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Found 846 Skills
Operate Resend email campaigns, webhooks, Gmail reply sync, and Google Sheets tracking for the m26pipeline scripts/emails toolkit. Use when sending batch mail via send_campaign, syncing replies, running the email webhook server, UTM links, Resend API keys, sparse-clone setup, idempotency, or follow-up cohorts. Applies to any environment once scripts/emails is present or fetched from GitHub.
Migrate Next.js, Vite, React, Vue, Svelte, and other web applications from Vercel to CreateOS. Parses vercel.json, maps environment variables, detects framework and build settings, and deploys to CreateOS via the CreateOS MCP server. Use this skill whenever the user mentions migrating from Vercel, leaving Vercel, moving a deployment off Vercel, replacing Vercel, or when a repository contains a vercel.json file and the user wants to deploy elsewhere. Also use when the user references concerns about Vercel reliability, pricing, security, or the Vercel breach, and wants an alternative.
Design and optimize systems for high concurrency, throughput, scalability, and elastic scale—concurrency models (threads, async/await, actors), lock-free patterns, connection pooling, caching stampede mitigation, horizontal scaling, load balancing, backpressure, queueing, rate limiting, bulkheads, read replicas, sharding, pool tuning, profiling, capacity planning, SLO-driven autoscaling, multi-region and CDN edge architecture. Use when the user asks about high concurrency, scalability, throughput, horizontal scaling, connection pooling, backpressure, rate limiting, caching stampede, read replica, sharding, autoscaling, capacity planning, lock contention, async scalability, or load balancing—not service decomposition (microservices-developer), event buses only (event-driven-architecture), generic CRUD (senior-software-engineer), SRE on-call only (site-reliability-engineer), load tests without architecture (performance-engineer), or cost-only FinOps (cloud-economist).
Build backend AI with Vercel AI SDK v6 stable. Covers Output API (replaces generateObject/streamObject), speech synthesis, transcription, embeddings, MCP tools with security guidance. Includes v4→v5 migration and 15 error solutions with workarounds. Use when: implementing AI SDK v5/v6, migrating versions, troubleshooting AI_APICallError, Workers startup issues, Output API errors, Gemini caching issues, Anthropic tool errors, MCP tools, or stream resumption failures.
Complete E2E (end-to-end) and integration testing skill for TypeScript/NestJS projects using Jest, real infrastructure via Docker, and GWT pattern. ALWAYS use this skill when user needs to: **SETUP** - Initialize or configure E2E testing infrastructure: - Set up E2E testing for a new project - Configure docker-compose for testing (Kafka, PostgreSQL, MongoDB, Redis) - Create jest-e2e.config.ts or E2E Jest configuration - Set up test helpers for database, Kafka, or Redis - Configure .env.e2e environment variables - Create test/e2e directory structure **WRITE** - Create or add E2E/integration tests: - Write, create, add, or generate e2e tests or integration tests - Test API endpoints, workflows, or complete features end-to-end - Test with real databases, message brokers, or external services - Test Kafka consumers/producers, event-driven workflows - Working on any file ending in .e2e-spec.ts or in test/e2e/ directory - Use GWT (Given-When-Then) pattern for tests **REVIEW** - Audit or evaluate E2E tests: - Review existing E2E tests for quality - Check test isolation and cleanup patterns - Audit GWT pattern compliance - Evaluate assertion quality and specificity - Check for anti-patterns (multiple WHEN actions, conditional assertions) **RUN** - Execute or analyze E2E test results: - Run E2E tests - Start/stop Docker infrastructure for testing - Analyze E2E test results - Verify Docker services are healthy - Interpret test output and failures **DEBUG** - Fix failing or flaky E2E tests: - Fix failing E2E tests - Debug flaky tests or test isolation issues - Troubleshoot connection errors (database, Kafka, Redis) - Fix timeout issues or async operation failures - Diagnose race conditions or state leakage - Debug Kafka message consumption issues **OPTIMIZE** - Improve E2E test performance: - Speed up slow E2E tests - Optimize Docker infrastructure startup - Replace fixed waits with smart polling - Reduce beforeEach cleanup time - Improve test parallelization where safe Keywords: e2e, end-to-end, integration test, e2e-spec.ts, test/e2e, Jest, supertest, NestJS, Kafka, Redpanda, PostgreSQL, MongoDB, Redis, docker-compose, GWT pattern, Given-When-Then, real infrastructure, test isolation, flaky test, MSW, nock, waitForMessages, fix e2e, debug e2e, run e2e, review e2e, optimize e2e, setup e2e
Reviews PR comments from GitHub (Copilot, reviewers), evaluates against actual code, replies with reasoning, and resolves threads. Triggers on "review pr comments", "address pr feedback", "fix pr comments", or "review copilot suggestions".
Control Tavus CVI conversations in real-time using the Interactions Protocol. Use when sending text for the replica to speak (echo), interrupting the replica, injecting context mid-conversation, handling tool calls, or listening to conversation events via WebRTC/Daily.
Build features with AI coding tools (Claude Code, Lovable, Replit, Cursor). Use when implementing specs, iterating on AI code, or choosing tools. Focuses on tool selection, effective prompting, and iteration workflows for non-technical founders.
Building AAA-quality games and real-time experiences with Unreal Engine 5Use when "unreal, ue5, ue4, unreal engine, blueprints, blueprint, actor component, gameplay ability, gas unreal, niagara, nanite, lumen, world partition, level streaming, unreal multiplayer, unreal replication, gamemode, gamestate, playerstate, playercontroller, pawn, character class, uclass, ustruct, uenum, uproperty, ufunction, unreal, ue5, blueprints, c++, gamedev, aaa, real-time, rendering, nanite, lumen, niagara, gameplay, replication, multiplayer, gas" mentioned.
Implement ReasoningBank adaptive learning with AgentDBs 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
This skill should be used when users need to manage AWS Support cases via CLI. It handles listing cases (recent 2 weeks, unresolved), viewing case details with bilingual display (English-Chinese line by line), creating new cases with auto-detected service/category, replying to cases, and attachment handling. Triggers on requests mentioning "AWS support", "support case", "工单", "support ticket", or AWS technical support inquiries.
Exploratory discussion pattern for unsolved problems. Replicate the thinking of Staff+ engineers: "When there's no clear answer, expose blind spots by confronting diverse perspectives." True multi-agent discussions where experts directly engage with each other through team-based + messaging architecture.