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session-memory

Extract reusable architectural decisions, patterns, data flows, and constraints from AI session context blocks.

Updated May 3, 2024
Search Tags:#design-patterns#session-memory#context-optimization#session-reset#architectural-knowledge#cross-session-reference#knowledge-deduplication
One-click install
npx skills add https://github.com/Kyonax/dot-files --skill session-memory-kyonax
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: session-memory
Source: https://github.com/Kyonax/dot-files/tree/main/.config/doom-mac/gptel-directives/skills/session-memory
Command: npx skills add https://github.com/Kyonax/dot-files --skill session-memory-kyonax

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI sessions are ephemeral, so reusable architectural decisions, design patterns, and constraints discovered during work are lost when sessions end, forcing teams to re-debate and re-discover the same solutions in every new session.

Core Features & Use Cases

  • Architectural Knowledge Extraction: Automatically identifies and extracts reusable decisions, patterns, data flows, and constraints from session context blocks, filtering out implementation noise like one-off fixes and debugging steps.
  • Cross-Session Referencing: Uses a deterministic reference syntax to link to specific knowledge entries without duplicating content across session files, cutting down on context bloat.
  • Session Reset Integration: Adds an optional extraction step to existing session reset workflows to capture knowledge while it is fresh, with backwards compatibility for existing session files.
  • Use Case: For example, if your team spent multiple sessions resolving a cross-app state transfer constraint and a custom breakpoint pattern, this skill saves those decisions so future sessions can reference them instantly instead of re-running the same research.

Quick Start

Use the session-memory skill to extract all reusable architectural knowledge from your latest session context block and save it to a persistent memory file for cross-session reference.

Frequently Asked Questions about session-memory

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

FAQPage Schema
How do I persist architectural decisions across AI coding sessions?▼

To persist architectural decisions across AI coding sessions, you extract reusable design patterns and constraints from ephemeral context blocks into a structured memory file. This prevents the loss of cross-session context and eliminates redundant re-discovery of resolved design choices.

What is the best way to save design patterns discovered during AI-assisted development?▼

The best way to save design patterns is using an extraction process that filters out implementation noise like debugging steps, capturing only reusable data flows and constraints. This ensures structured knowledge storage for future cross-session referencing.

How do I prevent context bloat when referencing past architectural knowledge?▼

You prevent context bloat when referencing past architectural knowledge by using a deterministic reference syntax to link to specific memory entries. This avoids duplicating content across session files while maintaining backwards-compatible integration with existing compaction systems.

Can I integrate architectural knowledge extraction into my existing session reset workflow?▼

You can integrate architectural knowledge extraction into an existing session reset workflow by adding an optional extraction step. This captures cross-session context while it is fresh, maintaining backwards compatibility with your current session files.

Why does my team keep re-debating the same design decisions in new AI sessions?▼

Teams keep re-debating the same design decisions in new AI sessions because ephemeral context blocks lose resolved constraints when sessions end. Extracting and storing this architectural knowledge in a persistent memory file eliminates this redundant research.

Does session memory extraction work with existing session compaction systems?▼

Session memory extraction works with existing session compaction systems through backwards-compatible integration. It applies content deduplication to ensure deterministic cross-session referencing without disrupting your current architectural knowledge storage processes.

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