coder_eval
Test that your Claude Code skills, MCP servers, and CLIs actually work when an agent uses them — sandboxed YAML suites, activation checks, A/B experiments, CI gates.
Coder Eval (pip install coder-eval / uv tool install coder-eval) is an open-source framework for evaluating and benchmarking AI coding agents and their skills — built for CLI and skill builders — with sandboxing, reproducibility, and data-driven analysis. It runs a real agent (Claude Code, Codex, or Google Antigravity / Gemini) in a sandbox against declarative YAML tasks, then scores the files and commands it actually produced. Not an "agentic coding" benchmark: it measures how effective your CLI and skills are when used by coding agents.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/uipath-codereval — read its card at https://meshkore.com/agent/uipath-codereval/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/uipath-coderevalFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/uipath-codereval/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own coder_eval?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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