データ分析、機械学習、LLMアプリ、プロンプト設計（8,912 件）
概要と使いどころ
Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
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。
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
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。
Fetches tweets from Twitter List, scores them using data metrics and AI analysis, and generates topic recommendation reports for content creators.
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。
On-Chain Analytics Agent — whale movements, exchange flows, active addresses, network growth, holder distribution, and transaction metrics with On-Chain Score (0-100)
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。
当用户要写出高质量 Goal Prompt，把模糊、战略性、多步骤、证据不足、持续/自动化或容易跑偏的请求整理成可执行、可验证、可暂停的目标契约时使用。适用于写 goal、优化任务提示词、明确 done/success criteria、deep research 后定战略、大改前 inventory、修复跑偏计划、识别可委托的重复 workflow、为 Codex 或 Claude Code 准备执行任务；默认一次性交付只生成 Goal Prompt，只有存在真实后续迭代需求时才追加 Loop Prompt，不执行 goal 或 loop，也不创建自动化。
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。
Pre-landing PR review. Analyzes diff against the base branch for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues.
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 the user wants to set up the evolver in their project, optimize an LLM agent, improve agent performance, or mentions evolver for the first time in a project without .evolver.json.
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 a plug-in-ready SDR operator package for sales development and outbound pipeline work: ICP definition, compliant lead-source plan, enrichment/research workflow, account scoring, outbound assets, CRM handoff, reply triage, meeting prep, approval gates, and loop memory. Use when "SDR", "sales development", "outbound SDR", "operator room", "lead gen pipeline", "prospecting pipeline", "ICP targeting", "Apify", "RapidAPI", "Clay", "Apollo", "agentic SDR workflow", "sales operating loop", "sales dashboard handoff", or any request to package outbound sales workflows that can later connect to data tools, CRMs, or operator surfaces.
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 and maintain a provenance-clean proof library — inventory every real proof asset (analytics, reviews, permitted testimonials, case studies, press, certifications), categorize it, rewrite each cleared item in 3 lengths, fuse top assets with narrative, and gate everything through FTC testimonial rules before it can ship. Use when "proof library", "gather our proof", "authority builder", "collect testimonials", "social proof assets", "proof points for the sales page", "what results can we claim", or any request to assemble evidence for marketing claims. NEVER invents proof — missing proof is reported as a gap.
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。
LLM brand manipulation — build a consensus web so ChatGPT, Claude, Perplexity, and Google AI Overviews mention your brand when people ask about your category. Uses surround sound methodology, entity SEO, and LLM citation science. Use when "get mentioned in AI", "LLM brand presence", "surround sound", "AI search visibility", "Perplexity ranking", "ChatGPT mentions", "AI Overview inclusion", "entity authority", "brand mentions in AI", or any request to influence how AI systems perceive and recommend your brand.
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。
Analytics and attribution setup — tracking plan, UTM conventions, dashboard design, and attribution model selection. Use when "analytics setup", "attribution", "tracking plan", "UTM", "marketing analytics", "dashboard setup", "measurement strategy", "how do I track", "which metrics", or any request to set up or improve marketing measurement and attribution.
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。
Pull performance data for published content across every surface — LinkedIn, Dev.to, Google Search Console, GA4, and AI citation tracking (Perplexity, Anthropic, OpenAI). Grades each piece as winner, average, or underperformer and surfaces whether an AI search engine has cited 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。
Suggest content topics ranked by opportunity score. Analyzes GSC keyword data and cross-references with personas to find high-potential topics.
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 a strategic content brief from format + site + keyword. Auto-resolves persona, pulls GSC data, generates hooks, angles, and competitor weakness via LLM.
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。
Convert Kai workflow data, CSV exports, audit folders, SDR package outputs, and marketing reports into dashboard-ready specs or lightweight static dashboards. Use when "data dashboard", "operator dashboard", "operator room", "HTML operators room", "sales dashboard", "SDR dashboard", "turn this data into a dashboard", "dashboard handoff", "visualize Kai data", or any request to package sourced marketing, sales, audit, or SDR data for a dashboard or presentation surface.
