Skills
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Integrates Clerk authentication in React/Vite, Next.js App Router, and Cloudflare Workers apps. Covers ClerkProvider setup, clerkMiddleware and createRouteMatcher for protected routes, token verification with @clerk/backend verifyToken, custom JWT templates and session claims (user.public_metadata, org_id, org_role) for RBAC, Hono integration via @hono/clerk-auth, shadcn/ui auth components, and Playwright E2E testing with @clerk/testing, +clerk_test emails and the 424242 OTP. Documents 11 pitfalls: Missing Clerk Secret Key, JWKS cache races, missing authorizedParties CSRF risk, Core 2 import path changes, JWT size limits, ClerkProvider cannot be used as a JSX component, async auth() confusion, and Vite dev 431 header errors. Use when adding Clerk to a project, protecting routes, verifying tokens, shaping JWT claims, testing auth flows, or debugging Clerk authentication errors.
Build, review, debug, configure, migrate, teach, or plan ClickHouse analytics work from TypeScript with current docs and a full usage guide. Use for @clickhouse/client, @clickhouse/client-web, createClient, query, insert, command, exec, JSONEachRow, query_params, streaming, MergeTree family, ORDER BY/PARTITION BY, materialized views, projections, async_insert, FINAL, lightweight deletes and updates, 26.x server upgrades, ClickHouse Cloud HTTPS, system.query_log, and TS schema/ingest patterns. TypeScript/Node SDKs only — not Python/Go/Java clients.
Query the ClinPGx (formerly PharmGKB) REST API plus the CPIC PostgREST companion API for pharmacogenomic clinical annotations, CPIC/DPWG dosing guidelines, gene-drug pairs, variant-drug associations, FDA/EMA drug labels, and PGx pathways. Two-host architecture: api.clinpgx.org for annotation records, api.cpicpgx.org for genotype→recommendation lookups. No auth. For germline pathogenicity use clinvar-database; for somatic cancer PGx use cosmic-database or opentargets-database; for drug bioactivity use chembl-database-bioactivity.
Close sales deals consistently as a solopreneur. Use when a prospect is at the decision stage and you need to move them to yes, when deals are stalling or going cold, when you need closing scripts or techniques, or when you want to build a repeatable process for turning proposals into signed contracts. Covers decision-stage psychology, closing techniques, stall recovery, contract-to-payment flow, and post-close relationship setup. Trigger on "how do I close a deal", "closing deals", "deal stalling", "prospect not responding", "how to get them to say yes", "close the sale", "convert proposal to client", "sales closing".
Cloudbeds hospitality API expertise for building property-management, booking, payments, accounting, and channel integrations. Use when code calls `api.cloudbeds.com`, `hotels.cloudbeds.com`, `api.payments.cloudbeds.com`, or imports a Cloudbeds SDK; when the user asks about Cloudbeds PMS, reservations, rate plans, webhooks, Pay-By-Link, Payments Vault, Accounting API, Data Insights, Fiscal Documents, or Group Profile APIs; when authenticating with `cbat_`-prefixed API keys or OAuth 2.0 against Cloudbeds; when subscribing to Cloudbeds webhooks or working with `postReservation`, `getAvailableRoomTypes`, `postCharge`, `patchRate`, or similar operations.
(NS) Harness-aware autonomous execution engine — self-decides planning depth, resolves doubts against docs/context and docs/specs before escalating, dispatches multi-agent implementation in an isolated worktree. Use as the standalone entry point for a local plan file, a pasted execution plan, or an ad-hoc "run this autonomously" request with no GitLab issue involved. Also acts as the Phase 2 execution engine when invoked by execution-gitlab-issue — do not run the standalone pipeline in that case. Not for simple single-step edits (use code-coder) and not for GitLab issues directly (use execution-gitlab-issue, which calls this skill internally).
Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.
A standalone adversarial PLAN-review loop where Claude Code (builder) and OpenAI Codex (read-only critic) tag-team an implementation plan before any code is written. Use this when you ALREADY have a plan or a clear idea and just want the cross-model stress-test — no requirements interview first. Claude drafts/loads the plan into PLAN_FILE (default PLAN.md), Codex reviews it in a read-only sandbox and returns VERDICT:APPROVED or VERDICT:REVISE, Claude revises and re-submits to the SAME Codex session (context preserved) until APPROVED or a configurable MAX_ROUNDS cap is hit. Human approves the converged plan before code. Use when the user says "/codex-plan-review", "codex review my plan", "have Codex review my plan", "argue this plan with Codex", "adversarial plan review", "make Claude and Codex argue/fight over the plan", or is about to build something high-stakes (auth, schema, concurrency, migrations, payments) and wants a second-model sanity check on the PLAN before implementation. For a guided requirements interview BEFORE the review, use /grill-me-codex instead. NOT for reviewing already-written CODE (that is /codex-code-review or the Codex plugin's /codex:review) and NOT for trivial changes.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
Guides commercial contract review and negotiation support for B2B agreements—MSAs, SaaS/order forms, vendor and customer contracts, DPAs, SLAs, limitation of liability, indemnity, IP, payment terms, and redline/issue logs with business impact notes. Use when reviewing or negotiating commercial terms, comparing vendor or customer paper, drafting negotiation positions, or triaging contract risk before sign-off—not for SOC/ISO GRC programs or vendor questionnaire ops (compliance-specialist), technical audit evidence (compliance-engineer), revenue recognition under ASC 606 (senior-revenue-accountant), or product requirements (business-analyst), strategy (business-consultant). Corporate/board: corporate-counsel. AI architecture for contract review: applied-ai-architect-commercial-enterprise. M&A economics mandate: transaction-principal. Drafting assistance only; human counsel must approve.
