Add Mutagent: eval-driven prompt optimization#25
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Adds Mutagent to the Evals & Verification section.
What it is: Open-source (MIT) Claude Code plugin + bare Agent Skills bundle that gives AI engineers an eval-driven loop for prompt and agent optimization: capture traces → curate datasets → score against rubrics → optimize prompts against measurable targets.
Why it fits: Existing Evals entries (DeepEval, Inspect AI, tau-bench, SWE-bench) cover scoring infrastructure and benchmarks; Mutagent is orthogonal — it closes the loop from existing-framework traces (Mastra/LangChain/LangGraph/Vercel AI SDK) to dataset curation, rubric scoring, and prompt optimization against measurable targets. Vendor-agnostic by principle: the pattern (trace → dataset → rubric → optimize) generalizes regardless of which adapter is wired.
Credibility: 9.6k npm downloads/30d on
@mutagent/cli. Framework adapters for Mastra, LangChain, LangGraph, Vercel AI SDK. Runtime-agnostic (works in Claude Code, Cursor, Aider, Continue).Source: https://github.com/mutagent-io/skills
Docs: https://docs.mutagent.io
Install:
npm install -g @mutagent/cli && mutagent skills installHappy to revise placement/wording if it's a better fit elsewhere.