搜索「Business」的结果

io.analyticlabs/zeus-memory

Enterprise memory, search, and context for frontier AI. 38 tools for business intelligence.

pro.begonia/begonia-pro

Local SEO: MCP for local business audits with tools and guidance.

ai.windsor/windsor-mcp

Query and analyze marketing, sales, and business data from 325+ platforms via Windsor.ai.

com.venturu/mcp-server

The Venturu MCP server. Search and contact business listings and brokers on Venturu.

com.gocontentflow/mcp

Search and analyze 50,000+ hours of business podcast transcripts, entities, and speakers.

io.github.sooda-ai/sooda-mcp

AI agent relay — message business agents across company boundaries via A2A protocol

net.agentutil/context-mcp

Situational awareness — holidays, business hours, and platform status for timing-sensitive actions.

io.github.domdomegg/google-maps-places-mcp

Allow AI systems to search for places and get business info via Google Maps Places API.

io.github.olgasafonova/nordic-registry-mcp-server

Access Nordic business registries (Norway, Denmark, Finland, Sweden). 23 tools.

io.github.Kibetho/saasforit

AI-powered SaaS tool discovery API. Search 150+ curated business tools and get recommendations.

io.github.tk26/minoa

Automate business cases, articulate customer outcomes & analyze your value-selling motion with Minoa

co.dockai/mcp

Discover MCP endpoints for real-world entities by resolving business domains.

io.github.topofgames/la-ei-romania

Romanian classified ads and business directory. Search 30K+ businesses, ads, exchange rates.

io.github.PostHog/posthog/querying-posthog-data

Required reading before writing any HogQL/SQL or calling execute-sql against PostHog. Use whenever the user wants to search, find, or do complex aggregations PostHog entities (insights, dashboards, cohorts, feature flags, experiments, surveys, hog flows, data warehouse, persons, etc.) and query analytics data (trends, funnels, retention, lifecycle, paths, stickiness, web analytics, error tracking, logs, sessions, LLM traces). Also the first stop for a governed business or telemetry measure (MRR, activation, billable usage, active organizations, failure rates): check the semantic layer (canonical metrics in system.information_schema.metrics) before deriving from raw events or a typed domain tool. Covers HogQL syntax differences from ClickHouse SQL, system table schemas (system.*), available functions, query examples, and the schema-discovery workflow.

io.github.Agents365-ai/drawio-skill/drawio-skill

Use when the user requests diagrams, flowcharts, architecture diagrams, ER diagrams, UML / sequence / class diagrams, SysML / MBSE diagrams (block definition, internal block, requirement, parametric), BPMN business process diagrams, swimlane / cross-functional flowcharts, network topology, cloud architecture from Terraform or Kubernetes manifests, ML/DL model figures (Transformer/CNN/LSTM), mind maps, or any visualization. Also use proactively when explaining systems with 3+ components, complex data flows, or relationships that benefit from visual representation. Best suited when the diagram needs custom styling, rich shape vocabulary, swimlanes, or exportable images (PNG/SVG/PDF/JPG). Generates .drawio XML and exports locally via the native draw.io desktop CLI.

io.github.shareAI-lab/learn-claude-code/agent-builder

Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

io.github.wshobson/agents/python-design-patterns

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

io.github.upstash/context7/context7-mcp

Fetches current, version-specific library documentation and code examples through the Context7 MCP server. Use whenever the user asks about a library, framework, SDK, API, CLI tool, or cloud service, including API syntax, configuration, setup instructions, version migration, CLI usage, and library-specific debugging. Use when generating code that calls a third-party library, and when the user names a version such as Next.js 15 or React 19. Use even for well-known libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Django, and Spring Boot, because training data may not reflect recent changes. Prefer this over web search for library documentation. Do not use it for refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts, or when the user has already supplied the relevant documentation.

io.github.mukul975/Anthropic-Cybersecurity-Skills/exploiting-excessive-data-exposure-in-api

Tests APIs for excessive data exposure (OWASP API3:2023) by intercepting raw API responses and comparing them against what the UI actually renders, looking for leaked PII, internal identifiers, debug data, or business-sensitive fields the frontend filters but the API still transmits. Use when auditing REST or mobile-app APIs for over-fetching, response filtering bypass, or unintended data leakage in endpoint responses.

io.github.PostHog/posthog/suggesting-data-imports

Use when the user asks about revenue, payments, subscriptions, billing, CRM deals, support tickets, ad spend, production database tables, or other data PostHog does not collect natively — or wants to join or correlate PostHog product events with that external business data. Also use when a query fails because a table does not exist or returns no results for expected external data. The data warehouse can import from SaaS tools (Stripe, Hubspot, Zendesk, etc.), ad platforms, production databases (Postgres, MySQL, BigQuery, Snowflake), and other arbitrary data sources. Covers checking existing sources, identifying the right source type, and guiding the setup.

io.github.PostHog/posthog/review-hog-perspective-logic-correctness

The Logic & Correctness review perspective for ReviewHog. Verifies that changed code does what it is supposed to do — business logic, edge cases, data transformations, and query / data-access correctness. Reports correctness issues only; security and performance are separate perspectives.

io.github.danielmiessler/LifeOS/Apify

Scrapes social platforms, business data, and e-commerce via Apify actors — Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon, and web crawls — filtering in code. USE WHEN scrape Instagram, scrape LinkedIn, scrape TikTok, scrape YouTube, scrape Facebook, Google Maps leads, Amazon reviews, business intelligence, multi-platform social listening, competitive analysis, lead generation, social monitoring, Apify actors, web crawl, extract contacts. NOT FOR X/Twitter operations (use _X), 4-tier progressive scraping with proxy escalation (use BrightData), or real-Chrome bot bypass and computer use (use Interceptor).

io.github.aiskillstore/marketplace/backend-orchestrator

Coordinates backend development tasks (APIs, services, databases). Use when implementing REST APIs, business logic, data models, or service integrations. Applies backend-standard.md for quality gates.

io.github.telagod/code-abyss/backend

Backend engineering judgment, distilled from a stronger model - invoke when CHOOSING a tech stack, language, database, queue, or architecture; designing a service, API, business logic, or schema; making a system production-ready (observability, failure handling, security); or reviewing server-side code and judging codebase health. Scenario-driven stack tradeoffs, logic-design rules, data discipline, production floors, and a rot catalog (the early signs of unmaintainable code).