evolve

Replay session transcripts through the memesis pipeline to evaluate memory fidelity.

Updated Apr 10, 2026

Search Tags:#pytest #validation #memory #diagnostics #session replay

Ready to run

npx skills add https://github.com/emmahyde/memesis --skill evolve-emmahyde

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evolve
Source: https://github.com/emmahyde/memesis/tree/main/skills/evolve
Command: npx skills add https://github.com/emmahyde/memesis --skill evolve-emmahyde

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill enables users to verify the integrity and effectiveness of the memesis pipeline by replaying session transcripts and assessing memory retention.

Core Features & Use Cases

Replay Session Transcripts: Reconstruct past interactions to test memory pipeline accuracy.
Memory Evaluation: Compile expected-memory descriptions into pytest evaluations to identify lost or corrupted memories.
Use Case: Use this to validate that critical memories are retained correctly after pipeline updates, ensuring consistency for AI assistants.

Quick Start

Use the evolve skill to replay a session transcript located at '/path/to/transcript.jsonl' and see detailed assessment reports.

Frequently Asked Questions about evolve

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I validate memory accuracy in an AI pipeline after updating session transcripts?▼

To validate memory accuracy, you can replay session transcripts through the pipeline and compile expected-memory descriptions into pytest evaluations. This process diagnoses signal loss and verifies that critical memories are retained correctly after updates.

What is session replay used for in memory fidelity testing?▼

Session replay is used to reconstruct past interactions and test memory pipeline accuracy. By recreating session transcripts, developers can identify lost or corrupted memories and evaluate the overall robustness of memory management in AI systems.

Do I need pytest to evaluate memory retention and diagnose signal loss?▼

Yes, pytest is required to evaluate memory retention and diagnose signal loss. The Skill compiles expected-memory descriptions into pytest evaluations to identify discrepancies and verify pipeline robustness during session transcript replay.

Can I use Python scripts to test memory pipeline consistency for AI assistants?▼

Yes, you can use Python scripts to test memory pipeline consistency for AI assistants. The process involves running the 'scripts/evolve.py' module to replay transcripts and generate detailed assessment reports on memory accuracy.

What is the best way to diagnose signal loss when replaying AI session transcripts?▼

The best way to diagnose signal loss is by replaying session transcripts and compiling expected-memory descriptions into pytest evaluations. This approach pinpoints lost or corrupted memories and verifies the robustness of the memory pipeline.

Why does my AI assistant lose critical memories after a pipeline update?▼

Critical memories may be lost after a pipeline update due to signal loss or reduced pipeline robustness. Replaying session transcripts and running pytest evaluations can diagnose where memory corruption occurs and verify retention consistency.