memory-pioneer_skill

Overview

This skill benchmarks an agent's memory by measuring recall, precision, and hallucination rates, and optionally contributes anonymized scores to an open research dataset. It is designed for repeated evaluation so you can track improvements after tuning memory systems or retrieval strategies. The tool emphasizes user control and privacy: no conversation content or personal data is ever shared unless you explicitly opt in to submit aggregated scores.

How this skill works

The skill runs a suite of memory tests that store, retrieve, and query items to evaluate how accurately the agent remembers and retrieves information. It calculates metrics for recall, precision, and hallucination rate across scenarios and presents results you can review locally. If you opt in, only aggregated, anonymized numeric scores are submitted to a public research dataset; raw memory content and conversations are never uploaded.

When to use it

• When you need objective metrics to compare different memory implementations or configurations.
• After tuning or retraining a memory system to validate real gains.
• During development of agents to detect and reduce hallucinations tied to memory.
• For contributing anonymized, aggregated results to community research efforts.
• When auditing an agent before deployment to verify retrieval reliability.

Best practices

• Run benchmarks on representative workloads that match your agent's real use cases.
• Repeat tests after each configuration change to measure true improvement over time.
• Review raw local logs before submitting any anonymized scores to confirm they reflect intended scenarios.
• Combine this skill with a controlled memory implementation (e.g., agent-memory-ultimate) for closed-loop tuning.
• Use aggregated results to compare across versions, not to draw conclusions from single runs.

Example use cases

• Compare vector-store versus key-value memory backends for recall and precision differences.
• Measure hallucination rate before and after adding grounding prompts or stricter retrieval filters.
• Track memory performance across agent releases to detect regressions.
• Contribute benchmark scores from diverse agents to a public dataset for community research.
• Validate that a memory upgrade actually improves retrieval accuracy on your production prompts.