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概要と使いどころ
Plan customer discovery interviews with the right goal, segment, constraints, and method. Use when preparing interviews for problem validation, churn research, or new product ideas.
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。
Uncover customer jobs, pains, and gains in a structured JTBD format. Use when clarifying unmet needs, repositioning a product, or improving discovery and messaging.
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。
Review and execute explicitly requested Stripe writes with exact environment, object, amount, currency, customer, and irreversible-effect confirmation.
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 safe WeChat follower-message automation and send customer-service text only when the user provides an explicit target and confirmation.
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 Stripe customers, subscriptions, prices, invoices, payment status, and lifecycle transitions with explicit environment and object scope.
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。
Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.
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。
This skill helps users automatically find social media profiles across platforms like Facebook, Twitter, Instagram, LinkedIn, etc. using the BrowserAct API. Agent should proactively apply this skill when users express needs like finding someone's social media accounts, discovering a brand's social media presence, tracking down social profiles of job candidates, finding contact info for sales prospects, researching a person's digital footprint, verifying the identity of someone met online, monitoring the social media reach of influencers, checking social accounts of business competitors, gathering public profiles for background research, locating official customer service accounts across platforms, or compiling contact databases for networking.
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。
This skill is designed to help users automatically extract reviews from Google Maps via the Google Maps Reviews API. Agent should proactively apply this skill when users request to find reviews for local businesses (e.g., coffee shops, clinics), monitor customer feedback for a specific brand or location, analyze sentiment of reviews for competitors, extract reviews for a chain of stores or services, track reputation of a local restaurant, gather user testimonials for a specific venue, conduct market research on service quality of local businesses, monitor reviews for a new retail location, collect feedback on public attractions or parks, identify common complaints for a specific service provider, research the best-rated places in a city, analyze recurring themes in reviews for a specific industry.
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。
First-response playbook for an inbound customer message — classifies the ticket, restates the issue, gathers missing context, and routes the reply.
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 a ticket must go to a human before any reply is sent, and how the first reply should read. Use when drafting a customer reply or setting needs_human_review.
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。
Hunt ASP.NET-specific surface — ViewState deserialization (signed-only vs encrypted), machineKey recovery, dual-parser MAC-bypass anti-pattern, request-validator bypass, trace.axd/elmah.axd disclosure, load-balanced ViewState cross-node failures, SafeControl enumeration via reflection, customErrors mode=Off stack-trace leaks, classic Webforms .aspx/.asmx/.svc surface. Built for ASP.NET Webforms + WCF + SharePoint farms.
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 detailed B2B buyer personas with pain points, objections, buying triggers, and messaging angles. Use when given a product and market to research ideal customer profiles, creating buyer personas, or needing to understand who you're selling to before launching campaigns. Platform: Google and Meta.
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。
Verify assumptions before implementing large or risky changes to APIs, provider data, billing, pricing, quotas, or defaults. Use when a mistake could affect customers or the user asks if a change is safe to ship. Checks real-world impact beyond passing tests.
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 sourced JSON dossiers on people, companies, or topics with DeepAPI. Use for profiles, prospects, vendor due diligence, or customer research across sources; use deep-research for 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。
Use this skill whenever the user asks you to load, clean, aggregate, join, or otherwise analyze tabular data (csv, xlsx, json, or a database export) — 'what's the total revenue by region', 'clean up this dataset', 'join these two files on customer id'. Establishes a verification discipline so reported numbers are always numbers the code actually printed, never numbers you inferred or estimated.
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。
Company discovery and deep research skill. Researches a company's product and ICP, discovers target companies to sell to using Browserbase Search API, deeply researches each using a Plan→Research→Synthesize pattern, and scores ICP fit — compiled into a scored research report and CSV. Supports depth modes (quick/deep/deeper) for balancing scale vs intelligence. Use when the user wants to: (1) find companies to sell to, (2) research potential customers, (3) discover companies matching an ICP, (4) build a target company list, (5) do market research on prospects. Triggers: "find companies to sell to", "company research", "find prospects", "ICP research", "target companies", "who should we sell to", "market research", "lead research", "prospect list".
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 validate a product idea, find pain points, mine demand signals, discover objections, or gather voice-of-customer language from Reddit, social posts, video transcripts, and comments. Produces evidence-backed product 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。
Look up people's LinkedIn profiles by URL with Datacircle, no markup, and manage the Datacircle balance and files. Use when the user gives LinkedIn profile URLs to enrich or research (a candidate, a customer, a person in their CRM), asks what their Datacircle balance is, or wants the co-op flat file.
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。
Automate Stripe tasks via Rube MCP (Composio): customers, charges, subscriptions, invoices, products, refunds. Always search tools first for current 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。
Issue, cancel, and fetch Hungarian invoices via the szamlazz.hu Agent API. Handles VAT calculation, NAV taxpayer lookup, partner caching, and PDF generation. Use when the user mentions számla, számlázás, invoice, sztornó, díjbekérő, proforma, or wants to bill a customer.
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。
Automate Shopify tasks via Rube MCP (Composio): products, orders, customers, inventory, collections. Always search tools first for current 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。
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
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。
Golang everyday observability — the always-on signals in production. Covers structured logging with slog, Prometheus metrics, OpenTelemetry distributed tracing, continuous profiling with pprof/Pyroscope, server-side RUM event tracking, alerting, and Grafana dashboards. Apply when instrumenting Go services for production monitoring, setting up metrics or alerting, adding OpenTelemetry tracing, correlating logs with traces, migrating legacy loggers (zap/logrus/zerolog) to slog, adding observability to new features, or implementing GDPR/CCPA-compliant tracking with Customer Data Platforms (CDP). Not for temporary deep-dive performance investigation (→ See `samber/cc-skills-golang@golang-benchmark` and `samber/cc-skills-golang@golang-performance` skills).
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。
[omh] Business question on market, competitors, pricing: business research brief - turns a market, competitor, pricing, or customer question into a structured evidence-vs-inference brief; for raw link gathering use ulw-research, and for ongoing multi-role research use research-department. Use when the user says: research-brief, business-research, business research, research brief, decision brief, pricing decision brief, decision-ready brief, source-backed business 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。