Agent Evals and Observability is a Quality skill for Claude Code, published by magnus919 in agent-skills.
Design, run, review, or release framework- and vendor-neutral evaluations and observability for AI agents. Use when defining agent evals, datasets, graders, trajectory review, regression analysis, release gates, production traces, or privacy-aware telemetry.
Agent Evals and Observability is cataloged under Quality on DirSkills. Agent Evals and Observability comes from a repository tagged agent-skills, agentskills, ai-agents, automation and llm.
Documentation
Evaluation asks whether behavior meets a defined criterion on a declared dataset or production sample. Observability supplies traces, logs, metrics, correlations, and diagnostic context. Use both; neither proves what the other does.
This is the opening of the README. Read the full README on GitHub.
Agent Evals and Observability is one of 33 skills that DirSkills catalogs from magnus919/agent-skills, the repository it ships in. Its siblings there include ADR Authoring, API Design and Evolution and ARR CLI. Each one is a separate skill with its own page in this directory, installs the same way Agent Evals and Observability does, and is maintained by magnus919 in that same repository. The rest of the collection is listed on the magnus919/agent-skills page.
Agent Evals and Observability ranks #1843 by stars among the 1897 Quality skills in this catalog. The most-starred ones next to it are Benchmark, Benchmark Optimization Loop and API Design Patterns. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Agent Evals and Observability against them. Open each page to compare what they document and how they install.
Agent Evals and Observability is one of 33 skills cataloged on DirSkills from magnus919/agent-skills.
Agent Evals and Observability is one of 1897 Quality skills cataloged on DirSkills, ranked #1843 by stars among them.