Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies…
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG…
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
A method for operating an AI product after launch or during preparation for launch. It covers monitoring, metrics, failure handling, human corrections, feedback, and business or customer feedback loops.
Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy.
Use when adding pause/resume, human approval, provider routing, tool scoping, or knowledge search to an existing agent — covers all optional Agent2 framework capabilities with implementation patterns.
Use when a domain expert wants to clone their professional brain into an Agent2 agent — interactive multi-phase interview that extracts identity, Chain-of-Thought, tools, knowledge, examples, and output format, then generates a complete production agent with a Sachbearbeiter-style Chain-of-Thought prompt, Pydantic…
Use when building an agent that needs deep domain expertise — reads documents, checks them against professional knowledge, asks clarifying questions, and produces structured domain-specific output. Covers knowledge-backed agents, document processing, multi-turn workflows with human approval, and per-context knowledge…
Design where, when, and how a human gates, reviews, or rescues an LLM agent — without turning the agent into a button labelled "approve". Use when the user is building an agent that takes irreversible actions or operates in regulated workflows and mentions human-in-the-loop, HITL, approval gate, escalation, review…
Design and validate LLM-as-judge scoring — pairwise vs pointwise, bias correction, anchor calibration, and the cases where a judge is the wrong tool. Use when the user is building an eval, scoring open-ended outputs, or comparing model versions and mentions LLM-as-judge, model grader, pairwise comparison, position…
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy.
Plan a production day end to end — order of day, categorised shot list, equipment checklist and a WhatsApp-ready crew briefing so nobody shows up asking where the call sheet is.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance…
Use this skill to verify that an AI system is ready for production before deployment. Covers model abstraction, cost/latency, retries, fallbacks, structured logging, tracing, observability, evaluation in CI/CD, monitoring, and rollback planning. Activates as a mandatory gate before production deployment of any AI/LLM…
Use this skill when a user asks to produce audit reports or compliance evidence from Octopus Deploy event data through the Octopus MCP server. It covers questions about who deployed, who edited which variables, when a release shipped, why a permission changed, and what activity happened in a Space over a given window.…
Comprehensive knowledge about Octopus Deploy, including core concepts, deployment processes, variables, releases, lifecycles, tenants, runbooks, AI-powered features, and best practices. Trigger when user mentions Octopus Deploy or general software deployment concepts.
Guides a user through their first Octopus Deploy setup — connecting a code repository, registering a deployment target (Kubernetes, Azure App Service, AWS ECS, Lambda, on-prem Tentacle, etc.), wiring up packages from their existing CI, and reaching their first real deployment. Prefers the Octopus MCP server's tools…
Typography system compiler that generates structured type scales, font stacks, and typographic tokens. Use when users request type systems, font scales, typography tokens, design system typography, or ask to create/export typographic styles. Pairs with hexed for complete design systems.