880 件 ・ 関連度順
概要と使いどころ
GEO (Generative Engine Optimization) 引用追踪 + 优化（2026-06-24 标准）。每周对 4 大 AI（Claude/GPT/Perplexity/Gemini） 跑固定查询，检测回答里是否引用目标域名。对未被引用的页面补 Citable Statistics + AI 友好格式（一句话答+表格+FAQ）。 Bing IndexNow 推送确保大模型快速 discover/index。GA4 AI 来源流量监控。 当用户说"我有没有被 AI 引用"、"GEO 优化"、"llms.txt 更新"、"AI 引用追踪"时调用。 默认 B2B/开源；2C 产品（教育/应用）的查询选题与 Citable Stats 来源不同，见 gingiris-seo-geo/references/2c-adaptation.md。
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。
Zustand state management for React and vanilla JavaScript. Use when creating stores, using selectors, persisting state to localStorage, integrating devtools, or managing global state without Redux complexity. Triggers on zustand, create(), createStore, useStore, persist, devtools, immer middleware.
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Sichtbarkeit, Zitierung und Empfehlung in KI-Antwortsystemen für Deutschland und den DACH-Raum evidenzbasiert prüfen und verbessern. Nutze diesen Skill, wenn eine Marke oder Seite in KI-Overviews, ChatGPT, Perplexity, Gemini oder Copilot auftauchen soll, wenn Crawler-Freigaben für KI-Bots, llms.txt, strukturierte Daten oder die Extrahierbarkeit von Inhalten zu bewerten sind, oder wenn jemand nach AEO, GEO oder LLMO fragt. Trennt Abruf, Zitierung, Erwähnung und Empfehlung, prüft Behauptungen über KI-Rankingfaktoren gegen Primärquellen und verspricht keine Sichtbarkeit. Für klassisches technisches SEO ist seo-audit zuständig.
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 or audit B2B sales pipeline — define stage names, entry/exit criteria, qualification standards, and CRM field requirements. Use when asked to "design our pipeline", "audit our CRM stages", "define what qualified means", or "build a sales process".
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。
Expert guide for intelligent model cascading and routing — complexity-scored task routing from Flash/Haiku to Sonnet/Opus/Astra, dynamic escalation with quality gates, 40-60% token cost reduction while maintaining output quality / Panduan ahli untuk kaskade dan routing model cerdas — routing tugas berbasis skor kompleksitas dari Flash/Haiku ke Sonnet/Opus/Astra, eskalasi dinamis dengan gerbang kualitas, pengurangan biaya token 40-60% dengan kualitas output terjaga.
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。
Creates, supersedes and validates decision records (ADRs) against the convention a collection already follows, instead of imposing a published one. Use when the user wants to record a decision, write an ADR, supersede an existing decision, audit or lint a decisions folder, check that an ADR index is in sync, or asks "why did we decide X". Deduces the filename scheme, section set and status vocabulary from the records already there; ships a validator with an exit code, so the audit is a check and not an opinion.
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。
Structured approaches to decisions under uncertainty and complexity. Covers expected value, decision trees, multi-criteria decision analysis, System 1 vs System 2 allocation, pre-mortems, reversible vs irreversible decisions, and the distinction between good decisions and good outcomes. Use when choosing among alternatives with uncertain or multi-dimensional consequences, especially when the stakes justify a deliberate rather than intuitive process.
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。
Algorithms and data structures from first principles through advanced analysis. Covers sorting (bubble, insertion, selection, merge, quick, heap, radix), searching (linear, binary, BFS, DFS, Dijkstra, A*), fundamental data structures (arrays, linked lists, stacks, queues, hash tables, trees, heaps, graphs, tries), complexity analysis (Big-O, Big-Omega, Big-Theta, amortized), recurrence relations, and algorithm design paradigms (divide-and-conquer, greedy, dynamic programming, backtracking). Use when analyzing, selecting, implementing, or comparing algorithms and data structures.
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。
Entrepreneurship, innovation, and disruption for starting, scaling, and defending new ventures. Covers opportunity recognition, jobs-to-be-done, the disruptive innovation framework, sustaining vs disruptive innovation, platform businesses, network effects, minimum viable product, customer development, and the startup lifecycle from idea to exit. Use when evaluating a venture idea, positioning a new product, interpreting a competitive threat, or designing a platform.
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。
Launches and supervises long-running commands — solves, workflows, downloads, builds — detached with a log, PID and exit sentinel, then tells stuck from slow and kills verifiably. Use before running anything that may exceed a minute, for nohup or background launches, and when a run hangs, shows no output, or timed out.
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。
Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.
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。
Systematic code maturity assessment using Trail of Bits' 9-category framework. Analyzes codebase for arithmetic safety, auditing practices, access controls, complexity, decentralization, documentation, MEV risks, low-level code, and testing. Produces professional scorecard with evidence-based ratings and actionable recommendations. (project, gitignored)
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。
Code refactoring patterns and techniques for improving code quality without changing behavior. Use for cleaning up legacy code, reducing complexity, or improving maintainability.
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。
Lee Cronin's Assembly Theory for molecular complexity measurement and life detection via assembly index computation.
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。
ABD quorum-based linearizable registers for distributed storage — the canonical crash-tolerant read/write protocol with precise RTT and message complexity.
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 checking skill quality, validating frontmatter before commit, or diagnosing validator warnings. Runs the plugin validator on a skill, agent, or plugin directory — reports token complexity, broken links, frontmatter issues, and structural problems. Pass the path as an argument.
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。
Cross-platform hooks reference for AI coding assistants — Claude Code, OpenAI Codex, GitHub Copilot, Cursor, Windsurf, and Amp. Covers hook authoring in Node.js (ESM and CJS) and Python, per-platform event schemas, inline-agent hooks and MCP in agent frontmatter, common JSON I/O, exit codes, best practices, and a fetch script to refresh docs from official sources. Use when writing, reviewing, or debugging hooks for any AI assistant.
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。
Hook JSON input/output API reference — what data hooks receive via stdin and what JSON they can return to control Claude Code behavior. Use when writing hook scripts, checking exit code behavior, building JSON output for PreToolUse permissions, or understanding event-specific input schemas.
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 a diff needs four parallel perspective reviewers (Security, Performance, Quality, Accessibility). Creates an ephemeral SAM plan, collects structured verdicts, synthesizes them into one deduplicated cross-referenced punch list, prints one summary line per perspective, and exits non-zero if any perspective returns REJECT. SKIP is a passing outcome.
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。
Reviews CLI application code for correctness and quality. Use when reviewing tools that use argparse, click, typer, commander.js, or similar argument parsers — covers exit codes, help flags, stdin/stdout/stderr separation, non-interactive operation, signal handling, argument validation, ANSI color safety, and dry-run support for destructive operations.
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。
When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent," "popup conversions," "modal optimization," "lead capture popup," "email popup," "announcement banner," or "overlay." For forms outside of popups, see form-cro. For general page conversion optimization, see page-cro.
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。
Apply systematic problem-solving techniques for marketing challenges including campaign complexity (simplification cascades), creative blocks (collision-zone thinking), recurring campaign patterns (meta-pattern recognition), assumption constraints (inversion exercise), audience scale uncertainty (scale game), and dispatch when stuck. Techniques derived from proven problem-solving frameworks adapted for marketing execution.
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。
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
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。
Analyze code coverage and CRAP (Change Risk Anti-Patterns) scores to identify high-risk code. Use OpenCover format with ReportGenerator for Risk Hotspots showing cyclomatic complexity and untested code paths.
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。