Caliper
Open-source reliability testing harness for AI agent skills, computing pass@k scores across Claude Code, Codex, and Pi backends.
Caliper
What is Caliper?
Caliper vs Similar AI Tools
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How to use Caliper?
- 1Install via pipx: pipx install caliper-eval
- 2Write a .eval.yaml spec with tasks, prompts, and expected outcomes
- 3Run the evaluation: caliper run my-skill.eval.yaml --k 3 --baseline
- 4Or use the agent skills: /evaluate-skill run my-skill.eval.yaml --k 3 --baseline
Caliper Key Features
- pass@k reliability scoring with configurable k
- Baseline comparison without the skill to prove delta
- LLM judge (expect:) and deterministic Python assertions (assert:)
- Multiple backend support: Claude Code, Codex, Pi, Claude API, OpenAI API
- Parallel task execution with configurable workers
- Agent skills (evaluate-skill, grill-skill) for in-workflow usage
- Saved results as JSON for historical tracking
- Interactive spec generation with grill-skill
Caliper Use Cases
- Validate that a skill actually improves agent performance over baseline
- Track reliability across model updates and prompt changes
- Compare which agent backend runs a skill more reliably
- Iterate on skill prompts with measurable improvement
Caliper Pricing & Free Credits
Caliper currently operates on a Free model.
This tool is completely free to use
Caliper Pros & Cons
Pros
- Repeatable, quantitative reliability scores
- Works with multiple agent backends out of the box
- Separates skill contribution from base agent via baseline
- Supports both LLM-based and deterministic judging
- Agent skills allow running evals inside Claude Code/Codex
Cons
- Requires CLI setup and agent-specific dependencies
- LLM judge may be inconsistent for subjective tasks
- No built-in test suite for non-agent workflows
- Limited documentation for custom assertion helpers
What is Caliper best for?
- Developers building and maintaining agent skills
- Teams needing to measure agent skill reliability across runs
Caliper FAQ
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