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A Claude Code skill is a folder of instructions the agent loads only when a task calls for it. Six MIT-licensed skills for security, observability, cost, MCP, coding-agent governance and skill management, ranked on stated criteria, installed and linted in a clean repository.
Every major API management platform now ships some form of AI gateway. The criteria below separate the platforms built to publish and monetize APIs from the layer built to meter tokens, fail over between model providers and govern MCP tools, and compare twelve options against both.
OpenRouter's catalog holds 468 entries under 57 publisher prefixes, but the price next to each name is the price of one endpoint, not of the model. Nineteen leading models compared on published criteria, with picks by job and the routing settings that decide which host, precision and price tier serve a request.
An AI agent makes dozens of model and tool calls for one task, and a failure can sit in any of them. Ten observability tools, from gateways that see every call to SDK-based tracing platforms, compared on published criteria and the OpenTelemetry conventions they follow.
AI guardrails now come as gateways, cloud APIs, Python frameworks and small safety models. They differ less in what they detect than in where they sit and what traffic they can see. Nine tools, compared against published criteria, with the gaps each one leaves.
OpenAI's Codex and Anthropic's Claude Code now cover the same ground, from terminal to IDE to cloud sessions. They still differ on defaults that matter: who sandboxes what, how plans meter usage, what is open source and how an administrator locks a fleet down.
Frontier models now cluster around $2 per million input tokens and $10 per million output tokens, but batch discounts, cache multipliers, long-context tiers and off-peak clocks move the real bill by an order of magnitude. Every price below comes from the providers' own pricing pages as of October 15, 2026.
OpenRouter made one API key for hundreds of models the default way to prototype. Credit fees, a third party in the data path and governance that lives in someone else's account send production teams looking for alternatives. Seven options, ranked against published criteria.
Claude now runs on Amazon Bedrock, Google Cloud and Microsoft Foundry, and GPT models run on both OpenAI and Azure. That makes cross-cloud failover practical, but each cloud throttles, authenticates and names models differently. Five gateways, measured against seven published criteria.
Claude Code, Codex CLI and Cursor each reach a model provider in a different way. Five gateways that put all three behind one set of developer keys, budgets and logs, compared on how each client connects, what governance applies and where the gateway runs.
A multimodal LLM stack sends images, audio, video and files as well as text. Five gateways, measured endpoint by endpoint against the same published criteria, and which kind of team each one suits.
Most production teams now call at least two of the three big model providers. Five gateways that put OpenAI, Anthropic and Gemini behind one endpoint, measured on native SDK formats, cross-provider failover, spend control and where the proxy runs.
Most teams now run some models on their own GPUs and rent the rest. Five self-hostable gateways that route between vLLM, Ollama and hosted APIs, compared on how they reach local servers, how they decide where a request goes, and what happens when a local box falls over.
An on-premise LLM stack is only as private as the gateway in front of it. Five self-hosted AI gateways, read at the level of what each one fetches from the internet, how it installs without a connection, and how it routes to vLLM, Ollama and private cloud endpoints.
Agent frameworks have converged on the same feature list, but they still differ in how they model control flow, where state lives and how much they assume about your model provider. Ten code-first, open-source frameworks, compared against published criteria with GitHub activity checked on 5 October 2026.
An enterprise AI agent platform is where agents run, get an identity, reach tools and get audited. Ten managed options, from the three hyperscalers to the SaaS suites and an automation tool, compared on published criteria with documented prices.
An AI gateway now sits in front of most production model traffic. Ten options, from open-source binaries to cloud API platforms and managed routers, measured against ten published criteria before any ranking.
Claude Opus 5.5 leads the independent indexes this month, but no single model is best at everything. Ten models, from $50-per-million-token flagships to MIT-licensed open weights, measured against published criteria, with the best pick for reasoning, coding, cost, long context, multimodal and self-hosting.
Most of the MCP servers worth connecting in 2026 are now hosted by the vendors themselves, behind OAuth. The differences that matter are how much a server lets an agent change, how finely access can be narrowed, and how each one has handled its security incidents.
A coding agent is only as useful as the systems it can reach. Ten MCP servers for repositories, browsers, errors, databases, tickets, designs and infrastructure, compared on what they unlock, what they cost in context, and what they can break.
An open-source AI gateway is only as open as its license file and its paid tier allow. Ten self-hosted LLM gateways, read at the level of the LICENSE file, the dependency list and the release history, with criteria published before the ranking.
No single product secures the Model Context Protocol. Tool poisoning, rug pulls, shadow servers and token misuse each need a different control. Ten tools, compared against the risks the specification and OWASP name, with the gaps each one leaves.
The Model Context Protocol leaves authorization optional and tool safety to the implementer. An MCP gateway is where most teams end up enforcing both. Ten gateways, compared on the controls the specification asks for.
LiteLLM is the default first gateway for many teams. Its production footprint, the March 2026 PyPI compromise and its license split send some of them looking elsewhere. Six alternatives, measured against published criteria, with a migration path.
Every evaluation framework claims to measure quality. The differences show up in who can write the test, whether a trajectory can fail, and what happens to a bad score in production. Here are the criteria, the research that justifies them, and nine current tools read against them.