Run structured experiments to test and refine AI agent prompts.
npx skills add https://github.com/lidessen/moniro --skill prompt-lab
Please help me install this Agent Skill. Skill: prompt-lab Source: https://github.com/lidessen/moniro/tree/main/skills/prompt-lab Command: npx skills add https://github.com/lidessen/moniro --skill prompt-lab
Prompt Lab helps teams systematically test and improve agent instructions by running structured experiments, collecting evidence, and turning findings into reusable patterns to guide prompt design.
Start by selecting a Skill unit (e.g., this one) and run a simple decay or compliance test using the provided templates. Design your task, embed the instruction, and record results with the standard YAML templates. Review the consolidated findings to iterate on prompt design.
High-intent search queries and answers about installing and using this skill.
To test AI agent prompts systematically, you can run structured experiments like compliance, decay, and adversarial tests to evaluate prompt robustness. This process uses an evidence-based analysis framework to quantify results and drive iterative improvement.
Prompt decay testing is an experiment type that evaluates how prompt robustness degrades over extended or repeated interactions. You need decay testing when validating long-term agent performance and ensuring consistent instruction adherence across real tasks.
Running adversarial tests on agent prompts involves embedding malicious or edge-case instructions into structured tasks to evaluate robustness. You record results using standardized YAML templates to diagnose vulnerabilities and prescribe improvements through evidence-based analysis.
The best way to document prompt engineering experiments is recording results with standardized YAML templates. This captures findings as case studies and reusable patterns, enabling knowledge reuse and rapid iteration across teams for future prompt design.
Yes, you can use evidence-based analysis to improve prompt compliance by following a structured framework of Observation, Measurement, Diagnosis, Prescription, and Verification. This quantifies compliance test results to drive targeted improvements in agent instructions.
Yes, prompt-lab works without external dependencies or components. It operates as a standalone skill unit, providing internal templates and structured methodologies to execute prompt experiments and record evidence-based findings independently.
hhopkins95Configure agent profiles with prompts, tools, skills, and subagents.
AI4ScientistDiagnose and iterate on experiments with a five-step debugging workflow and structured logs.
samjhechtExtract decisions, constraints, and action items from research documents.
sori883Evaluate and iteratively refine agent prompts using executor feedback and system metrics.