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。
弗洛伊德.skill：给AI做心理分析的认知调优系统。基于Anthropic AI心理学研究（Persona Selection Model、全局工作空间/J-lens、Emotion Concepts、Introspective Awareness等7篇论文）。 两种模式：(1)执行增强——处理复杂任务前给AI做「认知准备」，像心理咨询师让来访者进入最佳状态；(2)诊断优化——用精神分析手法揭示prompt/skill/系统提示的身份冲突、规则堆砌、隐含矛盾等深层毛病，并重写。 当用户提到"优化表现"、"调优"、"输出不够好"、"风格不稳定"、"persona"、"认知配置"、"优化prompt"、"优化skill"、"弗洛伊德"、"心理分析"、"给AI看心理医生"、"给prompt做体检"时使用。 当用户开始重要/复杂任务但方向不明确、觉得之前的输出质量不够好、或想理解为什么某个prompt/skill有效或无效时，也应主动考虑使用。 即使用户只是说"帮我想想怎么做更好"、"这个结果不对味"、"为什么效果不好"也应触发。
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。
Search and read locally-stored Claude documentation covering Claude Code CLI, Claude API (Messages, tool use, vision, streaming, batch), Agent SDK (Python and TypeScript), prompt engineering, and all Anthropic platform docs. Use this skill whenever the user asks about Claude Code features (hooks, MCP servers, skills, plugins, settings, permissions, keybindings, sub-agents), the Anthropic API or any of its SDKs (Python, TypeScript, Go, Java), the Agent SDK (sessions, hooks, custom tools, MCP), model capabilities (context windows, extended thinking, pricing, rate limits, vision), prompt engineering best practices, or troubleshooting any Claude-related error. This skill provides instant access to official documentation files without web searches — always prefer it over web lookups for Claude and Anthropic topics.
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 an interactive, self-contained HTML course on any Claude documentation topic. Use this skill when the user wants to create an interactive course, tutorial, or deep-dive walkthrough about a Claude feature, API concept, SDK pattern, or prompt engineering technique. Triggers on: "create a course about", "interactive tutorial for", "teach me about X interactively", "make a course on", "I'd like a course", "/docs --course", "/docs course", or when the user accepts the post-docs course prompt. Produces a stunning single-page HTML file with scroll-based navigation, animated visualizations, quizzes, and code translations drawn from official Claude documentation.
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。
Work with a running QDash instance through qdash-client and saved local profiles. Use when an agent needs to inspect chips, metrics, task results, calibration data, provenance, issues, flows, executions, project files, project-scoped QDash API data, or OpenAPI endpoints from QDash using qdash-client, ~/.config/qdash/config.ini, XDG_CONFIG_HOME/qdash/config.ini, QDASH_* environment variables, or a local oqtopus-team/qdash checkout.
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。
Enforce the project's documentation voice when writing or reviewing public-facing content. Triggers on edits to doc files (packages/web/src/content/docs/**/*.md) and README.md. Catches LLM prose patterns, marketing language, and messaging drift.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。
Migrates Odoo QWeb HTML and PDF reports between major versions while preserving data semantics, access behavior, paper geometry, and intended visual appearance. Use when porting reports across Odoo versions, adapting report templates or assets after an upgrade, or diagnosing visual regressions in migrated PDFs.
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。
跨版本符号迁移与二进制差分。当你有旧版本的符号/逆向结果，需要快速迁移到新版本时使用。 适用场景：内核缺 PDB 用旧版符号推导、程序更新后批量迁移函数名、游戏反作弊更新后快速定位新偏移。 核心方法：用 LLM 做结构化差异比对，程序化输入输出，成本极低（200 函数 ~1 元）。 触发关键词：符号迁移、bindiff、跨版本、PDB 缺失、函数偏移迁移、symbol migration、binary diff、版本对比。
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for SSR, template rendering, route loaders, hydration payloads, server-client render boundaries, and template-to-handler enforcement gaps. Use when the user asks to inspect SSR or template routes, trace render context or hydration data, compare template gating with handler enforcement, explain preview or hidden-route rendering, or connect render pipeline behavior to the decisive branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。