ab-test-agent-skills-and-mcp-changes-with-caliper
SolidUse Caliper to run real agent tasks with and without a skill, MCP server, or rule change so reliability and token cost are measurable.
Install
Quality Score: 84/100
Skill Content
Details
- Author
- agentskillexchange
- Repository
- agentskillexchange/skills
- Created
- 6 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
evaluate-skill
Measure a skill's reliability — run it k times for a pass@k score, design or interpret its eval, or compare it against the base agent. Use when the user wants to run, design, or interpret a skill's eval, or write an .eval.yaml spec.
agent-benchmark
Use when the user wants a professional, dynamic agent/skill/tool benchmark — compare harnesses, skills, MCPs, CLIs, or workflows on the same tasks with tokens, turns, latency, cost, and success metrics; prove whether a change helps; run ablation-style experiments; or build a reusable bench harness for a repo. Inspired by rigorous same-task evaluation (not GitHub stars).
monte-carlo-instrument-agent
Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability. Detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, and proposes tracing setup and decorator placements as diffs. Asks before editing any file.