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Found 10,640 Skills
Use this skill when building Model Context Protocol (MCP) servers on Cloudflare Workers. This skill should be used when deploying remote MCP servers with TypeScript, implementing OAuth authentication (GitHub, Google, Azure, etc.), using Durable Objects for stateful MCP servers, implementing WebSocket hibernation for cost optimization, or configuring dual transport methods (SSE + Streamable HTTP). The skill prevents 15+ common errors including McpAgent class export issues, OAuth redirect URI mismatches, WebSocket state loss, Durable Objects binding errors, and CORS configuration mistakes. Includes production-tested templates for basic MCP servers, OAuth proxy integration, stateful servers with Durable Objects, and complete wrangler.jsonc configurations. Covers all 4 authentication patterns: token validation, remote OAuth with DCR, OAuth proxy (workers-oauth-provider), and full OAuth provider implementation. Self-contained with Worker and Durable Objects basics. Token efficiency: ~87% savings (40k → 5k tokens). Production tested on Cloudflare's official MCP servers. Keywords: MCP server, Model Context Protocol, cloudflare mcp, mcp workers, remote mcp server, mcp typescript, @modelcontextprotocol/sdk, mcp oauth, mcp authentication, github oauth mcp, durable objects mcp, websocket hibernation, mcp sse, streamable http, McpAgent class, mcp tools, mcp resources, mcp prompts, oauth proxy, workers-oauth-provider, mcp deployment, McpAgent export error, OAuth redirect URI, WebSocket state loss, mcp cors, mcp dcr
Comprehensive expertise in decentralized prediction markets, including Polymarket-style platforms, UMA Optimistic Oracle integration, Conditional Tokens Framework (CTF), market making, resolution mechanisms, and regulatory considerations. Use when "prediction market, Polymarket, betting market, outcome tokens, resolution oracle, UMA oracle, conditional tokens, binary market, outcome prediction, information market, " mentioned.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
Debug failed Render deployments by analyzing logs, metrics, and database state. Identifies errors (missing env vars, port binding, OOM, etc.) and suggests fixes. Use when deployments fail, services won't start, or users mention errors, logs, or debugging.
Open-source workflow automation platform with visual node-based editor, 400+ integrations, webhooks, and self-hosted deployment capabilities
Deterministically merge per-section files under `sections/` into `output/DRAFT.md`, preserving outline order and weaving transitions from `outline/transitions.md`. **Trigger**: merge sections, merge draft, combine section files, sections/ -> output/DRAFT.md, 合并小节, 拼接草稿. **Use when**: you have per-unit prose files under `sections/` and want a single `output/DRAFT.md` for polishing/review/LaTeX. **Skip if**: section files are missing or still contain scaffolding markers (fix `subsection-writer` first). **Network**: none. **Guardrail**: deterministic merge only (no new facts/citations); preserve section order from `outline/outline.yml`.
Identify missing skills and recommend installations from AI Cortex or public skill catalogs. Use when discovering capabilities or suggesting skills to fill gaps.
Generates hierarchical knowledge graphs via Recursive Pareto Principle for optimised schema construction. Produces four-level structures (L0 meta-graph through L3 detail-graph) where each level contains 80% fewer nodes while grounding 80% of its derivative, achieving 51% coverage from 0.8% of nodes via Pareto³ compression. Use when creating domain ontologies or knowledge architectures requiring: (1) Atomic first principles with emergent composites, (2) Pareto-optimised information density, (3) Small-world topology with validated node ratios (L1:L2 2-3:1), or (4) Bidirectional construction. Integrates with graph (η≥4 validation), abduct (refactoring), mega (SuperHyperGraphs), infranodus (gap detection). Triggers: 'schema generation', 'ontology creation', 'Pareto hierarchy', 'recursive graph', 'first principles decomposition'.
Prompt engineering guidance for Claude (Anthropic) model. Use when crafting prompts for Claude to leverage XML-style tags, long-context capabilities, extended thinking, and strong instruction following.
Expert LLC operations management for ID8Labs LLC (Florida single-member LLC). 9 specialized agents providing PhD-level expertise in compliance, tax strategy, asset protection, and business operations. Triggers on keywords like LLC, taxes, expenses, annual report, EIN, compliance, bookkeeping, deductions, filing, sunbiz, quarterly, S-Corp, retirement, audit, insurance, cash flow, mentor, teach, learn.
Sistema para convertir logros tecnicos en narrativas que comunican senioridad e impacto. Usar cuando el usuario necesite escribir sobre sus proyectos, preparar presentaciones tecnicas, documentar decisiones de arquitectura, o comunicar complejidad a audiencias no-tecnicas. Activa con palabras como explicar proyecto, presentacion, documentar, caso de estudio, blog tecnico, conferencia. Especializado en developers senior que necesitan comunicar impacto business.