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A SKILL.md file changes what every coding agent on a team does the next time it loads. How to write descriptions that trigger, keep skills small, version and review them, pick a distribution model from git to plugin marketplaces to a private registry, and roll a bad release back.
A Codex skill is a folder with a SKILL.md file that the CLI and IDE extension find in .agents/skills and load when a task matches its description. Scan paths, the context budget, writing one that triggers, installing shared skills with npx skills, and six worth adding for LLM app teams.
The skills CLI installs SKILL.md folders from GitHub into 79 coding agents, and skills.sh is the directory built on its telemetry. Every command in version 1.7.4, project and global scope, the symlink layout, the lock file and its hashes, and the warning printed on every install, checked against the source.
Claude Code talks to one API format, and anything that speaks that format can answer it. Routers, gateways and local servers use that to swap in other models. The configuration takes minutes; knowing what breaks on the way takes longer.
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.
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.
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.
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.
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.