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Reusable instructions for compatible AI agents.
Field counts come from the latest full-catalog snapshot. A resource can belong to several fields and platforms.
Spec-Driven Product Development - A 3-phase methodology (Research, Spec, Implementation) for building software from structured specifications.
Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.
Expert patterns for Salesforce platform development including Lightning Web Components (LWC), Apex triggers and classes, REST/Bulk APIs, Connected Apps, and Salesforce DX with scratch orgs and 2nd ...
Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when...
Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents.
Next.js App Router principles. Server Components, data fetching, routing patterns.
Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.
Choose and configure NLWeb retrieval backends — Qdrant (local + remote), Azure AI Search, Elasticsearch, OpenSearch (with/without k-NN), Postgres pgvector, Milvus, Snowflake Cortex Search, Cloudflare AutoRAG, Shopify MCP, and Bing Web Search. Covers `config_retrieval.yaml`, the single `write_endp...
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augm...
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