Word・Excel・PowerPoint・PDF、議事録、マニュアル（3,354 件）
概要と使いどころ
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
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。
Keep README files current with project changes. Use when project structure changes, features added, or setup instructions modified. Suggests README updates based on code changes. Triggers on significant project changes, new features, dependency changes.
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。
Auto-generate API documentation from code and comments. Use when API endpoints change, or user mentions API docs. Creates OpenAPI/Swagger specs from code. Triggers on API file changes, documentation requests, endpoint additions.
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。
Mention/keyword sweep on social platforms for [REPLACE: KEYWORDS] — trends, sentiment, top posts
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。
Update developer-facing documentation to reflect code changes on the current branch. Runs as an inline skill inside backend-agent and frontend-agent (step 2.5 of the internal sequence) after implementation and before DOD. Receives the explicit list of changed files from the implementation agent. No-op if no public API changes occurred.
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。
Check a change against WordPress.org plugin rules and PHPCS standards.
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 this skill when modifying templates, admin UI, output, hooks, plugin metadata, sanitization, escaping, or any code that must remain compliant with WordPress.org and repository PHPCS rules.
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 this skill when adding or modifying WordPress abilities
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。
Decides which named technologies a company actually runs on its website, verified against the live site. Triggers on "find companies using Shopify", "they're on HubSpot", "tech stack targeting", "technographics", "who uses Klaviyo", "companies running WordPress". Outputs a per-domain verdict (tech_confirmed plus the verified stack and its evidence); the copy-ready clause is OFF by default and runs only when the operator asks for one.
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。
Always-on spam and deliverability guardrails for cold email copy. Apply these rules whenever writing, reviewing, or QA-ing subject lines, openers, follow-ups, CTAs, or any cold outreach copy. Trigger on phrases like "check this for spam", "spam word check", "deliverability review", "QA this copy", "flag banned words", "check subject line", "is this safe to send", "scan for spam triggers", or any time cold email copy is being generated or reviewed. Also trigger automatically as a background rule whenever the campaign-copywriting or cold-email-copywriting skill is active -- these guardrails apply to EVERY piece of outbound copy generated, even if the user does not explicitly ask for a check.
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。
Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".
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 near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".
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。
Checks whether a company's own website mentions a specific keyword, using one simple Google site: query per keyword through a SERP API, and turns the hit into a copy-ready line. Triggers on "does their site mention X", "site: search", "find companies whose website says X", "filter this list by a keyword on their site", "check if they talk about X", "google search enrichment". Outputs site_keyword_hit plus a one-clause personalization line with an evidence URL.
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。
Amazon backend search term optimization and strategy. 250-byte limit management, keyword deduplication, prioritization frameworks, and search visibility optimization. Use when the user asks about backend keywords, search terms, Amazon SEO, or keyword 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。
Amazon keyword position monitoring and SERP analysis. Real-time position tracking, competitor movement alerts, search result changes, and keyword performance optimization. Use when the user asks about keyword monitoring, SERP tracking, position alerts, or search performance 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。
Category and brand ungating — requirements, documentation, appeal process, restricted categories guide
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。
Amazon keyword research and market opportunity analysis for sellers. Retrieve autocomplete suggestions (long-tail keywords), analyze competitor landscape, and assess market opportunity for any keyword on 12 Amazon marketplaces (US/UK/DE/FR/IT/ES/JP/CA/AU/IN/MX/BR). No API key required. Make sure to use this skill whenever the user mentions Amazon product research, finding products to sell on Amazon, Amazon keyword ideas, niche analysis, competition analysis for Amazon, market opportunity on Amazon, comparing Amazon keywords, evaluating whether a product is worth selling, Amazon autocomplete data, seasonal demand for Amazon products, or anything related to researching what to sell on Amazon — even if they don't explicitly say 'keyword research'. Also trigger when the user asks vague questions like 'is this a good product to sell?', 'what's the competition like for X on Amazon?', 'should I sell X or Y?', or 'what are people searching for on Amazon?'.
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。
Amazon negative keyword management and optimization. Search term analysis, waste reduction, savings estimation, and automated negative keyword strategies. Use when the user asks about negative keywords, search term analysis, ad waste reduction, or PPC 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。
Product compliance and safety — certifications, labeling requirements, restricted substances, 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。
One sentence that describes WHEN to invoke this skill. Write it as a trigger phrase, not a summary — include the words a user would say ("fix bug", "add feature", "review diff"). The agent routes on this field.
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。
Get a PDF into the model without blowing the context window or losing structure. Native PDF beats OCR-then-text for most cases; extract-then-summarize beats native for very long docs.
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。
PDF 数据提取工具。当用户提到"PDF 提取"、"PDF 转 Markdown"、"PDF 解析"、"提取 PDF 内容"、"PDF 转 JSON"、"RAG PDF"时使用。OpenDataLoader PDF 是目前基准测试第一的 PDF 解析器，支持本地模式（快速、确定）和混合 AI 模式（复杂表格、扫描件、公式），输出 Markdown、JSON（带边界框）、HTML。适用于需要从 PDF 提取结构化数据用于 RAG/LLM pipeline，或需要批量处理 PDF 文档的场景。
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。
文章解读助手。对用户提供的文章（URL、Markdown、PDF、粘贴文本）进行结构化解读，输出包含总览、关键要点、个人启发、批判性思考和延伸思考的 Markdown 报告。触发词：文章解读、解读文章、帮我解读、读一下这篇、分析这篇文章、article interpretation。
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。
Read one conversation window and return every independently mutable claim it states, one record each, with who or what it is about, whether it happened or stands, and the words that carry its time. Used by the memory extraction model on every conversation window; the engine resolves dates, validates keys, and repairs format.
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。