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概要と使いどころ
Deep web research skill using Firecrawl MCP. Use when estimating market size (TAM/SAM/SOM), analyzing trends, or scraping competitors. For competitor teardowns, see competitor-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。
Structured competitor teardown skill. Use when scraping competitor sites, extracting pricing/features/positioning, or analyzing strategic gaps. For general research, see market-research.
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。
Run a Busabase-backed sales prospecting and first-touch outreach desk with a bundled Hono App-in-Skill deployable to AirApp. Use whenever the user invokes $kelly-sales-outreach or /kelly-sales-outreach, wants to enter a product or service and discover its ideal customers, asks to continuously find target accounts or public business contacts, needs evidence-based ICP scoring and personalized outreach drafts, or wants to review and send cold outreach safely. Four subcommands - setup, profile, research, send - plus the desk itself. Every first-touch message requires human approval; one company receives one initial email; guessed addresses, purchased lists, secret exposure, automated spam, and unreviewed external sends are forbidden.
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。
Ad-campaign command desk (投放指挥台, Busabase App-in-Skill) for a cross-border e-commerce seller running Amazon Ads, Meta (FB/IG), TikTok Ads, and Google Ads. Use when the user invokes $kelly-ads or /kelly-ads, or asks about 投放, ad campaigns, ad spend, ACOS, ROAS, Amazon Ads, TikTok ads, Meta ads, Google Ads, search terms, negative keywords, bid adjustment, budget pacing, CPC spikes, rejected ads, ad anomaly detection, 广告优化, or reviewing agent-proposed adjustment cards (negative keyword, bid down/up, pause target, budget shift, creative refresh).
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。
Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live 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。
Legal casebase App-in-Skill (Busabase-backed) intake and anonymization QA desk for law-firm judgment and award documents. Use when the user invokes $kelly-legal-casebase-ingest, mentions internal case database, 智能案例库, 裁判文书入库, 脱敏, 结构化, 分类标注, 案例审核, 质量验收, or wants a Busabase-backed desk where reviewers approve, revise, or block agent-prepared case records before they become searchable knowledge assets.
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-launch command desk (Busabase App-in-Skill) built on the RAMP discipline — Research / Assemble / Mobilize / Prove. The agent assembles the launch checklist and drafts launch assets, channel submissions (Product Hunt / Hacker News / directories), press pitches, and the launch-day runbook; the human approves the launch-readiness gate and steers launch day in a review UI. Use when the user invokes $kelly-launch or /kelly-launch, or mentions launching / shipping a product, a launch checklist or runbook, launch readiness, RAMP, Product Hunt / Show HN / press-kit / launch-email drafting, or wants to review and approve launch assets and channel submissions before they go public. This is launch OPERATIONS — the checklist, assets, submissions, and runbook — not a launch video (for a promo video use product-launch-video).
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 sourced, BDR-ready account research briefs for named target accounts in B2B SaaS ABM and outbound motions. Use after a target account list is built and before outreach sequences are written. Best for Tier 1, Tier 2, and intent-qualified accounts where BDRs need a clear "why this account, why now, why this persona" hypothesis. Designed for B2B SaaS teams selling compliance, security, GRC, risk, or other technical platforms into mid-market and enterprise buyers. Not intended for broad list building or mass enrichment.
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 account research briefs, personalized outreach sequences, persona-specific talk tracks, and objection handling frameworks for B2B SaaS BDR teams. Use when preparing BDR outreach for target accounts, building prospecting sequences, creating call scripts, developing objection responses, onboarding new BDRs, or reviewing outreach performance. Also use when asked to "write outreach emails," "build a prospecting sequence," "create a call script," "handle objections," or "research an account for outreach." Designed for BDR teams selling compliance, security, and GRC platforms to mid-market and enterprise buyers with multi-persona buying committees.
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。
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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。
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
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 whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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 query the document_embeddings table for raw signal data using HogQL. Use when you need to perform semantic search over signals, fetch every signal that contributed to a specific report, or list signal types. For browsing the curated report layer (the Inbox) — listing reports, filtering by status/source, drilling into a single report by ID — use the `inbox-exploration` skill first; drop into this skill afterwards if the user wants the underlying observations.
