Agent Evaluation Framework
$2.99OfficialDesign and run comprehensive eval suites for AI agents: task completion rates, tool call accuracy, cost tracking, and regression testing.
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 5 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when the user needs to measure whether an AI agent (or multi-agent system) actually works: "how good is my agent?", "set up evals," "did my prompt change make things better or worse," "I need a CI gate for agent quality." Trigger phrases: agent evals, eval framework, agent benchmark, regression testing, completion rate, agent metrics.
Do NOT use it for evaluating a single LLM call or a classification model (use standard ML evals), or for ad-hoc manual testing. This is about systematic, repeatable evaluation of goal-directed agents that call tools and take multiple steps.
Inputs to gather
- The agent and its task domain: what does the agent do? Get a
…
🔒 Buy once ($2.99) to unlock the full playbook, download it, and install it in every tool you use.