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概要と使いどころ
Constructs secure, efficient CI/CD pipelines with supply chain security (SLSA), monorepo optimization, caching strategies, and parallelization patterns for GitHub Actions, GitLab CI, and Argo Workflows. Use when setting up automated testing, building, or deployment workflows.
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Rewrite and audit SEO page titles, title tags, MDX frontmatter titles, H1 alignment, and bulk title improvements. Use when the user mentions titles, title tags, SERP titles, metadata titles, SEO titles, CTR, truncation, or asks to improve titles across pages.
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。
Guide for diagnosing and improving MSBuild project evaluation performance. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems.
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。
Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy 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。
Guides creation and modification of ASP.NET Core Web API endpoints with correct HTTP semantics, OpenAPI metadata, and error handling. USE FOR: adding new API endpoints (controllers or minimal APIs), wiring up OpenAPI/Swagger, creating .http test files, setting up global error handling middleware. DO NOT USE FOR: general C# coding style, EF Core data access or query optimization (use optimizing-ef-core-queries), frontend/Blazor work, gRPC services, or SignalR hubs.
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。
JD优化与简历匹配技能。针对招聘需求补充和优化JD内容，包含显性JD文本和隐性匹配规则两部分，支持多轮沟通确认优先级和权重。
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。
Automatically generate production-ready Skills for any software, API, CLI tool, library, workflow, or service. Use this skill whenever the user wants to create a skill from scratch for a target application, convert an existing tool into an agent-native skill, generate skills for multiple platforms (Claude Code, OpenClaw, Codex), or automate the full skill creation pipeline including analysis, design, implementation, testing, optimization, and multi-platform packaging. Also use when the user mentions "skill-anything", "generate a skill for", "make a skill from", "skillify", or wants to turn any software into an agent-ready skill. Even if they just say "create a skill for X" where X is any tool or API, this skill should trigger.
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。
Comprehensive SEO/GEO/AEO analysis toolkit for optimizing content visibility across traditional search engines (Google, Bing), AI platforms (ChatGPT, Perplexity, Claude, Gemini, Grokipedia), answer engines (Google AI Overviews, Bing Copilot, featured snippets), voice assistants (Google Assistant, Siri, Alexa), and social media (Facebook, Twitter, LinkedIn, WhatsApp, Instagram). Analyzes HTML/Markdown/JSX files for metadata completeness, schema markup, keyword optimization, entity extraction, and generates multi-format audit reports with platform-specific recommendations.
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。
Optimize title tags, meta descriptions, Open Graph, and Twitter cards for maximum click-through rate. Generates multiple A/B test variations with character counting and SERP preview. Use when asked to "optimize title tag", "write meta description", "improve CTR", "Open Graph tags", "fix my meta tags", "social media preview", "my click rate is low", "title optimization", or any meta tag task.
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。
Entry point + orchestrator for the recomby-geo GEO (Generative Engine Optimization) workflow on OpenAI Codex CLI. Use when the user wants to run any stage of the GEO pipeline on a client folder — intake, visibility audit, content-gap analysis, content brief, draft production, distribution, or monthly re-audit — or asks to "run GEO", "audit AI search visibility", or "GEO this client". Codex has no bare slash commands, so this skill is how the 7 stages (that Claude Code runs as /01-intake … /07-reaudit) are driven on Codex. It routes to the per-stage specs in this plugin's commands/ and enforces the orchestration rules. Does not auto-fill expert content — the human-in-loop brief checkpoint is the moat.
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 you need to verify Java performance optimizations by comparing profiling results before and after refactoring — including baseline validation, post-refactoring report generation, quantitative before/after metrics comparison, side-by-side flamegraph analysis, regression detection, or creating profiling-comparison-analysis and profiling-final-results documentation. This should trigger for requests such as Verify performance fix; Verify the performance; Verify the memory; Verify the threading; Compare before and after profiling results; Check for performance regressions after refactoring. Part of Plinth Toolkit
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 you need to refactor Java code for high performance — including memory/allocation reduction, CPU hot-path optimization, and syntax/API/control-flow improvements. This should trigger for requests such as Review Java code for high performance; Optimize Java hot path; Reduce Java allocations; Improve Java latency/throughput. Part of Plinth Toolkit
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。
SEO analysis and optimization specialist. Use when conducting technical SEO audits, optimizing meta tags, analyzing Core Web Vitals, or improving search engine rankings. Focuses on actionable recommendations.
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。
CLI command development specialist. Use when creating commands, designing argument parsing, automating tasks, or implementing CLI best practices. Specializes in command design patterns and user experience.
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。
Developer Experience specialist for tooling, setup, and workflow optimization. Use when setting up projects, reducing friction, improving development workflows, or automating repetitive tasks. Focuses on making development joyful and productive.
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。
Core planning principles for Portfolio Buddy 2 development. Use when: planning any feature implementation, modification, or refactoring. Ensures code preservation, mobile/desktop optimization, and thorough requirement gathering.
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。
Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.
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。
The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).
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。
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
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。
Reinforcement learning for trade execution optimization including order splitting, adaptive timing, and impact minimization
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。
Jito bundle submission for MEV protection on Solana — bundle building, tip strategies, block engine endpoints, and landing rate optimization
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。
ML trading signal classifiers using XGBoost and LightGBM with walk-forward validation, SHAP feature importance, and threshold optimization
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。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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。
Specialized skill for nondominium Holochain DNA development, focusing on zome creation, entry patterns, integrity/coordinator architecture, ValueFlows compliance, and WASM optimization. Use when creating new zomes, implementing entry types, or modifying Holochain DNA code.
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。