questions.
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。
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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。
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
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。
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract) with SES scaling, k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
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。
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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。
Use when auditing CloudBase cloud API wrappers, MCP tools, generated action metadata, or related docs for outdated or incorrect action names, parameters, casing, request shapes, or missing contract tests, especially during periodic quality review or before preparing corrective PRs.
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。
Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.
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。
Generate and render-verify publication-quality mathematical-modeling figures from saved evidence, using the approved figure plan, source data, claim, type, and consistent visual system.
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。
Use when building or fixing game UI in Unity 6.3: main menu, settings screen (resolution, quality, volume, key rebinding), HUD, health bar, minimap frame; choosing uGUI or UI Toolkit or mixing both; 'UI scales wrong on phones', portrait to 4K, Canvas Scaler, anchors, layout groups collapsing, sort order, UXML/USS, data binding, TextMeshPro or UI Toolkit fonts missing glyphs, localized menus, gamepad navigation, UI draw calls, canvas rebuild spikes, UI tests, or capturing UI to check it.
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。
How to design and run a programmatic SEO program that produces durable traffic instead of penalty-bait. Data source identification, template design, schema patterns, quality control at scale, internal linking architecture, crawl budget management, AEO/GEO for programmatic pages, refresh discipline, and the make-or-break question of whether pSEO is the right answer for your program at all. Triggers on programmatic SEO, pSEO, scaled content, page generation, template SEO, location pages, comparison pages, directory site, listing site, scaled landing pages, programmatic content. Also triggers when a content set is not ranking, has been hit by an algorithm update, or when a team is considering pSEO as a growth lever.
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。