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 whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
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。
Install the ToolUniverse Claude Code plugin in one step — provides MCP server with 1000+ scientific tools, 120+ research skills, slash commands, hooks, and the research agent. Use for first-time plugin install, troubleshooting plugin not loading, verifying MCP server connection, listing API key requirements, or configuring auto-update.
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。
Orchestrate the full ToolUniverse self-improvement cycle: discover APIs, create tools, test with researcher personas, fix issues, optimize skills, and push via git. References and dispatches to all other devtu skills. Use when asked to: run the self-improvement loop, do a debug/test round, expand tool coverage, improve tool quality, or evolve ToolUniverse.
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。
Extract property, owner, legal description, liens, mortgages, easements, and tax status from title search reports.
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。
Windows アプリ開発 (Windows AI Foundry) の AI/ML 統合リファレンス。 Windows AI APIs、LanguageModel, TextRecognizer, ImageScaler, ImageObjectExtractor, ImageObjectRemover, ImageDescriptionGenerator, SemanticSearch, ContentModeration、 Phi Silica / Aion Instruct、LoRA、Windows ML、Windows ML Runtime API、ONNX Runtime、 execution providers、WebGPU EP、Model Catalog、LearningModel、 Foundry Local (CLI / REST API / SDK)、MCP サーバー、App Actions、IActionProvider、 Agent Launchers、DirectML、IDMLDevice、operators、tensors、NPU。
nuqs (Type-safe URL search params 状態管理, React / Next.js) リファレンス。 useQueryState, useQueryStates, parseAsString / parseAsInteger / parseAsArrayOf 等 parsers、 NuqsAdapter (Next.js / React Router / TanStack Router / Remix)、createSerializer、 createStandardSchemaV1, inferParserType、shallow routing、history 制御、SSR、 URL 長さ制限 (limits)、Zod / Effect / Sury 連携。
React Router v8 (RR8、旧 Remix) の Framework / Data / Declarative Mode リファレンス。 loader, action, middleware, routes.ts, shouldRevalidate、createBrowserRouter, RouterProvider, BrowserRouter、 hooks (useLoaderData, useActionData, useFetcher, useNavigate, useNavigation, useParams, useSearchParams)、 Form, Link, NavLink, Outlet, Route, Routes, Await、SSR / SPA / Static / RSC、ErrorBoundary, redirect, defer (v7 で削除), session, matchPath, matchRoutes, type-safe routing。 TanStack Router (@tanstack/react-router) とは別ライブラリ。
AppKit macOS UI リファレンス。Liquid Glass (NSGlassEffectView)、 NSApplication, NSWindow, NSViewController, NSView, NSMenu, NSToolbar, NSTableView, NSOutlineView, NSCollectionView, NSSplitViewController, NSTabView, NSPopover, NSOpenPanel / NSSavePanel, NSSearchField, NSControl, NSStackView, NSScrollView, Diffable Data Source, target-action, responder chain。
Android Jetpack Compose (Kotlin) の androidx Material3 コンポーネントリファレンス。 Button, IconButton, FloatingActionButton, Checkbox, Switch, Slider, TextField, SearchBar, DropdownMenu, Chip, DatePicker, TimePicker, Card, Scaffold, Dialog, BottomSheet, TopAppBar, NavigationBar, NavigationRail, Tab, Snackbar, ProgressIndicator, Badge, Icon, MaterialTheme, ColorScheme, Typography, Shapes。
Research a specific company before applying or interviewing by gathering facts from primary and official sources, recording the source and date behind every claim, and keeping anonymous or unverifiable material clearly separated from confirmed fact. Use when a user asks what a company actually does, how it makes money, what its published conditions are, whether a posting matches the company's own disclosures, or what to verify before an interview; do not use to predict hiring outcomes, to repeat review-site content as fact, or to bulk-crawl sites that prohibit automated access.
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。
研究概要・中間発表予稿の「当てどころの深さ」を評価し、より深い框(フレーム)への付け替えを提案するスキル。鎖が繋がっているか(→framing-check)ではなく、狙いが浅くないか(道具の実装で終わっていないか)・題材固有の強みを活かしてもっと深い研究課題に貼り替えられるかを見る。深い框が実在するときだけ提案し、無ければ黙る。研究の面白さ・野心・当てどころ・框の付け替えを見てほしい、と言われたとき、または research-review から内部呼び出しされる。教員向け(率直・野心を上げてよい)。