This skill should be used when the user asks to "compress this video", "reduce file size", "make this video smaller", "optimize for web", "shrink this video", "compress to under X MB", "reduce bitrate", "make it smaller without losing quality", "encode with H.265", or "re-encode this video".
Surfaces a single, genuinely relevant AWS Activate partner offer as optional context AFTER another AWS Startup Advisor skill has finalized its recommendation, plan, or build—never influencing the technical advice. Consulted by architect-for-startups, start-building-for-startups, agent-advisor, gcp-to-aws, heroku-to-aws, and llm-to-bedrock: once their output is final, this skill checks whether a relevant Activate offer exists for what the founder is building or migrating and, if so, appends one quiet, dismissible line with a tracked redeem link. Merit-first (offers never change the recommendation), relevance-gated to what the founder actually surfaced, at most one per response and often none, muteable. Offer content is read live from the sibling knowledge-base-for-startups skill (references/offers.md). Do NOT use for account-specific lookups (credits balance, Activate membership, application status)—send those to https://aws.amazon.com/startups.
Use this skill when the user asks to "convert this page to markdown", "extract this webpage", "save this article", "grab content from URL", "get markdown from this link", "scrape this page", provides a URL to extract, or wants clean web content without ads and clutter
When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' or 'copy sweep.' This skill provides a systematic approach to editing marketing copy through multiple focused passes.
Paid API marketplace for AI agents via Corbits. Search hundreds of premium API proxies, call them with automatic USDC micropayments (x402 protocol), and fund the payment wallet with MoonPay. Use when the user wants to access paid data APIs (crypto prices, weather, financial data, etc.) with per-request billing and no API key management.
Multi-perspective deliberation on a question. Use when the user says "get perspectives on", "multiple viewpoints", "council on this", "devil's advocate", "stress test this idea", "red team this", "poke holes in", "second opinion on", "what am I missing", or wants multi-perspective analysis of a decision, design, or strategy.
Cryptocurrency arbitrage monitor | 加密货币套利监控. Supports Binance, OKX, Gate.io, Huobi | 支持币安、OKX、Gate.io、火币. Real-time price monitoring, profit calculation, Telegram alerts | 实时价格监控、利润计算、Telegram 通知. Triggers: 套利, arbitrage, 加密货币, crypto, 价格差. Find arbitrage opportunities across Chinese-accessible exchanges.
Pure CSS functional feedback effects for form inputs, buttons, loading states and text links. Triggered by :focus, :active, and state changes — not hover. Auto-matches intensity to the existing design style. Zero JS, zero dependencies, no SSR issues. Sourced from cssfx (6.1k ★).
This skill should be used when users need to analyze CSV or Excel files, understand data patterns, generate statistical summaries, or create data visualizations. Trigger keywords include "analyze CSV", "analyze Excel", "data analysis", "CSV analysis", "Excel analysis", "data statistics", "generate charts", "data visualization", "分析CSV", "分析Excel", "数据分析", "CSV分析", "Excel分析", "数据统计", "生成图表", "数据可视化".
Design and execute customer onboarding that drives activation and retention. Use when building onboarding flows for new users, reducing churn in the first 30 days, improving time-to-value, or creating onboarding sequences (email, in-app, or manual). Covers activation metrics, onboarding step design, friction reduction, and measuring onboarding success. Trigger on "customer onboarding", "onboarding flow", "user onboarding", "reduce early churn", "improve activation", "onboarding sequence", "time to value".
Design and audit customer reference programs covering reference eligibility, consent, legal approval, quote/case-study evidence, logo use, segment matching, sales requests, advocacy fatigue, confidentiality, incentives, renewal/health risk, win stories, analyst/procurement proof, and reference governance. Use when sales and marketing need credible customer proof without overusing or misrepresenting customers.
Research customer questions by searching across documentation, knowledge bases, and connected sources, then synthesize a confidence-scored answer. Use when a customer asks a question you need to investigate, when building background on a customer situation, or when you need account context.
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns
Check preprocessing, splits, leakage, augmentation, label noise, and distribution mismatch before blaming the model. Use for new datasets, suspicious gains, brittle generalization, unexplained train-test gaps, or any result that may be caused by data pipeline bugs rather than model behavior.
World-class database schema design - data modeling, migrations, relationships, and the battle scars from scaling databases that store billions of rowsUse when "database schema, data model, migration, prisma schema, drizzle schema, create table, add column, foreign key, primary key, uuid, auto increment, soft delete, normalization, denormalization, one to many, many to many, junction table, polymorphic, enum type, index strategy, database, schema, migration, data-model, prisma, drizzle, typeorm, postgresql, mysql, sqlite" mentioned
Manage Databricks workspace connections: check current workspace, switch profiles, list available workspaces, or authenticate to a new workspace. Use when the user mentions "switch workspace", "which workspace", "current profile", "databrickscfg", "connect to workspace", or "databricks auth".
Build professional financial services data packs from various sources, including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs
Use when adding local data persistence to a Python project — caching, relational storage (simple, complex/OLTP, or analytical/OLAP), document storage, full-text search, vector/embedding search, background job queues, cron/scheduled tasks, pub/sub/event streaming, graph data, blob storage, or time-series data — and no explicit \"evaluate the best DB for this\" request was made. Gives the default technology per category, chosen for permissive licensing, pytest-local testability with no docker/cloud, and low-boilerplate LLM-agent-friendly APIs, so picks stay consistent across projects instead of drifting session to session.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.